Computer implemented method and system for applying a machine learning model to identify one or more RF signals and related computer program product

Abstract

A modular radio frequency ("RF") system includes one or more directional antennas and is configured with both hardware and software components to enable the RF system to monitor objects (e.g., detect or track signals or objects) and / or interact with objects (e.g., track signals or objects, or transmit signals) in a specific direction. The RF system includes one or more machine learning models to determine, based on received signals, which are used to transmit one or more signals.

Description

Technical Field

[0001] Embodiments of the present invention relate to modular, directional transceiver systems and methods for detecting, tracking, and / or transmitting data to an identified object. Further embodiments of the present invention relate to devices, systems, and methods for locating or identifying an object in three-dimensional space, tracking the movement or position of the object, determining properties associated with one or more signals emitted from or near the object, and generating one or more signals and transmitting those signals in the direction of the object. Prior Technology

[0002] The methods described in this paragraph are methods that can be pursued, but are not necessarily methods that have been previously conceived or pursued. Therefore, unless otherwise indicated, none of the methods described in this paragraph should be assumed to be prior art simply because they are included in this paragraph.

[0003] Radio frequency (“RF”) systems can provide monitoring and / or transmission capabilities for specific radio frequencies. Such RF systems typically include a holographic antenna and can be configured to transmit or receive specific radio frequencies. Summary of the Invention

[0004] The systems, methods, and devices described herein each have several forms, and none of them alone is considered as a desired attribute. Without limiting the scope of the invention, several non-limiting features will now be briefly described.

[0005] To monitor a surrounding area, multiple specialized devices can be installed to identify objects, track them, and transmit signals toward them. Conventional devices and their associated software components (if any) are typically manufactured and / or programmed for a single function or purpose and may be limited to these pre-configured functions. Such conventional systems may not be easily adaptable, and in some cases, may be completely unadaptable. For example, if a system can monitor within a specific RF range, it may not be easily updated to monitor additional RF ranges without significant cost or effort (e.g., new hardware, software rewrites, and / or the like). Such systems may also be difficult to move or install in new locations or orientations without testing, calibration, new hardware, and the like. Furthermore, such systems may use holographic antennas that radiate or receive from many directions simultaneously, eliminating the possibility of target signal transmission in a specific direction and potentially requiring high power.

[0006] The systems, methods, and devices of the present invention (generally referred to herein as "RF systems") overcome one or more of these disadvantages and may include modular, adaptable, and portable systems that can be updated and / or reprogrammed to perform one or more different purposes and functions.

[0007] The hardware components of the radio frequency (“RF”) system may include one or more directional broadband antennas, one or more module housings, one or more processing modules, and one or more RF modules, as well as other hardware components described in more detail herein. One or more directional antennas may be physically located and configured to transmit or receive at different power levels and frequencies in one or more specific directions, such that the antennas collectively provide greater directivity and sensitivity in (a few) specific directions than in other directions. The hardware components of the system may also include a direction finder, also referred to herein as a radio direction finder or direction-finding antenna. The direction finder uses the reception of radio waves to determine the orientation of an object. In various embodiments, a transmission source can be located (e.g., via triangulation or other similar means) by combining directional information from multiple sources (e.g., other direction finders, other systems, or one or more directional broadband antennas and / or the like) from multiple sources. In various embodiments, each directional antenna and its associated electronic circuitry may operate independently and in coordination with other directional antennas. The antenna can also be configured to include automated or manual adjustability relative to a vertical angle, allowing the antenna to be lowered towards the ground or adjusted to be higher towards the sky.

[0008] RF systems can be advantageously modularized to achieve multiple configurations for various applications. The modularity of an RF system can be found in both the modularity of a single RF system that can operate independently (including coordination with one or more additional systems or sensors) and the modularity of multiple RF systems that can coordinate with each other (including coordination with one or more additional systems or sensors). For example, an RF system can be implemented using one module housing, two module housings, or more module housings. In instances of two or more module housings, the module housings of the RF system can be joined together by one or more connecting housings. Thus, in one embodiment, the RF system may include two stacked module housings joined together by a connecting housing. In various embodiments, the RF system may further include components for mounting the RF system, such as one or more mounting brackets, clamps, slides, pins, and / or the like. Advantageously, due to its modularity, the RF system can be appropriately configured for a given application and mounted on a tripod, a vehicle, a building, and / or the like.

[0009] Each module housing may accommodate one or more processing modules, one or more RF modules, and one or more power supply modules, as described herein. In various embodiments, a single module housing may be connected to two directional broadband antennas, wherein the antennas may be placed at a single location, or the antennas may be placed at a distance from each other (e.g., 5, 10, or 100 feet) and connected to the same module housing. In various embodiments, the RF modules may include power amplifier technology and positioning, navigation, and timing ("PNT") capabilities (in some embodiments, these may be provided in a direction finder).

[0010] In various embodiments, the processing module may include a machine learning component that can be used to assist an RF system in detecting and / or identifying one or more RF signals captured by a connected antenna. For example, the machine learning component may implement machine learning (“ML”) algorithms, artificial intelligence (“AI”) algorithms, and / or any other type of algorithm (generally referred to herein as “AI / ML algorithms,” “AI / ML models,” or simply “ML algorithms,” “ML models,” and / or the like) that can be implemented by one or more processors. Enabling an ML model to identify RF signals can advantageously provide a significant improvement over a known system because many detected signals may contain interference at the same level, which is relatively weak and difficult to detect, or otherwise difficult to identify due to other factors. In various embodiments, the machine learning component may use one or more machine learning algorithms to implement one or more models or parameter functions for detection / identification. Machine learning components can be configured to apply a model that helps detect which types of RF signals (e.g., a series of RF signals, a specific frequency or combination of frequencies, and / or similar) indicate which types of objects. Therefore, the model can be applied by the RF system to received or captured RF signals for identification purposes. For example, in various embodiments, the machine learning model of the RF system can be trained by: (1) sampling the raw signal, (2) applying the trained model, and / or (3) outputting categories and probabilities (e.g., associated with object type). Then, for example, a processing module can identify an object type based on the output of (3) the categories and probabilities. Furthermore, in various embodiments, the application of the trained machine learning model may include (0) preliminary steps of filtering the baseline signal and / or the friendly signal.

[0011] In various embodiments, the RF system (e.g., via one or more processing modules and / or one or more RF modules) can use objects of an identified type (e.g., from the output of an applied machine learning model) to generate one or more new signals and transmit the new signals using one or more directional antennas. The new signals can be transmitted in the direction of the identified signals or one or more objects. Generating a signal based on the identified signals can be advantageous due to increased power efficiency / optimization. For example, instead of transmitting signals across the entire frequency band, signals can be transmitted only at a specific frequency or within a narrow frequency range, thereby increasing power efficiency and / or signal power to reach greater distances.

[0012] In various embodiments, there may be other sensors or systems that can be connected to the RF system to provide additional data (e.g., and include other RF systems in the area), which may be used to: (1) further train the machine learning model; (2) assist the RF system in continuing to track, or to begin tracking an object or signal; and / or (3) generate and transmit, or continue to generate and transmit, a specific signal in the direction of an object, and other functions.

[0013] In various embodiments, the RF system may include many other advantageous features, characteristics, functionalities, and / or morphologies, including, for example, a configurable antenna mount that can be installed without tools and allows for adjustment of the angle of a directional antenna; a physical modular configuration and materials that efficiently dissipate heat from the system's components to enable the RF system to operate in high-temperature and / or extreme environments; a physical modular configuration that provides physical protection for components used in, for example, dirty or extreme environments; and / or electromagnetic interference ("EMI") shielding for the components of the RF system; and others described herein.

[0014] Furthermore, according to various embodiments, a variety of interactive graphical user interfaces can be provided, thereby allowing various types of users to interact with the systems and methods described herein to, for example, generate, view and / or modify data captured or used by one or more RF systems or connected systems.

[0015] The interactive and dynamic user interface described herein is achieved through innovative and efficient interaction between the user interface and the underlying system and components. For example, this paper discloses an improved method for receiving user input, translating and delivering such input to various system components, automatically and dynamically executing complex procedures in response to input delivery, automatically interacting among various components and procedures of the system, and automatically and dynamically updating the user interface. Therefore, compared to previous systems, the interaction and presentation of data through the interactive user interface described herein offers cognitive and ergonomic efficiency and advantages.

[0016] Therefore, in various embodiments, large amounts of data can be automatically and dynamically collected and analyzed in response to user input and configuration, and the analyzed data can be efficiently presented to the user. Thus, in some embodiments, the systems, devices, configuration capabilities, graphical user interfaces, and similar systems described herein are more efficient than prior art systems and / or the like.

[0017] The various embodiments of this invention provide improvements to various technologies and fields, as well as practical applications of various technical features and advancements. For example, as described above, some existing systems are limited in various ways, and the various embodiments of this invention provide significant improvements over such systems and practical applications of such improvements. Furthermore, the various embodiments of this invention are inseparable from computer technology and provide practical applications of computer technology. Specifically, the various embodiments rely on dedicated hardware and software components installed in specific locations to improve energy and processing efficiency. These features and others are inseparable from and implemented by computer technology, artificial intelligence, and digital signaling technology, and would not exist outside of computer technology, artificial intelligence, and digital signaling technology. For example, the RF system, processing module, RF module, and signal detection, generation, and transmission functionality and interaction with detected objects / signals described herein with reference to the various embodiments cannot be reasonably performed independently by humans without the computer and technology implementing them. Moreover, the various embodiments of this invention achieve many of the advantages described herein through the implementation of computer technology, including more efficient interaction and analysis with various types of electronic data and the like.

[0018] The various combinations of features, embodiments, and states described above and below are also disclosed and carefully considered by the present invention.

[0019] The following description of additional embodiments of the invention is based on the appended patent claims, which may serve as an additional overview of the invention.

[0020] In various embodiments, a system and a computer system are disclosed, including: a computer-readable storage medium having program instructions embodied therein; and one or more processors configured to execute the program instructions to cause the system and / or computer system to perform operations including one or more of the embodiments described above and / or below (including one or more of the embodiments covered by the appended claims).

[0021] In various embodiments, a computer implementation method is disclosed, wherein one or more processors executing program instructions implement and / or perform one or more of the embodiments described above and / or below (including one or more of the embodiments claimed in the appended invention claims).

[0022] In various embodiments, a computer program product including a computer-readable storage medium is disclosed, wherein the computer-readable storage medium has program instructions embodied therein, which are executable by one or more processors to cause the one or more processors to perform operations including one or more of the embodiments described above and / or below (including one or more of the embodiments claimed in the appended invention claims). Simple Explanation of the Diagram

[0023] The following figures and associated descriptions are provided to illustrate embodiments of the invention and do not limit the scope of the invention claims. These features and numerous associated advantages will become more readily apparent when the invention is described in detail below in conjunction with the accompanying drawings.

[0024] Figure 1A illustrates a block diagram of an exemplary operating environment in which one or more embodiments of the present invention may be operated;

[0025] Figure 1B illustrates a block diagram of an exemplary hardware component of an RF system according to one embodiment of the present invention;

[0026] Figure 1C illustrates a block diagram of an exemplary software component of an RF system according to various embodiments of the present invention;

[0027] Figure 1D illustrates a perspective view of an exemplary embodiment of an RF system comprising two module housings according to various embodiments of the present invention;

[0028] Figure 2A illustrates an exemplary embodiment and orientation of one of a plurality of RF systems according to various embodiments of the present invention;

[0029] Figure 2B illustrates an exemplary embodiment and orientation of an RF system for interacting with an object according to various embodiments of the present invention;

[0030] Figure 3 shows a block diagram illustrating one of the exemplary computer system components in various forms by which the present invention can be implemented;

[0031] Figure 4A illustrates a perspective view of an exemplary embodiment of an RF system including a module housing according to various embodiments of the present invention;

[0032] Figure 4B illustrates a perspective view of a cross-section along a vertical plane of an exemplary embodiment of the RF system in Figure 4A;

[0033] Figure 4C shows a side view of one of the exemplary implementation schemes of the RF system in Figure 4A;

[0034] Figure 4D illustrates a top view of a cross-section along a horizontal plane of an exemplary embodiment of the RF system in Figure 4C;

[0035] Figure 5A illustrates a perspective view of an exemplary embodiment of an RF system comprising two module housings according to various embodiments of the present invention;

[0036] Figure 5B illustrates a perspective view of a cross-section of a vertical plane along an exemplary embodiment of the RF system of Figure 5A;

[0037] Figure 5C shows a side view of one of the exemplary implementation schemes of the RF system in Figure 5A;

[0038] Figure 5D illustrates a top view of a cross-section along a horizontal plane of an exemplary embodiment of the RF system in Figure 5C;

[0039] Figure 5E illustrates a perspective view of an exemplary embodiment of an antenna mount for an RF system according to various embodiments of the present invention;

[0040] Figure 6 illustrates a block diagram of an exemplary hardware component of an RF system according to one embodiment of the present invention;

[0041] Figures 7A to 7B and Figure 8 illustrate block diagrams of exemplary modules of an RF system according to various embodiments of the present invention;

[0042] Figures 9A to 9D are flowcharts illustrating exemplary processes or methods for assembling, operating, and / or functioning an RF system according to various embodiments of the present invention;

[0043] Figure 10 illustrates a block diagram of the exemplary functionality of an RF system according to various embodiments of the present invention;

[0044] Figure 11 illustrates an exemplary process for training an RF artificial intelligence and machine learning model according to various embodiments of the present invention;

[0045] Figure 12 illustrates an exemplary process for applying a trained RF artificial intelligence or machine learning model according to various embodiments of the present invention;

[0046] Figure 13 illustrates an exemplary process for transmitting and tracking an object using an RF system according to various embodiments of the present invention;

[0047] Figure 14 illustrates an exemplary flow for coordinating the application of an RF artificial intelligence and machine learning model among multiple connected systems and devices according to various embodiments of the present invention; and

[0048] Figure 15 illustrates an exemplary process for coordinating one of multiple interconnected systems and devices according to various embodiments of the present invention. Implementation

[0049] Cross-reference to related applications This application is a continuation of any of the following: U.S. Patent Application No. 18 / 051743, filed November 1, 2022; U.S. Patent Application No. 18 / 051801, filed November 1, 2022; U.S. Patent Application No. 17 / 978736, filed November 1, 2022; U.S. Patent Application No. 17 / 978822, filed November 1, 2022; U.S. Patent Application No. 17 / 978701, filed November 1, 2022; U.S. Patent Application No. 17 / 978807, filed November 1, 2022; U.S. Patent Application No. 17 / 978868, filed November 1, 2022; and U.S. Patent Application No. 17 / 978821, filed November 1, 2022. The applications listed above claim the rights to: U.S. Provisional Patent Application No. 63 / 365115, filed May 20, 2022; and U.S. Provisional Patent Application No. 63 / 420247, filed October 28, 2022. The complete disclosure of each of the items listed above is hereby incorporated, as if its entirety were described herein, and is incorporated by reference for all purposes.

[0050] Any and all of the foreign or domestic priority claims identified in the filing forms submitted with this application are hereby incorporated, for all purposes, by reference to all of their contents, in accordance with 37 CFR 1.57.

[0051] Although specific preferred embodiments and examples are disclosed below, the subject matter of the invention extends beyond the specifically disclosed embodiments to other alternative embodiments and / or uses, as well as modifications and equivalents thereof. Therefore, the scope of the appended claims is not limited to any of the specific embodiments described below. For example, in any method or process disclosed herein, the actions or operations of the method or process can be performed in any suitable sequence and are not necessarily limited to any particular disclosed sequence. Various operations may be described sequentially as a plurality of discrete operations in a manner that may aid in understanding a particular embodiment; however, the order of description should not be construed as implying that such operations are sequentially dependent. Furthermore, the structures, systems, and / or devices described herein may be embodied as integrated components or as separate components. For the purpose of comparing the various embodiments, specific features and advantages of such embodiments are described. Any particular embodiment does not necessarily achieve all of these features or advantages. Therefore, for example, embodiments may be implemented in a manner that achieves or optimizes one advantage or group of advantages as taught herein without necessarily achieving other features or advantages as may also be taught or implied herein. I. [, Overview , ] [ ]

[0052] As mentioned above, to monitor a surrounding area, multiple specialized devices can be installed to identify objects, track them, and transmit signals toward them. Conventional devices and their associated software components (if any) are typically manufactured and / or programmed for a single function or purpose and may be limited to these pre-configured functions. Such conventional systems may not be easily adaptable, and in some cases, may be completely unadaptable. For example, if a system can monitor within a specific RF range, it may not be easily updated to monitor additional RF ranges without significant cost or effort (e.g., new hardware, software rewrites, and / or the like). Such systems may also be difficult to move or install in new locations or orientations without testing, calibration, new hardware, and the like. Furthermore, such systems may use holographic antennas that radiate or receive from many directions simultaneously, which eliminates the possibility of target signal transmission in a specific direction and may have high power requirements.

[0053] As mentioned above, the systems, methods, and devices of the present invention (generally referred to herein as "RF systems") overcome one or more of these disadvantages and may include a modular, adaptable, and portable system that can be updated and / or reprogrammed to perform one or more different purposes and functions. RF systems may advantageously include the ability to be updated over time for other purposes not currently considered at the time of manufacture or installation. The systems, methods, and devices described herein relate to hardware and software components of one or more modular, adaptable, and portable systems that can be reprogrammed to perform one or more purposes and functions at once or over time.

[0054] The hardware components of the radio frequency (“RF”) system may include one or more directional broadband antennas, one or more module housings, one or more processing modules, and one or more RF modules, as well as other hardware components described in more detail below. Regarding directivity, one or more directional antennas may be physically positioned and configured to transmit or receive at different power levels and frequencies in one or more specific directions, such that the antennas collectively provide greater directivity and sensitivity in (a few) specific directions than in other directions. When greater concentration of radiation in a specific direction is desired, directional antennas can provide increased performance compared to dipole antennas or holographic antennas. Furthermore, such directional broadband antennas can be used to transmit, receive, or transmit and receive radio signals over a wide spectrum. In various embodiments, the antennas may be configured to transmit and / or receive radio signals in a subset of a wide spectrum. For example, there may be nearby devices where one of the antennas is oriented towards a specific frequency range, and the system may be programmed (e.g., using software) to filter the received signal so as not to interfere with the analysis of the received signal and / or (e.g., using software or additional digital signal filtering equipment) to filter the transmitted signal to minimize or remove interference to the operation of nearby devices. Therefore, an RF system can selectively transmit signals with different electrical powers via antennas in various directions.

[0055] The hardware components of the system may also include a direction finder, also referred to herein as a radio direction finder or direction-finding antenna. The direction finder uses the reception of radio waves to determine the orientation of an object. In various embodiments, a transmission source can be located (e.g., via triangulation or other similar means) by combining directional information from multiple sources (e.g., other direction finders in the area, other systems, or one or more directional broadband antennas and / or the like). The direction finder can be used to detect any wireless power source. The direction finder can communicate with one or more (or all) of the processing modules of the RF system in any given configuration.

[0056] In various embodiments, each directional antenna and its associated electronic circuitry can operate independently and in coordination with other directional antennas. For example, a single directional antenna can be configured to face and monitor a 90° field of view, and four of the directional antennas can be configured to monitor a full 360° (or approximately 360°) field of view. In various embodiments, additional antennas may be used (e.g., five antennas each covering 72°, six antennas each covering 60°, seven antennas each covering 52°, and / or the like), fewer antennas may be used (e.g., one antenna each covering 360°, two antennas each covering 180°, three antennas each covering 120°), and / or some fields of view may overlap (e.g., four antennas each covering 120°, or the like). Antennas can also be configured to include automated or manual adjustability relative to a vertical angle, allowing the antenna to face downwards towards the ground or be adjusted upwards towards the sky. In some applications, the antenna can be adjusted to one of the optimal angles based on empirical data or artificial intelligence / machine learning.

[0057] In various embodiments, the directional broadband antenna and its associated electronic circuitry may include multiple physical configurations. For example, the antenna and associated electronic circuitry may be configured to be detachable and / or stackable, allowing multiple antennas to be used in a given location. For example, there may be two antennas in one location, each configured to monitor a 90° field of view for a total field of view of 180° monitored by the two antennas.

[0058] RF systems can be advantageously modularized to achieve multiple configurations for various applications. The modularity of an RF system can be found in both the modularity of a single RF system that can operate independently (including coordination with one or more additional systems or sensors) and the modularity of multiple RF systems that can coordinate with each other (including coordination with one or more additional systems or sensors). For example, an RF system can be implemented using one module housing, two module housings, or more module housings. In instances of two or more module housings, the module housings of the RF system can be joined together by one or more connecting housings. Thus, in one embodiment, the RF system may include two stacked module housings joined together by a connecting housing. In various embodiments, the RF system may also include an upper housing and a lower housing, and may further include components for mounting the RF system, such as one or more mounting brackets, clamps, slides, pins, and / or the like. Advantageously, due to its modularity, the RF system can be appropriately configured for a given application and mounted on a tripod, a vehicle, a building, and / or the like.

[0059] Each module housing may accommodate one or more processing modules, one or more RF modules, and one or more power supply modules, as described herein. In various embodiments, the processing module may include a system-on-module ("SOM") configuration and may therefore be referred to herein as an "SOM module". Each of the module housing and each of the associated processing module(s), RF module(s), and power supply module(s) may support one or more directional antennas and / or a direction finder, as described herein.

[0060] In one embodiment, each module housing includes a single processing module / SOM module, two RF modules, and a power supply module. In this embodiment, each RF module supports a single directional antenna (and therefore the module housing supports up to two directional antennas), the processing module / SOM module supports two RF modules, and the power supply module provides power to the processing module / SOM module and the two RF modules. Therefore, in one configuration where the RF system includes one module housing, the RF system can support up to two directional antennas, and in one configuration where the RF system includes two module housings, the RF system can support up to four directional antennas. Additionally, in any of these configurations, the RF system can further support one or more direction finders via one or more components of the module housing (e.g., the processing module / SOM module, (a number of) RF modules, and / or the power supply module).

[0061] As mentioned above, each module housing of the RF system may include a processing module and an RF module, which include electronic circuitry configurable to connect to and operate one, two, three, four or more individual directional broadband antennas. For example, each processing module may include one or more motherboards, one or more processors, one or more graphics processing units (“GPUs”), one or more software-defined radios (“SDRs”) and / or the like, and may be configurable to control and operate one or more directional antennas. In various embodiments, a single module housing may be connected to two directional broadband antennas, wherein the antennas may be placed at a single location, or the antennas may be placed at a distance from each other (e.g., 5, 10, or 100 feet) and connected to the same module housing.

[0062] In various embodiments, an RF system may be manufactured or assembled with a modular housing (and other hardware components as described herein) and may be configured to have one or more antennas with a compact, portable, and / or adaptable design. Additionally, in various embodiments, the RF system may be manufactured with a compact and / or lightweight design, allowing the RF system to be placed in various locations and positions. For example, in one embodiment, the RF system may have an overall height (e.g., length) between about 20 cm and about 250 cm, and may have an overall weight between about 10 kg and about 100 kg. Furthermore, depending on the application (e.g., reduced or increased size, different shapes, and the like), directional broadband antennas may be disconnected from the RF system's modular housing and exchanged with different types of antennas that may provide different functionalities (e.g., wider or narrower field of view (such as omnidirectional), longer range sensitivity, shorter range sensitivity, and the like) and / or different physical properties to improve mobility or adaptability. For example, if an RF system is to be moved from the roof of a building to a vehicle, it may be necessary to use one or more different antennas configured to securely attach to the vehicle while it is in operation and to simultaneously meet new requirements associated with the placement. These requirements may include the ability to monitor a field of view wider than a previous location placed on the side of a building, and the wider field of view can be achieved using additional antennas configured differently and / or different antennas.

[0063] RF systems can also advantageously incorporate a modular configuration and materials for efficient heat dissipation from the system's components, enabling the RF system to operate in high-temperature and / or extreme environments. For example, upper and lower housings may include fans, and the upper and lower housings, (a number of) module housings, and connecting housings (if applicable) may together provide a cavity or channel for airflow through the RF system to cool its various components. Module housings may, for example, contain heat sinks within the cavity or channel, and are thermally coupled to processing modules, RF modules, and power supply modules that can flow above them when propelled or drawn by the fan to cool the components of the RF system. The fan may cause air to flow upward from the lower housing through the heat sinks of one or more module housings and exit through the upper housing.

[0064] RF systems can also advantageously include a modular configuration that provides physical protection for components used in, for example, dirty or extreme environments. For example, each module housing may include cavities in which processing modules, RF modules, and power supply modules can be housed. These cavities may be sealed from the external environment or hermetically sealed. The module housings, as well as the upper, lower, and combined housings, may also include additional cavities for wiring and connections among the various components. These additional cavities may also be sealed from the external environment or hermetically sealed. These cavities can also advantageously provide shielding against electromagnetic interference (“EMI”) for the various components of the RF system. For example, EMI shielding can be provided by constructing cavities of metal and / or other EM shielding materials or components. Additionally, the upper and lower housings may include vents, grilles, filters, or the like to prevent sand or other debris from entering cavities or channels through which air flows in the RF system.

[0065] In various embodiments, the RF system, comprising various components such as module housings, upper and lower housings, processing modules, RF modules, and power supply modules and / or antennas, can be manufactured with high temperatures and / or extreme environments in mind. For example, specific materials (such as metals) can be used for faster heat dissipation. Additionally, for example, each of the processing module, RF module, and power supply module can include a separate enclosure that provides additional environmental protection, shock protection, and thermal conductivity (e.g., to provide thermal conductivity and heat dissipation to the exterior of the individual components) for the internal components. Therefore, the RF system can advantageously provide shielding for sensitive components from weather, sunlight (e.g., heat), and other external threats that may damage or reduce the efficiency of the device (e.g., processor throttling due to high temperatures).

[0066] In various embodiments, the RF module may include power amplifier technology. For example, the RF module may include a radio frequency (“RF”) power amplifier as an electronic amplifier that converts a low-power RF signal into a higher-power signal. The RF module may also include digital and / or analog filter technology. For example, a digital filter (e.g., in signal processing) may perform mathematical operations on a sampled discrete-time signal to reduce or enhance certain characteristics of the signal. The RF module may also include a multiplexer to provide reception and transmission via a directional antenna.

[0067] In various embodiments, the RF system (e.g., a processing module) may also include positioning, navigation, and timing (“PNT”) capabilities. These PNT capabilities may be provided by one or more PNT components, which may include, for example, Global Navigation Satellite System capabilities (e.g., Global Positioning System (“GPS”) capabilities) and other PNT functions. The one or more PNT components may further provide orientation information, altitude information, angle / tilt information, and / or the like. In some embodiments, the PNT capabilities may be provided, in whole or in part, in a direction finder and / or by a direction finder. The PNT capabilities of the RF system may be provided by one or more PNT components and / or the like. PNT capabilities may also be referred to herein as “positioning capabilities,” and one or more PNT components may also be referred to herein as “positioning components” and / or the like. The PNT capability of an RF system can be used for, for example, object location determination and / or tracking, as described herein, because this capability can depend on the position, orientation, tilt angle and / or similar of the RF system (e.g., enabling a correctly oriented antenna with correct orientation and tilt to be used to detect or calibrate an object).

[0068] In various embodiments, the processing module may include a machine learning component that can be used to assist an RF system in detecting and / or identifying one or more RF signals captured by a connected antenna. For example, the machine learning component may implement machine learning (“ML”) algorithms, artificial intelligence (“AI”) algorithms, ML models, other stylized algorithms, and / or the like (generally referred to herein as “AI / ML algorithms,” “AI / ML models,” or simply “ML algorithms,” “ML models,” and / or the like”) that can be implemented by one or more processors. Enabling an AI / ML model to identify RF signals can advantageously provide a significant improvement over a known system because many detected signals may contain interference at the same level, which is relatively weak and difficult to detect, or otherwise difficult to identify due to other factors. In various embodiments, the machine learning component may use one or more machine learning algorithms to implement one or more models or parameter functions for detection / identification. Machine learning components can be configured to apply a model that helps detect which types of RF signals (e.g., a series of RF signals, a specific frequency or combination of frequencies, and / or the like) indicate which types of objects.

[0069] In various embodiments, a machine learning model of an RF system can be programmed or trained by: (1) sampling the raw signal (e.g., captured from one or more connected antennas), (2) annotating the signal (e.g., frequency, time, and intensity), (3) filtering the signal, and (4) training the model. The trained model can then be applied by the RF system to the received or captured RF signal for identification purposes. For example, in various embodiments, the application of the trained machine learning model may include: (1) sampling the raw signal, (2) applying the trained model, and / or (3) outputting categories and probabilities (e.g., associated with object type). Then, for example, a processing module may identify a type of captured RF signal and / or object based on the output of (3) categories and probabilities. Also, in various embodiments, the application of the trained machine learning model may include (0) a preliminary step of filtering the baseline signal and / or friendly signal.

[0070] In various embodiments, sampling of the raw signal or raw signal (e.g., RF) data can include any form of data sampling. For example, data sampling can include a statistical analysis technique for selecting, manipulating, and analyzing a representative subset of data points to identify patterns and trends in a larger dataset under examination. This enables the processing of a small manageable amount of data that can represent a large, unmanageable amount of data. Sampling can advantageously enable the analysis of datasets that are too large to be fully analyzed or efficiently analyzed within a required time frame. In various embodiments, the RF system can sample the raw signal over the same time period (e.g., the same number of milliseconds, such as 1 ms, 2 ms, 3 ms, 5 ms, 10 ms, 50 ms, or the same other time period). In various embodiments, other or additional sampling methods may be employed.

[0071] In various embodiments, an RF system (e.g., via one or more processing modules and / or one or more RF modules) may use an identified type of object (e.g., from the output of an applied machine learning model) to generate one or more new signals and transmit the new signals using one or more directional antennas. The new signals may be transmitted in the direction of the identified signal or one or more objects. Therefore, the RF system may selectively transmit signals with different powers via antennas in various directions. In various embodiments, the identified signal corresponds to one or more moving objects (e.g., vehicles, boats, aircraft, drones, and / or the like), and the transmitted signal may affect communication in the vicinity of the moving object during transmission. In various embodiments, the detection or identification of an object or the identification of a signal corresponding to the object may be received from one or more other systems or sensors. Generating a signal based on the identified signal may be advantageous due to increased power efficiency / optimization. For example, instead of transmitting signals across the entire frequency band, signals may be transmitted only at a specific frequency or within a narrow frequency range, thereby increasing power efficiency and / or signal power to reach greater distances. In various embodiments, the transmitted signal can be further filtered to limit interference with sensitive and friendly systems in the area.

[0072] In various embodiments, the RF system can track an identified signal or object (e.g., when the RF system is transmitting or not transmitting). In various embodiments, a direction finder can also provide more accurate tracking. For example, a direction finder combined with one or more antennas can identify (e.g., when the antennas are transmitting or not transmitting) the direction in which a detected signal is emitted.

[0073] In various embodiments, there may be other sensors or systems that can be connected to the RF system to provide additional data (e.g., and include other RF systems in the area), which may be used to: (1) improve detection performed by the RF system using a machine learning model; (2) assist the RF system in continuing to track, or initiating tracking of an object or signal; and / or (3) generate and transmit, or continue to generate and transmit, a specific signal in the direction of an object, and other functions.

[0074] In various embodiments, each RF system may include software that can be updated over-the-air (“OTA”) or via electrical hardwired connection, including machine learning models or components. For example, an RF system may be connected to a central processing server to receive updates. As another example, if multiple RF systems are installed in an area, it may be beneficial for the RF systems to connect over time and update each other’s machine learning models (e.g., by sending updated models or capturing relevant data so that each RF system can be trained on additional data) so that each RF system has the latest available data or model. In various embodiments, although the RF systems may be in the same area, it may be beneficial to share only a portion of the data or machine learning models between the RF systems, because there may be subtle differences between the fields of view of each RF system that may cause one model to be more suitable for a first environment / area than another model that is more suitable for a second environment / area.

[0075] In various embodiments, a series of one or more RF systems can be placed in an area. For example, a first RF system may be placed in the northeast corner of a building, with one antenna facing north and another facing east. A second RF system may be placed in the southwest corner of the same building, with one antenna facing south and another facing west. Thus, four antennas (and / or additional RF systems and associated antennas) connected to the two RF systems can monitor a 360° area (or approximately a 360° area) around the building, while simultaneously omitting any signal detection from the building itself. Due to the orientation, when any antenna is transmitting a signal, the transmission is directed away from the building, ensuring that the building and any equipment or personnel within it are not affected by the transmission. The orientation and signal filtering described herein can be further refined to limit interference to friendly areas and equipment.

[0076] Furthermore, according to various embodiments, a variety of interactive graphical user interfaces can be provided, thereby allowing various types of users to interact with the systems and methods described herein to, for example, generate, view and / or modify data captured or used by one or more RF systems or connected systems.

[0077] The interactive and dynamic user interface described herein is achieved through innovative and efficient interaction between the user interface and the underlying system and components. For example, this paper discloses an improved method for receiving user input, translating and delivering such input to various system components, automatically and dynamically executing complex procedures in response to input delivery, automatically interacting among various components and procedures of the system, and automatically and dynamically updating the user interface. Therefore, compared to previous systems, the interaction and presentation of data through the interactive user interface described herein offers cognitive and ergonomic efficiency and advantages.

[0078] Therefore, in various embodiments, large amounts of data can be automatically and dynamically collected and analyzed in response to user input and configuration, and the analyzed data can be efficiently presented to the user. Thus, in some embodiments, the systems, devices, configuration capabilities, graphical user interfaces, and similar systems described herein are more efficient than prior art systems and / or the like.

[0079] The various embodiments of this invention provide improvements to various technologies and fields, as well as practical applications of various technical features and advancements. For example, as described above, some existing systems are limited in various ways, and the various embodiments of this invention provide significant improvements over such systems and practical applications of such improvements. Furthermore, the various embodiments of this invention are inseparable from computer technology and provide practical applications of computer technology. Specifically, the various embodiments rely on dedicated hardware and software components installed in specific locations to improve energy and processing efficiency. These features and others are inseparable from and implemented by computer technology, artificial intelligence, and digital signaling technology, and would not exist without computer technology, artificial intelligence, and digital signaling technology. For example, the RF system, processing module, RF module, and signal detection, generation, and transmission functionality and interaction with detected objects / signals described herein with reference to the various embodiments cannot be reasonably performed independently by humans without the computer and technology implementing them. Moreover, the various embodiments of this invention achieve many of the advantages described herein through the implementation of computer technology, including more efficient interaction and analysis with various types of electronic data, and the like.

[0080] Embodiments of the invention are described below with reference to the accompanying drawings, wherein like numbers refer to like elements throughout. The terminology used in the description presented herein is not intended to be interpreted in any limited or restrictive manner, but is used only as part of a detailed description of certain specific embodiments of the invention. Furthermore, embodiments of the invention may include several novel features, none of which are individually desired attributes or essential for practicing the embodiments of the invention described herein. II. [, the term , ] [ ]

[0081] To facilitate understanding of one of the systems and methods discussed herein, several terms are defined. The terms defined below, as well as other terms used herein, should be broadly interpreted to include the common and conventional meanings of the terms as defined and / or other implied meanings of their respective terms. Therefore, the definitions below do not limit the meaning of these terms, but are merely illustrative.

[0082] [User Input] [(] [Also known as "input"] [)] [:] Any interaction, data, instruction, and / or similar received by a system / device from a user, a representative of the user, an entity associated with the user, and / or any other entity or object. Input may include any interaction intended to be received and / or stored by the system / device; causing the system / device to access and / or store data items; causing the system to analyze, integrate, and / or otherwise use data items; causing the system to update displayed data; a manner causing the system to update displayed data; transmitting or accessing data; and / or similar. Non-limiting examples of user input include keyboard input, mouse input, digital pen input, voice input, finger touch input (e.g., via a touch-sensitive display), gesture input (e.g., hand movement, finger movement, arm movement, movement of any other appendage, and / or body movement) and / or similar. Additionally, user input to the system may include input via tools and / or other objects manipulated by the user. For example, a user may move an object (such as a tool, pen, or magic wand) to provide input. For example, user input may include motion, positioning, rotation, angle, alignment, orientation, configuration (e.g., fist, hand flat, extended finger and / or similar) and / or similar. For example, user input may include the positioning, orientation and / or motion of a hand or other appendage, a body, a 3D mouse and / or similar.

[0083] [Data Storage:] Any computer-readable storage medium and / or device (or a collection of data storage media and / or devices). Examples of data storage include, but are not limited to, optical discs (e.g., CD-ROM, DVD-ROM, and / or the like), magnetic disks (e.g., hard disks, floppy disks, and / or the like), memory circuits (e.g., solid-state drives, random access memory (RAM), and / or the like) and / or the like. Another example of a data storage device is a hosted storage environment (often referred to as "cloud" storage) comprising a collection of physical data storage devices that are remotely accessible and can be rapidly deployed on demand.

[0084] [Database:] Any set of data or data structure (and / or a combination of sets of data or data structures) used for storing and / or organizing data, including but not limited to relational databases (e.g., Oracle databases, PostgreSQL databases and / or similar), non-relational databases (e.g., NoSQL databases and / or similar), in-memory databases, spreadsheets, comma-separated value (CSV) files, Extensible Markup Language (XML) files, TeXT (TXT) files, general files, spreadsheet files and / or any other widely used or proprietary format used for data storage. Databases are typically stored in one or more data stores. Therefore, each database mentioned herein (e.g., in the description herein and / or the figures of this application) should be understood as being stored in one or more data stores. Furthermore, although the present invention may show or describe data stored in combined or separate databases, in various embodiments, such data may be combined and / or separated in any suitable manner into one or more databases, one or more tables in one or more databases and / or the like. As used herein, for example, a data source may refer to a table in a relational database. It may also be referred to herein as a "data set" and / or the like. III. [, Indicative operating environment , ] [ ]

[0085] Figure 1A illustrates a block diagram of an exemplary operating environment 100 in which one or more embodiments of the present invention may operate according to various embodiments. The operating environment 100 may include an RF system 102, an optional additional RF system 106, an additional system or sensor 104, a central processing server 107, and one or more user devices 110. Each RF system 102 (and the optional additional RF system 106) may include various hardware components 103 and software components 105 that provide various functionalities as further described herein.

[0086] In various embodiments, communication among the various components of the exemplary operating environment 100 can be accomplished via any suitable device, system, method, and / or the like. For example, RF system 102 (and optionally additional RF system 106) can communicate with each other via network 112 or any other wired or wireless communication network, method (e.g., Bluetooth, WiFi, infrared, cellular, and / or the like) and / or any combination thereof, and communicate with additional systems or sensors 104, central processing server 107, and one or more user devices 110. As further described below, for example, network 112 may include one or more internal or external networks, the Internet, and / or the like.

[0087] This document describes, with reference to various figures, further details and examples of the implementation, operation and functionality of the various components of the RF system 102 and the exemplary operating environment 100. a. [, network , ] [, 112 , ]

[0088] Network 112 may comprise any combination of wired networks, wireless networks, or the like. For example, network 112 may be a personal area network, a local area network, a wide area network, an over-the-air broadcast network (e.g., for radio or television), a cable network, a satellite network, a cellular telephone network, or the like. As a further example, network 112 may be one of the publicly accessible interconnected networks that may be operated by various different parties, such as the Internet. In various embodiments, network 112 may be a private or semi-private network, such as a corporate or university intranet. Network 112 may include one or more wireless networks, such as a Global System for Mobile Communications (GSM) network, a Code Division Multiple Access (CDMA) network, a Long Term Evolution (LTE) network, C-band, millimeter wave, sub-6 GHz, or any other type of wireless network. Network 112 may use protocols and components for communication via the Internet or any other of the aforementioned types of networks. For example, protocols used by Network 112 may include HyperFile Transfer Protocol (HTTP), HTTP Secure (HTTPS), Message Queuing Telemetry Transport (MQTT), CoAP (Co-Application Protocol), and the like. Protocols and components used for communication via the Internet or any other communication network of the aforementioned types are well known to those skilled in the art and therefore will not be described in further detail herein.

[0089] In various embodiments, network 112 may represent a network that is localized within a particular organization, such as a private or semi-private network, like a corporate or university intranet. In some embodiments, devices (e.g., RF system 102, RF system 106, additional systems or sensors 104, central processing server 107, (a number of) devices 110 and / or the like) may communicate via network 112 without traversing an external network such as the Internet. In some embodiments, devices connected via network 112 may be isolated from the Internet; for example, network 112 may not be connected to the Internet. Thus, for example, (a number of) user devices 110 may communicate directly (via wired or wireless communication) or via network 112 with RF system 102, RF system 106, or additional systems or sensors 104 without using the Internet. Therefore, even if network 112 or the Internet fails, RF system 102, RF system 106 or additional systems or sensors 104 can continue to communicate and operate via direct communication (and / or via network 112).

[0090] In various implementations, various other forms of network 112 and / or operating environment 100 may be incorporated into "mesh" type communication and / or secure communication within the components. Examples of such mesh and / or secure communication are described in U.S. Patent No. 10,506,436 ('436 Patent), published December 10, 2019, entitled "Lattice Mesh," the entire disclosure of which is incorporated herein by reference as if its entirety were described herein and all its contents are incorporated herein by reference for all purposes. For example, and in some embodiments as described herein, the detection and / or identification of (certain) objects and / or the response to such detection and / or identification (e.g., by transmitting one or more RF signals) may be performed by one or more systems (e.g., RF systems), devices, sensors, or the like. For example, any device and / or sensor may communicate with one or more of the RF systems described herein such that all or part of the information transmitted between the devices may be used (e.g., in combination with one or more of the RF system’s own detections) to initiate a response (e.g., to transmit one or more RF signals). b. [, Additional systems or sensors , ] [, 104 , ] [ ]

[0091] Additional system or sensor 104 may include, for example, various sensors and monitoring devices. For example, non-limiting examples of additional system or sensor 104 may include: sensors / monitors (e.g., temperature, positioning / location, PNT, direction finder, altitude, angle / tilt, level, vibration, electrical, pressure and / or the like); video cameras (e.g., video, audio, position, motion, thermal and / or the like); antennas (e.g., long-range, short-range and / or the like); radar devices; optical detection and ranging ("LIDAR") devices; mobile systems or sensors (e.g., a sensor on a vehicle or an aerial drone); fixed systems or sensors (e.g., a sensor on a tower site); other types of systems or sensors; and / or any combination of the foregoing. Additional instances of systems or sensors 104 that can be included in operating environment 100 and can provide information to or receive information from RF systems 102, 106, as described herein, are found in U.S. Patent Application Publication No. 2020 / 0167059 ('059 Publication), filed November 27, 2018, entitled "Interactive Virtual Interface," and in U.S. Patent Application Publication No. 2020 / 0363824 ('824 Publication), filed May 17, 2019, entitled "Counter Drone System." The full disclosure of those patents is hereby incorporated by reference as if its entire contents were described herein.

[0092] As described herein, RF system 102 can communicate with additional systems or sensors 104, providing information to or receiving information from additional systems or sensors 104. Similarly, RF system 102 can communicate with one or more RF systems 106, providing information to or receiving information from one or more RF systems 106. Similarly, RF system 106 can communicate with additional systems or sensors 104, providing information to or receiving information from additional systems or sensors 104. In various embodiments, communication among the various components of the operating environment 100 may be accomplished via intermediary communication with a centralized server or database (e.g., central processing server 107) capable of storing data associated with additional systems or sensors 104. Alternatively, additional systems or sensors 104 may communicate with and / or be configured via communication with user devices 110. Data and information collected from the additional system or sensor 104 can be provided directly or indirectly to the RF system 102.

[0093] In various implementations, one or more or a combination of RF system 102, RF system 106 and / or (a number of) user devices 110 may provide an application programming interface ("API") that allows communication to be performed using additional systems or sensors 104.

[0094] As described herein, various communications within the components of the operating environment 100 can be used to determine the position of objects (which may include moving objects) via various methods. Examples of such communications and methods for determining the position of objects are provided, for example, in '059 Publication and '824 Publication. c. [, Central Processing Server , ] [ ]

[0095] Central processing server 107 may include, for example (e.g., via network 112), one or more computing systems connected to RF systems (e.g., 102 and 106), additional systems or sensors 104 and / or (a number of) user devices 110. For example, data and information collected from additional systems or sensors 104, RF system 102, or RF system 106 may be provided directly or indirectly to central processing server 107 for storage, analysis, and / or transmission to other connected systems. For example, an RF system 106 may detect / identify a specific signal and / or object, and RF system 106 may transmit this data to central processing server 107, which may then transmit an indication of one of the detected signals to other systems (e.g., RF system 102). For example, in some instances described herein, RF systems (e.g., 102 and 106) may work together in a network to detect signals around a specified area or location (e.g., a building) because each RF system includes an antenna facing only a specific direction. In various embodiments, the central processing server 107 may communicate with and / or be configured via communication with (a number of) user devices 110.

[0096] As mentioned above and described herein, various components of the operating environment 100 can be used to determine the position of objects (which may include moving objects) via various methods. For example, examples of such methods for determining the position of objects are provided in '059 disclosure and '824 disclosure. Therefore, the central processing server 107 and / or (some) user devices 110 of the present invention may be wholly or partially similar to the interactive virtual interface system of '059 disclosure, in which various sensor data and position determinations can be integrated. This position information can be further shared among various components of the operating environment 100 (e.g., RF systems 102, 106) to achieve coordination among components to transmit generated signals to the positioned objects (which may include moving objects). Furthermore, as mentioned above, communication among the various components of the operating environment 100 can be provided via various methods, examples of which are described in '436 patent.

[0097] In various embodiments, the central processing server 107 may include hardware similar to that of the computer system described herein with reference to FIG3. Alternatively, in various embodiments, the central processing server 107 may exist via software, thereby linking several RF systems and any other optional additional systems or sensors together, such that the systems or sensors can share information among themselves (in these embodiments, the various common components of the RF systems can provide functionality similar to that of the computer system described with reference to FIG3). In various embodiments, the central processing server 107 may generate a mesh network, an example of which is described in '436 patent (as mentioned above). For example, the central processing server 107 may include an interface and a processor. The interface may be configured to receive a login request from a host, wherein the login request includes a key that the host wishes to claim and a set of asset identifiers ("IDs"). The processor can be configured to sign a key to generate an RA-signed key with a Resource Authorization ("RA") credential; update an asset database using the RA-signed key; expose the key to a network-distributed RA-signed host; and provide the RA-signed key to the host. In various embodiments, the server may further include a memory coupled to the processor and configured to provide instructions to the processor. A system for a mesh network may include a security mechanism for communication between nodes (e.g., RF system 102, RF system 106, additional systems or sensors 104, user devices 110, and / or the like) that enables messages with a target destination in a point-to-point mode and a mechanism that allows a message to be exposed to multiple destinations. The security of the communication can be designed to prevent the use of a compromised node to obtain significant information traffic from the network once a node is compromised. In addition, although the performance of network links is variable, the network can prioritize real-time data. The network can also ensure security by establishing secure routing using point-to-point authorization. Networks can also strategically cache data flowing within the network, allowing data to be sent when channels are available. Compared to other networks, mesh networks offer an improvement in security. Networks can be designed to overcome the possibility of unstable communication links and node failures. Mesh networks can overcome potential problems using security systems that preserve messages, routes, and the backfilling of messages waiting to be sent through the network.

[0098] In various embodiments, the central processing server 107 may provide an application programming interface (“API”) through which communication can be accomplished using RF system 102, RF system 106, (a number of) user devices 110 and / or additional systems or sensors 104. For example, data collected or generated by RF system 102 may be sent to the central processing server 107 to be combined with other data collected (e.g., from RF system 106 and / or additional systems or sensors 104) and stored for subsequent transmission to network 112 or any system outside network 112 (e.g., via the Internet using an API). In various embodiments, for example, the central processing server 107 may also implement some or all of the machine learning and / or data or signal processing performed by the RF systems (e.g., 102 and 106). d. [, ( , ] [, several , ] [, ) , ] [, Exemplary user devices , ] [ ]

[0099] (Several) user devices 110 may include a computing device that provides a user or administrator with a means of interacting with a device (e.g., RF system 102, RF system 106, additional system or sensor 104, or central processing server 107). User device 110 may include a user interface or dashboard that connects a user to a machine, system, or device commonly used in industrial processes. In various embodiments, (several) user devices 110 include a computer device having a display and a mechanism for user input (e.g., mouse, keyboard, voice recognition, touch screen, and / or the like). In various embodiments, (several) user devices 110 include tablet computing devices, laptop computing devices, or smartphones.

[0100] As mentioned above, (a number of) user devices 110 can communicate with RF system 102, RF system 106, additional systems or sensors 104 and / or central processing server 107 via direct (e.g., not via a network) wired and / or wireless communication and / or via a network (e.g., a local area network) wired and / or wireless communication. Advantageously, according to various embodiments, a user can configure an interactive user interface layout and can then push the interactive user interface layout configuration to one or more RF systems 102 and / or 106. In various embodiments, RF systems 102 and / or 106 can then remotely provide the configured interactive user interface to any user device 110 connected to RF systems 102 and / or 106. Advantageously, this functionality enables remote and centralized configuration of the interactive user interface without direct programming or interaction with RF systems 102 and / or 106 or (a number of) user devices 110. Advantageously, according to various embodiments, since the connection interface is provided by RF system 102 and / or 106, multiple user devices 110 can simultaneously access RF system 102 and / or 106 and / or communicate with RF system 102 and / or 106, and the current configuration / state of one of RF system 102 and / or 106 can be accurately kept synchronized / updated from each device and among such devices.

[0101] In various implementations, a user can operate the RF system 102 (and / or the RF system 106 and other components of the operating environment 100) via one or more user interfaces (and / or other user interfaces of the central processing server 107) accessible via device(s) 110. Through these user interfaces, the user can view and / or set a configuration or status of the RF system 102, receive instructions from an identified object of the RF system 102 (and / or the central processing server 107, which provides coordination among the various components of the operating environment 100), grant authorization to the RF system 102 (and / or the central processing server 107, which provides coordination among the various components of the operating environment 100) to initiate a transmission to an identified object, view the battery health of the RF system 102, and access automatic login and / or similar functions associated with the RF system 102 (and / or the central processing server 107).

[0102] In various embodiments, user device 110 may include a relatively simplified interactive graphical user interface. For example, interactive user device 110 may include a relatively few large buttons, by which a user can select to stop a currently running configuration, select a different configuration from a list, search for a different configuration, and / or monitor the current state of one of the inputs / outputs, analytics, machine learning models, and / or similar (and as mentioned above).

[0103] Furthermore, as mentioned earlier, the design of computer user interfaces "is a remarkable problem for software developers to make them both human-usable and easy to learn." (Dillon, A. (2003) User Interface Design. MacMillan Encyclopedia of Cognitive Science, Vol. 4, London: MacMillan, pp. 453-458.) This invention describes various embodiments of interactive and dynamic graphical user interfaces resulting from significant advancements. These remarkable advancements have led to the graphical user interfaces described herein, which offer significant cognitive and ergonomic efficiency and advantages compared to prior art systems. Interactive and dynamic graphical user interfaces include improved human-computer interaction that provides a user with reduced mental workload, improved decision-making, improved capabilities, reduced work stress, and / or similar benefits. For example, compared to previous systems, user interaction via the input and interactive graphical user interface described herein can provide an optimized display or interaction with a video gateway device or controller device, and enable a user to access, navigate, evaluate and process analysis, configure, receive / operate data and / or the like more quickly and accurately.

[0104] Furthermore, the interactive and dynamic graphical user interface described herein is innovatively achieved through efficient interaction between the user interface and the underlying system and components. For example, this paper discloses the following improved method: receiving user input (including methods of interacting with and selecting received data), translating and delivering such input to various system components (e.g., RF systems 102 and / or 106), automatically and dynamically executing complex procedures in response to input delivery (e.g., performing configurations on RF systems 102 and / or 106), automatic interaction among various components and procedures of the system, and automatic and dynamic updating of the user interface (e.g., to display information related to RF systems 102 and / or 106). Therefore, compared to previous systems, the interaction and presentation of data via the interactive graphical user interface described herein provides cognitive and ergonomic efficiency and advantages. IV. [, RF , ] [, system , ] [ ]

[0105] RF system 102 may include hardware and software components and may be a modular, adaptable, and portable system comprising one or more antennas. RF system 102 may be configured to receive or capture external RF signals from one or more directions (e.g., directions (to which the antennas face and the configured field of view angle), determine one or more RF signals to be transmitted based on the received RF signals (e.g., by applying one or more machine learning models and determining one type of object), and generate and transmit the determined one or more RF signals in a specific direction and with a specific power, as well as other functionalities as described in more detail herein.

[0106] In various implementations, in addition to RF system 102, one or more additional RF systems 106 may be provided (e.g., as illustrated in the exemplary operating environment 100 of FIG. 1A). Each RF system 106 may typically include a similar configuration and functionality to RF system 102. For example, each of RF system 102 and RF system 106 may include similar hardware and software components and functionality. Each RF system may also differ in various ways; for example, each may include one or more module housings and one or more directional antennas, as well as other features. The description herein provides details of implementations of RF system 102, but each RF system 106 may be implemented similarly.

[0107] Although RF system 102 is shown as separate from RF system 106, in some embodiments, each of the shown RF systems may have its own unique functionality (e.g., specificity of its trained data model based on location / placement, which may be the same as or different from other RF systems, different hardware or software, different frequency ranges to be monitored due to a configured blacklist or whitelist, or other features) or shared functionality among all RF systems (e.g., a shared machine learning model, shared data input, shared blacklist or whitelist, or other features). Therefore, in some embodiments, the functionality of the RF systems may reside on one or more devices. For example, processing of data signals received by one RF system 102 may be performed by RF system 102 or a combination of RF system 102 and one of the other RF systems 106. In some embodiments, RF system 102 may perform functions unique to RF system 102, and RF system 106 may perform functions unique to RF system 106. In some embodiments, a combination of features may be available to all RF systems, with some features unique to each RF system and some features shared. In some applications, only one RF system may be used, and therefore all available features or functionality of the single RF system will reside on that single RF system. In some embodiments, additional systems or sensors 104 may provide additional data or functionality to (a number of) RF systems, as described herein. As described above, coordination among the various components of the operating environment 100 may be direct, and / or via a central processing server 107, and other possible configurations.

[0108] In various embodiments, an additional RF system 106 may be installed in an area associated with or near RF system 102. In various embodiments, RF systems 102 and 106 may be placed in an area and connected together (e.g., via network 112 or hardwired connection). For example, an RF system 102 may be placed in the northeast corner of a building, with one antenna facing north and connected to RF system 102, and another antenna facing east and connected to RF system 102. Similarly, an RF system 106 may be placed in the southwest corner of the same building, with one antenna facing south and connected to RF system 106, and another antenna facing west and connected to RF system 106. Therefore, four antennas connected to the two RF systems (and / or additional RF systems and associated antennas) can monitor a 360° area (or approximately a 360° area) around the building, while simultaneously omitting any signal detection from the building itself. Due to the orientation, when any antenna is transmitting a signal, the transmission can be directed away from the building, so that the building and any equipment or personnel within the building are not affected by the transmission. The directional and signal filtering described in this article can be improved to further limit interference to friendly areas and equipment.

[0109] In various embodiments, the RF system can be manufactured as a compact, lightweight, portable, and / or adaptable design, allowing it to be placed in a variety of locations and positions. For example, in one embodiment, the RF system may have an overall height (e.g., length) between about 20 cm and about 180 cm, and an overall weight between about 10 kg and about 100 kg. Furthermore, depending on the application (e.g., reduced or increased size, different shapes, and the like), a directional broadband antenna may be disconnected from the RF system's module housing and interchanged with different types of antennas that may provide different functionalities (e.g., wider or narrower field of view (such as omnidirectional), longer range sensitivity, shorter range sensitivity, and the like) and / or different physical properties to improve mobility or adaptability. For example, if the RF system is to be moved from the roof of a building to a vehicle, it may be necessary to use one or more different antennas configured to securely attach to the vehicle while it is in operation and simultaneously meet new requirements associated with the placement. These requirements may include the ability to monitor a field of view wider than a previously positioned location on the side of a building, and a wider field of view can be achieved using additional antennas configured in different locations and / or different antennas. a. [, RF , ] [, Indicative hardware components of the system , ]

[0110] Figure 1B illustrates a block diagram of an exemplary hardware component 103 of an RF system 102 according to various embodiments of the present invention. In addition to the description below, further details of the hardware component and related functionality are described below with reference to, for example, Figures 4A-4D, 5A-5E, 7A-7B, 8, and 9A-9D. Hardware component 103 may include, for example, a direction finder 120, one or more directional antennas 122, a communication component 129, a cooling component 124, one or more power supply modules 141, one or more housing components 142, one or more RF modules 131, and one or more processing modules 130. Software components 105 of the RF module 102 may be implemented on various components of the RF system 102, but according to various embodiments, are primarily implemented on one or more processing modules 130, as described herein.

[0111] As mentioned above, RF system 102 can be advantageously modularized to achieve multiple configurations for various applications. The modularity of the RF system can be found in both the modularity of a single individual RF system that can operate independently (including coordinating with one or more additional systems or sensors) and the modularity of multiple RF systems that can coordinate with each other (including coordinating with one or more additional systems or sensors). The modularity of the RF system can be partially achieved by various housings 142 of RF system 102, which can accommodate or provide attachment for various other hardware components 103.

[0112] For example, RF system 102 can be implemented using one module housing, two module housings, or more module housings. In instances of two or more module housings, the module housings of RF system 102 can be joined together by one or more connecting housings. Thus, in one embodiment, RF system 102 may include two stacked module housings joined together by a connecting housing. In various embodiments, the RF system may also include an upper housing and a lower housing, and may further include components for mounting the RF system, such as one or more mounting brackets, clamps, slides, pins, and / or the like. Advantageously, due to its modularity, the RF system can be appropriately configured for a given application and mounted on a tripod, a vehicle, a building, and / or the like.

[0113] Figure 1D illustrates a perspective view of an exemplary embodiment 180 of an RF system 102 comprising two module housings and four directional antennas according to various embodiments of the present invention. However, in various embodiments, the RF system may include more or fewer antennas and may comprise a single module housing or more than two module housings. The exemplary embodiment 180 of the RF system 102 includes an upper housing 182, a first (e.g., top) module housing 184, a combined housing 186, a second (e.g., bottom) module housing 188, and a lower housing 190. The exemplary embodiment 180 of the RF system 102 further includes directional antennas 192a to 192d, a direction finder 194, and one or more control panels 196. Although not shown in Figure 1D, the RF system 102 may also include mounting points or surfaces 193, such as on the bottom surface of the lower housing 190, for mounting one or more of the RF system 102.

[0114] As shown in Figure 1D, the various housings of the RF system 102 can be combined to form a main housing of the RF system 102. As shown, directional antennas 192a to 192d and direction finder 194 can be mounted to the exterior (or outside) of the housing surface via one or more coupling points or antenna mounts. Each of the one or more antenna mounts can provide one or more degrees of freedom. Each degree of freedom can reflect the ability of the respective antenna to tilt, rotate, or translate along one or more axes.

[0115] The upper housing may include one or more vents 198, and the lower housing may include one or more vents 199. Vents 198 and 199 may enable or facilitate airflow within the RF system housing (such as within an internal portion or cavity of RF system 402). This airflow may be caused by one or more fans positioned within the upper and / or lower housings to facilitate airflow from the vents in the lower housing 190 upwards through an internal portion (also referred to herein as an internal portion) or cavity of RF system 180 and out through the vents 198. The internal portion or cavity of the RF system may include heat sinks thermally coupled to one or more modules located within a peripheral internal portion of the RF system (e.g., within a housing of module housings 184, 188, as described herein).

[0116] Referring again to FIG1B, each module housing may accommodate one or more processing modules 130, one or more RF modules 131, and one or more power supply modules 141. In various embodiments, the processing module 130 may include a system-on-module ("SOM") configuration and may therefore be referred to herein as an "SOM module". Each of the module housing and the associated processing module(s) 130, RF module(s) 131, and power supply module(s) 141 may support one or more directional antennas 122 (e.g., antennas 192a to 192d of FIG1D) and / or a direction finder 120 (e.g., direction finder 194 of FIG1D). In one embodiment, each module housing includes a single processing module 130, two RF modules 131, and a power supply module 141. In this embodiment, each RF module 131 supports a single directional antenna 122 (and therefore the module housing supports up to two directional antennas 122), the processing module 130 supports two RF modules 131, and the power supply module 141 provides power to the processing module 130 and the two RF modules 131. Therefore, in one configuration where the RF system 102 includes one module housing, the RF system 102 can support up to two directional antennas 122, and in one configuration where the RF system 102 includes two module housings, the RF system 102 can support up to four directional antennas 122. In embodiments including two or more module housings, the multiple processing modules 130 can communicate directly with each other to provide the functionality described herein, or they can communicate with each other via a system management module. Additionally, in any of these configurations, the RF system 102 may further support one or more direction finders 120 via one or more components of the module housing (e.g., processing module 130, (a number of) RF modules 131 and / or power supply module 141).

[0117] RF system 102 may advantageously include a modular configuration that provides physical protection for components used in, for example, dirty or extreme environments. For example, each housing 142 may include cavities (which may be contained within a periphery of a module housing) in which processing modules 130, RF modules 131, and power supply modules 141 can be housed. The cavities may be sealed from the external environment or hermetically sealed. The housing may also include additional cavities for wiring and connections among the various components. These additional cavities may also be sealed from the external environment or hermetically sealed. These cavities may also advantageously provide shielding against electromagnetic interference (“EMI”) for the various components of RF system 102. For example, EMI shielding may be provided by constructing cavities of metal and / or other EM shielding materials or components. Additionally, the upper and lower housings may include vents, grilles, filters, or the like to prevent sand or other debris from entering portions of the RF system through which air can flow.

[0118] Therefore, in one embodiment, each RF module 131 may include its own enclosed housing housing its associated components, each processing module 130 may include its own enclosed housing housing its associated components, and each power supply module 141 may include its own enclosed housing housing its associated components. The housing of each module may be made of a thermally conductive material (such as metal). Each of these individual housings of the various modules may then be placed within a cavity of, for example, one of the module housings of the RF system 102. Furthermore, the various electrical components of the RF system 102 may be wired through various cavities for power and data communication with each other. For example, the processing module 130 may communicate wiredly with one of the RF modules 131, and the RF modules 131 may communicate wiredly with one of the directional antennas 122 and / or direction finders 120. This wired data and power communication may be accomplished via wiring through the cavities of the housings and via connectors on and through the housing surfaces and on the exterior of the housings. In some implementations, one or more of the various components of the RF system 102 may communicate with each other wirelessly.

[0119] In addition to the internal power supply module 141 described above (which provides appropriate power to various other modules and components of the RF system 102), (a plurality of) power supply modules 141 may also include an external or internal main power supply module that provides main power to the RF system 102. This power may come from a wired main power source or a battery power source. In some embodiments, the RF system 102 includes an internal battery power source that provides power to the components of the RF system 102 via (a plurality of) power supply modules 141. i. [, Orientation Analyzer , ]

[0120] Direction finder 120 (also referred to herein as a radio direction finder or direction-finding antenna) may include a radio direction finder (“RDF”) or other direction-finding device, and may be a device configured to find or otherwise identify the direction or orientation of a wireless power source. Direction finder 120 may include one or more antennas configured to perform direction finding. Direction finding may involve using two or more measurements from different locations. Based on two or more measurements, the location of an unknown object (e.g., a transmitter, vehicle, drone, and / or the like) or other target can be determined. In various embodiments, a transmission source can be located (e.g., via triangulation or other similar means) by combining directional information from multiple sources (e.g., other direction finders, other systems or sensors in the area, or one or more directional broadband antennas and / or the like).

[0121] The direction finder 120 can be used to detect any wireless power source. The size of the receiver antenna of the direction finder 120 can vary depending on the wavelength of the received signal. For example, a longer wavelength (lower frequency) can include a larger antenna. The ability to locate a transmitter can be particularly valuable in various applications involving the search and identification of the location of transmitted objects. The direction finder 120 may include one or more phased array antennas to allow for faster beamforming for more accurate searching. The direction finder 120 may include a sensing antenna, a dipole antenna, a parabolic antenna, and / or the like. The direction finder 120 may employ one or more of a phase or a Doppler technique. In various embodiments, a plurality of direction finders 120 may obtain directional information from two or more appropriately spaced receivers (or a single mobile receiver), and a transmission source can be located via triangulation.

[0122] The direction finder 120 can communicate with one or more (or all) of the processing modules 130 of the RF system 102 in any given configuration. In various embodiments, a plurality of direction finders 120 may be coupled to a common processing unit (e.g., processing module 130). As mentioned herein, in some embodiments, PNT capabilities (e.g., some or part of the PNT components 140) may be provided, in whole or in part, in and / or by the direction finder 120. ii. [, Directional antenna , ] [ ]

[0123] One or more directional antennas 122 can be configured to transmit and / or receive radio signals. As mentioned above, each directional antenna 122 can communicate electrically / wired with an RF module 131, which can provide signal reception or transmission amplification and other functionalities. In various embodiments, the directional antenna 122 can be designed to transmit and receive radio waves in a specific direction (directional or high-gain or "beamed" antenna). For example, in various embodiments, the directional antenna 122 can be directional and can be configured to radiate or receive signals over a region between 80 degrees and 110 degrees above a direction in which the first antenna is configured to face (e.g., a primary receiving and / or transmission angular arc of the antenna). In some embodiments, one or more directional antennas 122 can be configured to provide communication within an angle of approximately 90°, and thus two directional antennas can be configured to provide communication within an angle of approximately 180°, three directional antennas can be configured to provide communication within an angle of approximately 270°, and four directional antennas can be configured to provide communication within an angle of approximately 360°. Other combinations of antennas and various configurations are possible. In various embodiments, the directional antenna 122 may include one or more reflectors (e.g., parabolic reflectors), horns, and / or parasitic elements that can guide radio waves into a beam or other desired radiation pattern.

[0124] One or more directional antennas 122 can be physically located and configured to transmit or receive at different power levels and frequencies in one or more specific directions, such that the antennas can collectively provide greater directivity and sensitivity in (a few) specific directions than in other directions. Advantageously, this allows for increased performance and reduced interference from undesired sources (e.g., sources not in the direction of the antenna's directivity). When greater concentration of radiation in a specific direction is desired, the directional antenna 122 can provide increased performance compared to a dipole antenna or a holographic antenna. Additionally, the directional antenna 122 can be a broadband antenna that can be used to transmit, receive, or transmit and receive radio signals over a wide spectrum. In various embodiments, the directional antenna 122 can be configured to transmit and / or receive radio signals in a subset of a wide spectrum. For example, there may be nearby devices where one of the antennas transmits signals within a specific frequency range, and the RF system may be programmed (e.g., using software) to filter the received signal to avoid interfering with the analysis of the received signal and / or (e.g., using software or additional digital signal filtering equipment) to filter the transmitted signal to minimize or remove interference to the operation of nearby devices. Therefore, the RF system can selectively transmit signals with different electrical powers via antennas in various directions.

[0125] In various embodiments, each directional antenna 122 and its associated electronic circuitry (e.g., associated RF module 131) can operate independently and in coordination with other directional antennas. For example, a single directional antenna 122 can be configured to face and monitor a 90° field of view, and four of the directional antennas (e.g., using 1, 2, 3, or 4 RF systems) can be configured to monitor a full 360° (or approximately 360°) field of view. In various embodiments, additional antennas may be used (e.g., five antennas each covering 72°, six antennas each covering 60°, seven antennas each covering 52°, and / or the like), fewer antennas may be used (e.g., one antenna each covering 360°, two antennas each covering 180°, three antennas each covering 120°), and / or some fields of view may overlap (e.g., four antennas each covering 120°, or the like).

[0126] Antennas can also be configured to include automated or manual adjustability relative to a vertical angle or tilt, allowing the antenna to be lowered towards the ground or adjusted to be higher towards the sky. In some applications, there may be an optimal angle that can be adjusted based on empirical data or artificial intelligence / machine learning (e.g., the machine learning model described herein or other models). The adjustability of the antenna angle can be provided by a user-adjustable antenna mount. The measured angle of an antenna may include an elevation angle or tilt relative to a plane on which the RF system is located (if the RF system is horizontally mounted, this may be the same as a line perpendicular to or normal to a side surface of the RF system on which the antenna is mounted).

[0127] In various embodiments, for the RF system 102, the corresponding one or more directional broadband antennas 122 and associated electronic circuitry may include multiple physical configurations. For example, the antennas and associated electronic circuitry may be configured to be detachable and / or stackable so that multiple antennas can be used in a given location. For example, there may be two antennas in one location, each configured to monitor a 90° field of view for a total field of view of 180° monitored by the two antennas. iii. [, Cooling components, environmental protection , ] [ ]

[0128] Cooling assembly 124 can be configured to remove heat generated by one or more hardware components 103. For example, cooling assembly 124 can help prevent temporary or permanent failures due to overheating of power supplies, amplifiers, integrated circuits (such as central processing units (“CPUs” and graphics processing units (“GPUs”)) and / or other components described herein. Other hardware components 103 described herein can be configured to generate less heat, but still generate more heat than could be removed without cooling assembly 124. Cooling assembly 124 may include one or more fans, one or more heat sinks, one or more thermal couplings, one or more heat pipes or conductors and / or the like configured to allow heat removal from the system. Thus, an RF system can advantageously include a modular configuration and materials for efficient heat dissipation from system components to enable the RF system to operate in high-temperature and / or extreme environments.

[0129] For example, the upper and lower housings may include a fan, and the upper and lower housings, (a number of) module housings, and the connecting housing (if applicable) may together provide a cavity or channel for airflow through the RF system to cool the various components of the RF system. The module housings may, for example, include heat sinks within the cavity or channel, and are thermally coupled to processing modules, RF modules, and power supply modules that can flow above them when propelled or drawn by the fan to cool the components of the RF system. The fan may cause air to flow upward from the lower housing through the heat sinks of one or more module housings and exit through the upper housing. Furthermore, each of the modules (e.g., (a number of) processing modules 130, (a number of) RF modules 131, and / or (a number of) power supply modules 141) may internally include various thermal couplings, heat pipes or conductors, and / or the like, to conduct heat to the thermal interface and thereby to the heat sink.

[0130] In various embodiments, the RF system, comprising various components such as module housings, upper and lower housings, processing modules, RF modules, and power supply modules and / or antennas, can be manufactured with high temperatures and / or extreme environments in mind. For example, specific materials (such as metals) can be used for faster heat dissipation. Additionally, for example, each of the processing module, RF module, and power supply module can include individual housings that provide additional environmental protection, shock protection, and thermal conductivity (e.g., to provide thermal conductivity and heat dissipation to the exterior of the individual components). Thus, the RF system can advantageously provide shielding for sensitive components from weather, sunlight (e.g., heat), and other external threats that may damage or reduce the efficiency of the device (e.g., processor throttling due to high temperatures). As mentioned above, the RF system may also include, for example, EMI protection for the various modules of the system.

[0131] In various embodiments, cooling assembly 124 may include a liquid cooling element that uses a liquid (e.g., water, liquid nitrogen) to cool other hardware components 103. The use of cooling assembly 124 may maintain or increase the clock speed of one of the components of processing module 130 (e.g., a processor 136, a GPU 138). iv. [, Communication components , ] [ ]

[0132] Communication component 129 may include various components that provide or implement communication within the RF system and communication with other systems and sensors. For example, such communication component 129 may include wires, optical fibers, transceivers, plugs, sockets, connectors and / or the like.

[0133] Communication component 129 includes wiring between directional antenna 122 and respective RF modules 131. This wiring may include a plug providing an interface to an external part of the RF system and an associated connector on a conductor from the antenna to allow the antenna conductor to be inserted into the plug to provide electrical communication between the antenna and the RF module. Communication component 129 includes similar wiring (including conductors, plugs, connectors, and / or the like) between direction finder 120 and one or more of RF modules 131 and / or processing modules 130. Communication component 129 also includes wiring or communication between RF module 131 and processing module 130; among multiple processing modules 130; and between processing module 130 and external systems or sensors 104, central processing server 107, and / or user devices 110.

[0134] In various implementations, communication component 129 may include electrical, optical, and / or electromagnetic communication channels. Communication component 129 may include components for communicating with other systems remote from the system. For example, communication component 129 may include a remote data interface, such as a wireless transmitter.

[0135] In various embodiments, communication component 129 may include one or more digital data interfaces for transmitting or receiving digital data via a wired link or a wireless link. For example, communication component 129 may include one or more wireless transceivers, one or more antennas, and / or one or more electronic systems (e.g., front-end modules, antenna switching modules, digital signal processors, power amplifier modules, and / or the like) supporting communication via one or more communication links and / or networks. In some instances, each transceiver may be configured to receive or transmit different types of signals based on different wireless standards via an antenna (e.g., an antenna chip). Some transceivers may support communication using a low-power wide-area network (“LPWAN”) communication standard. In some instances, one or more transceivers may support communication using a wide-area network (“WAN”), such as implementing a cellular network of 3G, 4G, 4G-LTE, or 5G. Furthermore, one or more transceivers may support communication via a narrowband LTE (“NB-LTE”), narrowband IoT (“NB-IoT”), or LTE-MTC communication connection to a wireless wide area network. In some cases, one or more transceivers may support Wi-Fi communication. In some cases, one or more transceivers may support data communication via a Bluetooth or Bluetooth Low Energy (“BLE”) standard. In some instances, one or more transceivers may be able to downconvert a baseband or data signal from a wireless carrier signal and / or upconvert a baseband or data signal to a wireless carrier signal. In some instances, communication component 129 may be able to wirelessly exchange data between other components (such as other parts of the system or another system, a mobile device (e.g., a smartphone, a laptop, and / or the like), a Wi-Fi network, a WLAN, a wireless router, a cellular tower, a Bluetooth device, and / or the like). The antenna may be able to transmit and receive various types of wireless signals, including but not limited to Bluetooth, LTE or 3G.

[0136] In various embodiments, the communication component 129 may also include the form of the PNT component 140. v. [, Processing module , ]

[0137] As mentioned above, each module housing of the RF system may include a processing module and an RF module, which include electronic circuitry configurable to connect to and operate one, two, three, four or more individual directional broadband antennas. For example, each processing module 130 may include memory 132, one or more motherboards 134, one or more processors 136, one or more GPUs 138, one or more software-defined radio ("SDR") transceivers, and one or more positioning, navigation, and timing ("PNT") components 140. The processing module 130 may be configured to receive and provide transmissions from one or more directional antennas. In various embodiments, a single module housing may be configured to have one processing module 130 and may support two directional broadband antennas, wherein the antennas may be placed in a single location or may be placed at a distance from each other (e.g., 5, 10, or 100 feet) and connected to the same module housing. For example, one antenna can be placed facing north on the north side of a building, and another antenna can be placed facing east on the east side of the building. Alternatively, the antennas can be placed in the northeast corner of the same location, with one antenna facing north and the other facing east. In various embodiments, two module housings combined in a single RF system can be configured to have two processing modules 130 and can support four directional broadband antennas, wherein the antennas can be placed in a single location, or the antennas can be placed at a distance from each other (e.g., 5, 10, or 100 feet) and connected to the same RF system.

[0138] As mentioned above, each processing module 130 may include a system-on-module ("SOM") configuration and may therefore be referred to herein as an "SOM module". In embodiments where a given RF system 102 includes two or more processing modules 130 (e.g., when the RF system includes two or more module housings), the multiple processing modules 130 may communicate directly with each other to provide the functionality described herein, or may communicate with each other via a system management module that provides coordinating functionality among the multiple processing modules 130. In embodiments using a system management module, the system management module may provide communication with other external systems or sensors and may relay such communication to the multiple processing modules 130. In various embodiments, the system management module may incorporate components and / or functionality (such as PNT component 140) of one or more processing modules. In various implementations, when the RF system 102 includes two or more processing modules 130, one of the processing modules may be manually and / or automatically designated as a system management module (and therefore there is no physically separate system management module) to provide the coordination and communication functionality described above. The system management module may also be referred to as a system controller module and / or the system management module may include a system controller module.

[0139] Memory 132 may include non-volatile memory and / or volatile memory. Non-volatile memory may include flash memory or solid-state memory. Memory 132 may store software instructions for implementing the operation of the RF system as described herein. Memory 132 may also store (some) other information required for the detection and signal generation of AI / ML models and objects.

[0140] Motherboard 134 may be referred to as a motherboard, a main circuit board, or other similar central processing system. Motherboard 134 may include a main printed circuit board (“PCB”). Motherboard 134 may include various communication interfaces or buses to allow communication among components of processing module 130, such as memory 132, one or more processors 136, one or more GPUs 138, one or more SDR transceivers, and one or more PNT components 140. Motherboard 134 may provide connectors for other components described herein. Motherboard 134 may include significant subsystems such as a central processing unit, chipset input / output and memory controllers, interface connectors, and other components integrated for general purpose use.

[0141] One or more processors 136 may include any type of general-purpose central processing unit (“CPU”). In various embodiments, one or more processors 136 may include more than one processor of any type, including, but not limited to, complex programmable logic devices (“CPLD”), field programmable gate arrays (“PPGA”), application-specific integrated circuits (“ASIC”), or the like.

[0142] One or more GPUs 138 may include any type of dedicated electronic circuitry capable of performing advanced computations that may be performed more slowly or less efficiently on a general-purpose processor (or may not be able to be performed on a general-purpose processor). GPUs 138 may include fast memory and highly parallel architectures for parallel processing of large data blocks. For example, GPUs 138 may be configured to perform matrix computations, linear algebra computations, Fourier transforms, and / or other advanced computations (including performing ML models as described herein). Also, for example, GPUs 138 may be configured to perform many computations per second (e.g., 10, 15, 20, 30, or more Tera FLOPS). These GPUs 138 may include their own memory and / or processors or be capable of executing instructions stored in memory 132 and / or as indicated by processor 136. Instructions may be executed by processor(s) 136 and / or GPU 138. For example, processor 136 may instruct GPU 138 to apply an ML model to sampled RF data to, for example, determine one type of object, as described herein. Alternatively or additionally, processor 136 may support calculations and other determinations.

[0143] The various states and functions of the processing module 130 can correspond to the states of the system described with reference to FIG3, and therefore the components and functions described with reference to FIG3 can be applied to the processing module 130.

[0144] SDR transceiver 139 includes circuitry and functionality for generating, modifying, detecting, sensing, or otherwise cooperating with RF signals as described herein. For example, SDR transceiver 139 can be configured to transmit / receive signals that can be mixed, filtered, amplified, modulated / demodulated, and / or detected using one or more of the components described herein. As a further example, SDR transceiver 139 can receive instructions from processor 136 to generate one or more signals to be transmitted via the RF system (e.g., calibrating an identified object). SDR transceiver 139 can then generate signals that can be subsequently passed to RF module 131 (if applicable, and containing instructions regarding the amount of power to be transmitted over any applicable antenna) for amplification and transmission via directional antenna 122.

[0145] SDR transceiver 139 may include one or more analog-to-digital (“ADC”) converters and one or more digital-to-analog (“DAC”) converters. For example, a received signal (e.g., received via a directional antenna and RF module and transmitted to a processing module) may be passed through an ADC for further digital-domain sampling and analysis, as described herein. The signal to be transmitted may be generated by the SDR transceiver and passed through a DAC before being transmitted to an RF module for amplification and then transmitted via a directional antenna. In various embodiments, the ADC and DAC may be located elsewhere in the system, for example, as separate components of the processing module and / or the RF module.

[0146] In various embodiments, the RF system (e.g., processing module 130) may include PNT capabilities. These PNT capabilities may be provided by one or more PNT components 140, which may include, for example, Global Navigation Satellite System capabilities (e.g., Global Positioning System (“GPS”) capabilities) and other PNT functions. The one or more PNT components 140 may further provide orientation information, altitude information, angle / tilt information, and / or the like. In some embodiments, the PNT capabilities 140 may be wholly or partially provided in and / or by the direction finder 120. PNT capabilities may also be referred to herein as “positioning capabilities,” and the one or more PNT components 140 may also be referred to herein as “positioning components” and / or the like. The PNT capability of an RF system can be used for, for example, object location determination and / or tracking, as described herein, because this capability can depend on the position, orientation, tilt angle and / or similar of the RF system (e.g., enabling a correctly oriented antenna 122 with the correct orientation and tilt angle to be used to detect or calibrate an object). vi. [, RF , ] [, Module , ]

[0147] Each RF module 131 may include one or more power amplifiers 126, one or more filters and / or limiters 128, and one or more multiplexers 125. As described herein, in various embodiments, each RF module 131 may communicate with a directional antenna 122. Furthermore, each RF module 131 may both receive RF signals via the associated directional antenna and initiate the transmission of RF signals via the associated directional antenna. Received signals may be relayed to a processing module 130 communicating with the RF module 131. Similarly, the processing module 130 (via SDR transceiver 139) may provide signals to the RF module 131 for transmission.

[0148] The radio frequency (“RF”) power amplifier 126 may include amplifiers for both receiving and transmitting signals. In various embodiments, the system may include multiple signal channels for receiving and transmitting signals, and thus multiple amplifiers. In various embodiments, the power amplifier 126 may drive an antenna or modify a received signal such that the output may include improved gain, power output, bandwidth, power efficiency, linearity (e.g., low signal compression at rated output), input and output impedance matching, and / or heat dissipation. In various embodiments, the power amplifier 126 may amplify signals in a radio frequency range between about 20 kHz and about 300 GHz, and / or may include preamplifiers that can be used before other signal processing stages.

[0149] One or more filters and / or limiters 128 may primarily provide signal filtering and / or limiting for received signals, but may also be used for transmitted signals, depending on the situation. Filters may include, for example, wideband, narrowband, high-pass, low-pass, notch, and / or other types of filters for RF signals. In some embodiments, filters may be configured to reduce noise in a received and / or transmitted RF signal. Limiters may include various circuit elements for limiting the power of signals, for example, received by the RF system. Generally, RF module 131 operates in the analog domain (e.g., analog signals are transmitted from the processing module / SDR transceiver to the RF module), but in some embodiments, some states may be in the digital domain (e.g., if an ADC and / or DAC are provided on the RF module, and / or if some filtering and / or limiting is performed by the RF module in the digital domain).

[0150] The RF module may also include a multiplexer to provide reception and transmission via a directional antenna. For example, one multiplexer of the RF module can switch between receiving an RF signal from a directional antenna (and providing the received signal to the processing module) and transmitting an RF signal via the directional antenna (received from the processing module). In various embodiments, at least two communication links are provided between the processing module and the RF module to achieve reception and transmission functionality. In various embodiments, the RF system can switch between receiving and transmitting signals periodically, intermittently, as needed, rapidly, and / or according to a program. In various embodiments, the RF system can simultaneously receive and transmit signals (e.g., one set of directional antennas and the RF module can be receiving, while another set of directional antennas and the RF module is transmitting). In various embodiments, the multiplexer can modulate and / or demodulate one of the received and / or transmitted signals.

[0151] In various embodiments, the RF system may include one or more FPGAs or ASICs, which may be used in place of a general-purpose processor or a dedicated digital signal processor (“DSP”) having a specific parallel architecture to accelerate operations such as filtering. In various embodiments, the RF system may include one or more additional amplifiers and / or other circuit components to provide the functionality described herein. a. [, RF , ] [, System example software components , ]

[0152] Figure 1C illustrates a block diagram of an exemplary software component 105 of an RF system 102 (and / or RF system 106) according to various embodiments of the present invention. In addition to the description below, further details of the software components and related functionality of the RF system are described below with reference to, for example, Figures 10 to 15. Software component 105 may include an RF transmission component 160, an AI / ML component 162, a digital signal filter 164, an external data system 166, a tracking component 168, a power control component 170, and a raw signal storage unit 172. In various embodiments, software component 105 is implemented in one or more hardware components 103 of the RF system 102. For example, software component 105 may be implemented by a processing module 130 (e.g., it may include software instructions stored in memory and executed by processor(s), GPU(s), SDR transceivers(s), and / or the like of the processing module 130).

[0153] In various embodiments, one or more software components 105 may include executable software instructions, modules, engines, and / or the like that can communicate with each other to share computer resources to perform various tasks such as: tracking a signal or object, identifying a signal, generating a signal, transmitting a signal, training / sharing / applying an AI / ML model, reducing / increasing power output, or the like. Each task may include one or more components of software component 105. In various embodiments, functionality may be shared by the various software components 105. In various embodiments, functionality may be shared by some software components 105 and hardware components (e.g., 103) or other devices and systems.

[0154] Generally speaking, the software component 105 of the RF system can realize object tracking, detect and / or identify one or more RF signals captured by a connected antenna and / or determine and cause a signal to be transmitted to a tracked object via one or more directional antennas. For example, the software component can implement, for example, machine learning ("ML") algorithms, artificial intelligence ("AI") algorithms, ML models, procedural algorithms and / or similar (generally referred to herein as "AI / ML algorithms", "AI / ML models", or simply "ML algorithms", "ML models" and / or similar) through a machine learning component, which can be implemented by one or more processors. Compared to a known system, enabling an AI / ML model to identify RF signals can advantageously provide significant improvements because many detected signals may contain interference of the same level, which is relatively weak and difficult to detect, or is otherwise difficult to identify due to other factors. In various embodiments, the machine learning component may apply one or more ML models or parametric functions for detection / identification. Machine learning components can be configured to apply ML models that can help detect which types of RF signals (e.g., a series of RF signals, a specific frequency or combination of frequencies and / or the like) indicate which types of objects.

[0155] AI / ML component 162 (also referred to above as the "machine learning component") can be configured to store, update, and / or apply one or more AI / ML models, programmed algorithms, and / or the like. As will be described in more detail herein, the AI / ML model implemented at the AI / ML component can be, for example, one or more models trained to identify the frequency (or related signal properties) of a received RF signal emitted from an object, the object, the category or type of the object, or other characteristics of the detected object. In some embodiments, AI / ML component 162 may also involve generating and / or training one or more AI / ML models. In various embodiments, the AI / ML component is executed by one or more of the processor or GPU of processing module 130.

[0156] One or more ML models can be used to determine a expected RF signal frequency range or additional signal properties based on the analysis of received or captured data. In various embodiments, signal monitoring criteria or signal identification criteria can be specified by a user, administrator, or automatically. For example, signal monitoring criteria or signal identification criteria can indicate which types of detection should be monitored, recorded, or analyzed. By specifying specific types of detection, resources (e.g., processing power, bandwidth, and / or the like) can be saved only for the type of detection to be performed. Various types of detection are described in more detail herein.

[0157] Several different types of AI / ML algorithms and models can be used by the RF system. Furthermore, various methods can be used to develop, program, and / or train these AI / ML models. For example, some embodiments described herein may use a logistic regression model, decision tree, random forest, convolutional neural network, deep network, etc. However, other models are feasible, such as a linear regression model, a discrete choice model, or a generalized linear model. Machine learning models can be configured to adaptively develop and update over time based on new inputs. For example, as new received data becomes available, the model can be trained, retrained, or otherwise updated on a periodic basis to help maintain more accurate predictions in the model as data is collected over time. Also, for example, the model can be trained, retrained, or otherwise updated based on configurations received from a user, administrator, or other device. Non-limiting instances of machine learning algorithms that can be used to train, retrain, or otherwise update models may include supervised and unsupervised machine learning algorithms, including regression algorithms (such as ordinary least squares regression), instance-based algorithms (such as learned vector quantization), decision tree algorithms (such as classification and regression trees), Bayesian algorithms (such as simple Bayesian), clustering algorithms (such as k-means clustering), association rule learning algorithms (such as prior algorithms), artificial neural network algorithms (such as perception), deep learning algorithms (such as deep Boltzmann machines), dimensionality reduction algorithms (such as principal component analysis), ensemble algorithms (such as stacked generalization), support vector machines, joint learning, and / or other machine learning algorithms. These machine learning algorithms may include any type of machine learning algorithm, including hierarchical clustering algorithms and cluster analysis algorithms, such as k-means algorithms. In some cases, performing a machine learning algorithm may involve using an artificial neural network. By using machine learning techniques, large amounts of received data (such as megabytes or gigabytes) can be analyzed to generate or implement models with minimal or no manual analysis or review by one or more humans. In some embodiments, algorithms can be programmed based on empirical data (e.g., in addition to or without the implementation of machine learning or artificial intelligence).

[0158] In various embodiments, one of the ML models of the RF system can be trained by: (1) sampling the raw signal (e.g., captured from one or more connected antennas), (2) annotating the signal (e.g., frequency, time, and intensity), (3) filtering the signal, and (4) training the model. The trained model can then be applied by the RF system to the received or captured RF signal for identification purposes. For example, in various embodiments, the application of the trained machine learning model may include: (1) sampling the raw signal, (2) applying the trained model, and (3) outputting categories and probabilities (e.g., associated with object type). Then, for example, a processing module may identify a type of captured RF signal and / or object based on the output of (3) category and probability. Also, in various embodiments, the application of the trained machine learning model may include (0) a preliminary step of filtering the baseline signal and / or friendly signal.

[0159] In various embodiments, sampling of the raw signal or raw signal (e.g., RF) data can include any form of data sampling. For example, data sampling can include a statistical analysis technique for selecting, manipulating, and analyzing a representative subset of data points to identify patterns and trends in a larger dataset under examination. This enables the processing of a small manageable amount of data that can represent a large, unmanageable amount of data. Sampling can advantageously enable the analysis of datasets that are too large to be fully analyzed or efficiently analyzed within a required time frame. In various embodiments, the RF system can sample the raw signal over the same time period (e.g., the same number of milliseconds, such as 1 ms, 2 ms, 3 ms, 5 ms, 10 ms, 50 ms, or the same other time period). In various embodiments, other or additional sampling methods may be employed.

[0160] In various embodiments, a machine learning model can be configured (e.g., trained automatically, manually, or in combination) to monitor a specific subset of frequencies or other subsets of wave properties. For example, while a processing module can detect and scan a set of frequencies via signals received from one or more antennas, the processing module can limit its analysis to a specific subset of frequencies. For example, this could be a result of training a machine learning model where some frequencies are unimportant or useless and are therefore ignored, freeing up processing power to analyze other frequencies. Also, for example, if there are friendly devices in the area that the transmitting system does not need to identify at certain frequencies (e.g., the devices are known / identified), the subset of frequencies can be manually configured. In various embodiments, each antenna can be operated independently by the processing module (via one or more RF modules), with one antenna monitoring a subset of frequencies and another antenna communicating with the same processing module monitoring a second subset of frequencies different from the first subset. For example, this could be implemented with one antenna facing a friendly device and the other not facing a friendly device. Furthermore, in various embodiments, the subset of frequencies can be adjusted based on a day, a week, a month, or a year. For example, during the day, a vehicle can be positioned in front of an antenna that transmits a signal at a specific frequency, and the vehicle can be moved so that it is no longer in front of the antenna at night.

[0161] In various embodiments, an RF system (e.g., via one or more processing modules and / or one or more RF modules) may use an identified type of object (e.g., from the output of an applied machine learning model) to generate one or more new signals and transmit the new signals using one or more directional antennas. The new signals may be transmitted in the direction of the identified signal or one or more objects. Therefore, the RF system may selectively transmit signals with different powers via antennas in various directions. In various embodiments, the identified signal corresponds to one or more moving objects (e.g., vehicles, boats, aircraft, drones, and / or the like), and the transmitted signal may affect communication in the vicinity of the moving object during transmission. In various embodiments, the detection or identification of an object or the identification of a signal corresponding to the object may be received from one or more other systems or sensors. Generating a signal based on the identified signal may be advantageous due to increased power efficiency / optimization. For example, instead of transmitting signals across the entire frequency band, signals may be transmitted only at a specific frequency or within a narrow frequency range, thereby increasing power efficiency and / or signal power to reach greater distances. In various embodiments, the transmitted signal can be further filtered to limit interference with sensitive and friendly systems in the area.

[0162] RF transmission component 160 can be configured to cause the transmission of RF signals and / or generate RF signals to be transmitted, as described above and herein. For example, RF transmission component 160 can communicate with and be configured to cause one or more antennas (e.g., the directional antennas 122 of FIG. 1B) or other transmission devices to transmit RF signals. In various embodiments, RF transmission component 160 may include at least a portion of an SDR transceiver and / or provide instructions to the SDR transceiver, including software configured to selectively cause signal transmission at a desired frequency, strength, and / or direction. RF transmission component 160 can be further configured to receive tracking data from tracking component 168, such as to transmit RF signals to an object tracked by the system.

[0163] Digital signal filtering component 164 can be configured to filter RF signal data, for example, before the raw RF signal data collected by the system is provided to AI / ML component 162 for signal analysis based on a trained AI / ML model. For example, in various embodiments, the RF system can remove RF signals from the collected raw RF signal data that correspond to RF signals associated with friendly devices (devices that have been manually or automatically marked as friendly), or filter the raw RF signal data based on a whitelist and / or blacklist (e.g., manually and / or automatically filled in). Digital signal filtering component 164 can also be configured to filter raw RF signal data transmitted or emitted by the system. For example, in various embodiments, the RF system can remove RF signal frequencies from any RF signal generated (e.g., transmitted by RF transmission component 160 or one or more antennas) that correspond to RF signals associated with friendly devices (devices that have been manually or automatically marked as friendly), or based on a whitelist and / or blacklist (e.g., manually and / or automatically filled in). Since digital signal filtering is primarily performed in the digital domain, the received signal is typically converted to digital before filtering, and the generated signal is typically converted to analog after filtering. In some implementations, the filtering described above may be performed partially or entirely in the analog domain. In various implementations, digital filtering is performed by one or more of the processor or SDR transceiver of processing module 130.

[0164] Tracking component 168 can be configured to track a detected object based on an identified RF transmission received from the detected object. Further details of the operation of tracking component 168 are described in more detail, for example, with reference to Figure 13 and elsewhere herein. In various embodiments, tracking component 168 can track an identified signal or object (e.g., when the RF system is transmitting or not). In various embodiments, a direction finder can also provide more accurate tracking. For example, a direction finder combined with one or more antennas can identify (e.g., when the antennas are transmitting or not) one direction from which a detected signal is emitted.

[0165] In various embodiments, there may be other sensors or systems (e.g., and including other RF systems in the area) that can be connected to the RF system to provide additional data, which can be used to: (1) improve detection performed by the RF system using a machine learning model; (2) assist the RF system in continuing to track, or initiating tracking of an object or signal; and / or (3) generate and transmit, or continue to generate and transmit, a specific signal in the direction of an object, and other functions. For example, if an object or signal moves from the range of an antenna connected to a first RF system (e.g., RF system 102) to the range of another antenna connected to a second RF system (e.g., RF system 106), the two RF systems can communicate and hand over tasks performed by the first RF system (e.g., identification, tracking, transmission, and / or the like) so that they can continue to be performed by the second RF system. In various embodiments, during the transition, the first RF system may be turned off and the second RF system may be turned on. In various embodiments, the first RF system and the second RF system may continue to perform the same task for a period of time (e.g., 5 seconds, 1 minute, 10 minutes and / or the like) or at least until the task is completed and both RF systems stop. In various embodiments, the first RF system may reduce the power used to perform the task as the second RF system increases power in conjunction (e.g., there may be a set of throttle power usage limits on the total amount of power used by one or both RF systems at one time).

[0166] External data system component 166 may store or retrieve any desired data from external data storage devices, sensors, or systems (e.g., additional system or sensor 104, central processing server 107, and / or the like) for implementation of systems and methods in conjunction with this technology. For example, AI / ML models, data associated with known objects or object categories, and / or RF signal characteristics associated with known objects or object categories, data defining signal content to be transmitted to detected and / or tracked objects, and the like may be stored and / or retrieved via external data system 166. In various embodiments, for example, external data system component 166 or associated external data sources or devices may include databases connected to one or more of a user device (e.g., 110), a central processing server (e.g., 107), one or more RF systems (e.g., 102 or 106), or additional systems or sensors (e.g., 104). In various embodiments, the data described above may be similarly stored in one of the memory units of the processing module, as described herein.

[0167] The power control unit 170 can be configured for software control of power supplied to any of the various components of the system, such as any of the hardware components 103 (e.g., FIG. 1B). For example, the power control unit 170 can control selective RF transmission from antenna 122 based at least in part on the power supplied to antenna 122 for transmission (e.g., via the RF module). Further information about the power control unit is provided elsewhere herein, and an example is described with reference to FIG. 15.

[0168] The raw signal storage 172 may store raw RF signals received by an antenna or complex data associated with raw RF signals. In various embodiments, for example, the raw signal storage 172 may store raw data corresponding to an analog-to-digital conversion of an RF signal received at an antenna, spectrogram data corresponding to the raw data, RF signals to be transmitted based on any received RF signal (e.g., as determined by an AI / ML component), or any other type of data structure indicating a raw RF signal received or transmitted by one or more antennas.

[0169] In various embodiments, each RF system may also include software components for performing over-the-air (“OTA”) (or via electrical hardwired connection) updates of various software, components, machine learning models or components and / or the like. For example, an RF system may be connected to a central processing server 107 to receive updates. As another example, if multiple RF systems are installed in an area, it may be beneficial for the RF systems to connect over time and update each other’s machine learning models (e.g., by sending updated models or capturing relevant data so that each RF system can be trained on additional data) so that each RF system has the latest available data or model. In various embodiments, although the RF systems may be in the same area, it may be beneficial to share only a portion of the data or machine learning models between the RF systems, because there may be subtle differences between the field of view of each RF system that may cause one model to be more suitable for a first environment / area than another model that is more suitable for a second environment / area. V. [, RF , ] [, Indicative implementation scheme of the system , ] [ ]

[0170] Figures 2A and 2B illustrate exemplary embodiments and orientations of one or more RF systems (e.g., RF systems 102 and / or 106) in operation according to various embodiments of the present invention.

[0171] Figure 2A illustrates an exemplary embodiment and orientation 200 of a plurality of RF systems (e.g., RF systems 102 and / or 106). In Figure 2A, the plurality of RF systems 204, 206, 208, and 210 may be positioned to surround a building or area 202, such that the corresponding antennas connected to the RF systems can be oriented away from a location that may include sensitive equipment or otherwise designated as an area to be monitored by the RF systems. Although Figure 2A shows one configuration, an infinite number of configurations can be envisioned for each point in which the RF systems are deployed / installed. For example, the placement of the RF systems, the hardware components (e.g., 103) or software components (e.g., 105) of each RF system, the type of data shared between the RF systems, the area or building to be excluded from the antenna's field of view, and other criteria may vary for each point in which these RF systems are to be deployed.

[0172] Additionally, in Figure 2A, RF systems (e.g., 204, 206, 208, and 210) are shown as having one or two directional broadband antennas corresponding to each RF system. For example, RF systems 204 and 208 are shown mounted on the roof of building 202, adjacent to building 202, or on one of the side walls of building 202. RF systems 204 and 208 each also correspond to two antennas, each facing a specific direction with a 90° field of view. For example, RF system 204 has one antenna facing D1 and one second antenna facing D2, both antennas having a 90° field of view. Similarly, for example, RF system 208 has one antenna facing D5 and one second antenna facing D6, both antennas having a 90° field of view. RF systems 206 and 210 each also correspond to two antennas, each antenna facing a specific direction with a 90° field of view. For example, RF system 206 has one antenna facing D3, and RF system 210 has one antenna facing D4, both antennas having a 90° field of view. In various embodiments, for example, RF system 204 may have one antenna with a 180° field of view, or three antennas with a 60° field of view, or other similar combinations to have the same overall field of view achieved by the two antennas shown. In various embodiments, RF systems (e.g., 204, 206, 208, and 210) may include any number (e.g., 1, 2, 3, 4, 5, 6, and / or similar) of antennas, and antennas facing (some) specific directions may be turned on or off based on software commands, orientation relative to sensitive devices, orientation relative to other RF systems, customization preferences (e.g., based on terrain or surrounding area), or nearby detected objects or the like.

[0173] Advantageously, the RF system and its corresponding antennas are configured such that building 202 and area 201 (e.g., which may be another building, temporary building, stationary vehicle or other friendly or sensitive equipment or the like) are located outside the field of view of the antennas. In various embodiments, for example, data can be transmitted between the RF systems to enable coordinated detection (e.g., training or application of a machine learning model), tracking and / or transmission.

[0174] Figure 2B illustrates an exemplary embodiment of an RF system interacting with an object 254 according to various embodiments of the present invention, and orientation 250. In Figure 2B, an RF system 252 may be placed in a location (e.g., near or on a building or area). In various embodiments, multiple directional broadband antennas may be used to cover a surrounding area. For example, Figure 2B shows an RF system 252 comprising at least four antennas facing directions D1, D2, D3, and D4. Although Figure 2B shows one configuration, an infinite number of configurations are conceivable, in which an RF system may comprise any number (e.g., 1, 2, 3, 4, 5, 6, and / or similar) of antennas, and antennas facing (some) specific directions may be turned on or off based on software commands, orientation relative to a sensitive device, orientation relative to other RF systems, customization preferences (e.g., based on terrain or surrounding area), or nearby detected objects or the like. The antennas are also shown to have at least a 90° field of view. In various embodiments, for example, RF system 252 may have eight antennas with a 45° field of view, or eighty antennas with a 4.5° field of view, or other similar combinations to achieve the same overall field of view as the four antennas shown. Furthermore, for example, the placement of RF system 252, the hardware components (e.g., 103) or software components (e.g., 105) of RF system 252, the type of data shared between RF system 252 and other RF systems or devices / sensors (e.g., 104), areas or buildings to be omitted from the field of view of the antennas, and other criteria may vary for RF system 252.

[0175] Furthermore, in Figure 2B, object 254 is shown moving freely in direction D5 toward an area covered by a first antenna in direction D1 toward an area covered by a second antenna in direction D2. In various embodiments, when object 254 is in an area covered by the first antenna, power can be supplied to the first antenna to improve the performance of the first antenna associated with receiving / transmitting RF signals in direction D1. As object 254 moves along direction D2 into an area covered by the second antenna, power can be diverted from the first antenna to the second antenna so that RF system 252 can continue to effectively receive / transmit RF signals associated with object 254. In various embodiments, power can be sloping down for the first antenna (e.g., 2 / 3 of the power signal displayed in direction D1 corresponding to the first antenna) and simultaneously sloping up for the second antenna (e.g., 1 / 3 of the power signal displayed in direction D2 corresponding to the second antenna). In various embodiments, power can be binary, and the first antenna can be turned off when the second antenna is on. In various embodiments, the power of each antenna can be controlled by one or more processing modules of an RF system (or one or more RF systems) (such as the RF systems described herein) based on the movement of an identified / tracked object (e.g., 254), thereby optimizing performance (e.g., based on the speed of the identified / tracked object, the distance of the identified / tracked object relative to the RF system performing the tracking, the nature of the tracked signal (e.g., strength, wavelength, frequency, or the like) or the like). Additionally, in the example shown in FIG2B, a third antenna facing direction D3 and a fourth antenna facing direction D4 are shown as deactivated or deactivated because object 254 is not in the area covered by the third or fourth antenna. In various embodiments, all antennas can also be turned on or off simultaneously.

[0176] In another exemplary configuration not shown in the figure, a first RF system may be placed at the northeast corner of a building, with one antenna facing north and the other facing east. A second RF system may be placed at the southwest corner of the same building, with one antenna facing south and the other facing west. Thus, the four antennas connected to the two RF systems (and / or additional RF systems and associated antennas) can monitor a 360° area (or approximately a 360° area) around the building, while simultaneously eliminating any signal detection from the building itself. Due to the orientation, when any antenna is transmitting a signal, the transmission is directed away from the building, ensuring that the building and any equipment or personnel within it are not affected by the transmission. The orientation and signal filtering described herein can be further refined to limit interference to friendly areas and equipment.

[0177] Advantageously, for the modular and configurable RF system of the present invention, an infinite number of other configurations of one or more RF systems and one or more directional antennas of each RF system (other than the examples provided above) are feasible. VI. [, Additional exemplary hardware-related features and functionalities , ] [ ]

[0178] The following descriptions of Figures 4A to 4D, 5A to 5E, 6, 7A to 7B, 8, and 9A to 9D provide further details regarding the implementation schemes, components, and related functionality of the RF system. Although different numbers may be used to describe various forms of the RF system compared to the foregoing descriptions, it should be understood that similar forms and components may contain similar or identical functionality. Therefore, the forms described above are applicable to the forms described below, and vice versa.

[0179] Figure 4A illustrates a perspective view of an exemplary embodiment of an RF system including a module housing according to various embodiments of the present invention. The illustrated embodiment includes an RF system 402 (in an embodiment having a single module housing, which may correspond to the RF system 102 described above), the RF system 402 including an upper housing 410, a module housing 412, a lower housing 414, directional antennas 406a to 406b, and a direction finder 408.

[0180] The upper housing 410 may be positioned above the module housing 412. Alternatively, the lower housing 414 may be positioned below the module housing 412. As shown, the upper housing 410 and the lower housing 414 are adjacent to the module housing 412. Furthermore, the surfaces of the upper housing 410 and the lower housing 414 are shown to be flush with the corresponding surfaces of the module housing 412. The upper housing 410, the module housing 412, and the lower housing 414 together may form a "modular assembly" or a main housing of an RF system.

[0181] The upper housing 410 may include one or more vents 416. Vents 416 may enable or facilitate airflow within a modular assembly (such as an internal portion or cavity of the RF system 402). This airflow may improve cooling of one or more portions of the RF system 402 within the modular assembly, as described below. The internal portion may be sealed from an external portion or cavity within the RF system 402 (e.g., a liquid seal, a fluid seal). The external portion may be referred to herein as a peripheral portion of the RF system 402 because the external portion may surround or include a periphery of an internal portion of the RF system. For example, the periphery / external portion may house a system module and surround an internal portion including, for example, a heat sink. A seal may include an hermetically sealed seal. The seal facilitates the inflow and outflow of more efficient air into and out of the vents 416 and / or vents 430 (shown in Figure 4B). For example, the external portion sealed from the internal portion may include, for example, components and regions 436, 438a to 438d and 428 (described below with reference to Figure 4B). The seal may comprise a metal, plastic, or other impermeable or otherwise resistant material secured by a mechanism that facilitates the securing of one or more sealed compartments (e.g., screws, welds, caps, pins, or the like with rubber O-rings). The seal may comprise an airtight seal that airtightly seals the system modules and other internal components of the RF system from the external environment. In some embodiments, a filter may be positioned anterior to vents 416 and / or 430 to restrict debris from entering the internal portion (e.g., where components 440a to 440d, 424, 246, 432, 434, and / or 442 are located), which may be referred to herein as a channel. For example, the filter may restrict or prevent dust or debris from entering the internal portion and damaging the heatsink fins. Alternatively or additionally, the seal may reduce the accumulation of sand or other debris in the portion outside the module housing 412 where critical system modules 438a to 438d may be housed.

[0182] As shown, vents 416 and 430 are respectively formed (e.g., modeled) as parts of upper and lower housings 410 and 414. However, in some instances, the vents are coupled to the upper / lower housings. The upper / lower housings may include coupling elements (e.g., screws, pins, snaps, adhesives, and / or the like) that couple the upper / lower housings to the module housing 412. The coupling elements can be configured for manual adjustment, such as wing nuts, wing screws, and the like.

[0183] The module housing 412 may be a shell configured to enclose or house one or more of the hardware components described herein or other hardware components that may benefit from said configuration. The module housing 412 may be configured to protect the internal components of the RF system 402 from harsh weather conditions, environmental hazards, wildlife interference, electromagnetic interference (“EMI”), and the like. The module housing 412 may typically be symmetrical about one or more axes. For example, the module housing 412 may exhibit substantial reflective and / or rotational symmetry about an axis (such as a generally vertical axis) parallel to one or more of the main surfaces of the directional antennas 406a to 406b. The module housing 412 may typically include a rectangular prism (e.g., shown in FIG. 4A), a triangular prism, a cylinder, or a portion of the same other regular polyhedron shape. The module housing 412 may include one or more regular and / or irregular shapes.

[0184] The upper housing 410, module housing 412, and lower housing 414 constituting the main enclosure of the RF system are typically made of rigid or robust materials such as metal. The main housing is typically made of aluminum, but may also be made of other materials such as plastic or rubber. The main housing may be made of a single material or may include a coating to generally protect internal components from EMI, weather, and / or other adverse conditions.

[0185] Each of the directional antennas 406a to 406b may include a generally elongated and / or flat shape. For example, directional antennas 406a to 406b may have main surfaces opposite each other and each having a generally rectangular shape. Other shapes of the main surfaces are possible, such as triangles, pentagons, other polygons, circles, ovals, or irregular shapes that may include combinations of two or more shapes. The edges of each of the directional antennas 406a to 406b may be joined to adjacent or neighboring edges at a point or along a smooth (e.g., curved) connection. As shown in Figure 4A, for example, the connection may be curved. In some embodiments, directional antennas 406a to 406b have main surfaces shaped like organic objects (such as a fish fin and / or bird wing).

[0186] The main surfaces of directional antennas 406a to 406b may be covered with a protective coating and / or covering. This protection helps to protect the directional antennas 406a to 406b from outdoor elements such as the sun, adverse weather conditions, wildlife, and the like. The coating and / or covering may be configured to facilitate proper and / or enhanced reception and / or transmission of RF signals, or otherwise limit or reduce interference with the reception and / or transmission of RF signals. For example, a covering may comprise a plastic covering, a rubber covering, a fiberglass covering, or the like. Alternatively or additionally, the protective coating and / or covering may help to shield the directional antennas 406a to 406b from detection by human or animal senses or even automated sensing technologies.

[0187] Directional antennas 406a to 406b can be configured for rapid transport and deployment. For example, directional antennas 406a to 406b can be sized so that an ordinary person can lift and transport them. Alternatively, directional antennas 406a to 406b can be configured for assembly without additional tools. Each of the directional antennas 406a to 406b can be coupled and / or decoupled from the module housing 412 (shown in Figure 4A) or the same other parts of the RF system 402. For example, each of the directional antennas 406a to 406b can be coupled to the RF system 402 via a snap-fit, a friction fit, a threaded fit, an adhesive, a sliding mechanism (e.g., using gravity and / or a corresponding physical structure to hold the directional antenna in place) and / or a press fit.

[0188] As shown, each of the directional antennas 406a to 406b is coupled to the RF system via its respective antenna mount 420a to 420b. The antenna mount 420a to 420b may include one or more coupling points between each of the directional antennas 406a to 406b and the remainder of the RF system 402 (e.g., module housing 412). Each of the coupling points may have one or more degrees of freedom. Each degree of freedom may reflect the ability of the respective directional antenna 406a to 406b to rotate about one or more axes or translate along one or more axes. For example, in some embodiments, each coupling point may have up to six degrees of freedom. As shown, the antenna mount 420a to 420b includes a single coupling point and two degrees of freedom: a first degree of freedom associated with angular rotation and a second degree of freedom associated with axial translation. Axial translation allows the respective directional antennas 406a to 406b to extend away from the module housing 412 and / or move closer to the module housing 412. Other configurations are feasible; some other configurations are described below with reference to Figure 5E.

[0189] Each of the directional antennas 406a to 406b may be associated with a respective range of motion (such as an angular range of motion relative to a preset position of each of the respective directional antennas 406a to 406b). For example, each of the directional antennas 406a to 406b may have a range of motion of about 0 degrees, about 2 degrees, about 4 degrees, about 5 degrees, about 8 degrees, about 10 degrees, about 12 degrees, about 15 degrees, about 20 degrees, about 30 degrees, about 35 degrees, about 40 degrees, about 45 degrees, about 50 degrees, or about 60 degrees, any angular value therein, or falling within any range having an endpoint therein. For example, in some embodiments, an angular range of each of the directional antennas 406a to 406b is about 0 degrees to about 30 degrees. Other ranges of motion (or their range) are possible. The measured angle may include an elevation or tilt angle relative to a plane on which the RF system is located (or, if the RF system is horizontally mounted, a line perpendicular to or normal to a side surface of the RF system on which the antenna is mounted).

[0190] RF system 402 may additionally or alternatively include one or more direction finders 408. The direction finder 408 may be mounted on a top surface of RF system 402, such as the top surface of upper housing 410 and / or vent 416. In other embodiments, the direction finder 408 may be mounted elsewhere, such as extending from a module housing 412 or a bottom surface of RF system 402. The direction finder 408 may include a directional antenna and / or a receiver. The direction finder 408 may be configured to point in one or more directions (e.g., including 360° around RF system 402). When in a particular direction, the direction finder 408 can identify the strength of a received RF signal. In some embodiments, only one value of the signal is used to determine the direction of an RF transmitter. Alternatively or alternatively, the direction finder 408 may be able to automatically determine the direction of an RF transmitter using other variables, such as a change in signal strength. The data collected by the direction finder 408 can be used alone or in conjunction with data collected by one or more directional antennas (e.g., directional antennas 406a to 406d) to determine the location of one of the detected signals emitted from them.

[0191] The direction finder 408 may include a motor configured to automatically adjust the orientation of the direction finder 408. The direction finder 408 may be able to use knowledge of the radiation pattern of the direction finder 408 to improve its accuracy, such as by using a trained machine learning model as described herein.

[0192] The direction finder 408 may include a Doppler system coupled to a holographic antenna configured to rotate along a circumference of a circle. In these embodiments, as the direction finder 408 moves toward the RF source, the Doppler frequency shift increases the received frequency, but as the direction finder 408 moves away from the RF source, the received frequency decreases. The frequency change can be used to determine the direction of the RF source. The frequency change can be calculated by demodulating the RF signal (e.g., frequency modulation ("FM") demodulation).

[0193] In some embodiments, a plurality of antennas may be used in a direction finder 408 and may be used along an array pattern (e.g., in a circle) on the direction finder 408. Each of the plurality of antennas may be sampled in a pattern (e.g., continuously around a circle).

[0194] The direction finder 408 can use a single-pulse or sum-difference technique. Multiple antennas of the direction finder 408 can be connected to generate a sum-difference signal along a target angular range (e.g., 180°, 270°, 360°, and / or similar). The RF system 402 can calculate a ratio of the sum and difference signals based on the sum and difference pattern. Based on this information, the RF system 402 can determine the direction of the RF transmitter. Alternatively, the direction finder 408 can identify phase information to determine one side of the sum pattern associated with the RF transmitter. This method has the advantage of allowing the direction finder 408 to determine the direction of a transmitter after receiving a single pulse (whose duration may be only a few milliseconds).

[0195] The direction finder 408 may include a holographic antenna. In some embodiments, the holographic antenna may include two or more interlocking loop antennas. A holographic antenna array may be used to form an array (e.g., an Adcock array) to more accurately and / or quickly identify an RF source. Other configurations are possible.

[0196] RF system 402 may include one or more mounting surfaces 448. As shown, mounting surface 448 is attached to one of the bottom surfaces of lower housing 414, but one mounting surface 448 may be located elsewhere. Mounting surface 448 may be configured to be mounted to another modular assembly and / or to a mounting component (such as a tripod or other mounting system). Mounting surface 448 may be on one of the bottom sides of a vent 430 (shown in FIG. 4B). In some cases, mounting surface 448 may include an area surrounding the vent 430 on lower housing 414.

[0197] RF system 402 may include other features, such as a control panel 422. Control panel 422 may include one or more buttons, levers, and / or interface elements that allow a user to view and / or modify details related to RF system 402. For example, control panel 422 may include an indicator indicating a state of RF system 402 (e.g., on / off, active / inactive, transmitting / receiving, and / or similar). In some embodiments, control panel 422 may include a touchscreen interface, such as a graphical user interface. A user may be able to modify a state of RF system 402 using the touchscreen. In some embodiments, control panel 422 may include options for turning RF system 402 on or off. In some embodiments, control panel 422 may include options for activating one of the "search modes" described herein and other dedicated or customized operating modes. For example, it may be possible to deactivate or activate one or more directional antennas (e.g., directional antennas 406a to 406d).

[0198] The RF system 402 may have an overall height (e.g., length) between about 20 cm and about 180 cm. The RF system 402 may have an overall weight between about 10 kg and about 75 kg.

[0199] Figure 4B illustrates a cross-section of a vertical plane along a perspective view of an exemplary embodiment of the RF system of Figure 4A. The RF system 402 may include an upper housing vent cavity 424 within a vent 416, one or more upper housing cavities 428, one or more lower housing cavities 436, and a lower housing vent cavity 432 within a vent 430. One or more of the upper housing cavities 428 and / or lower housing cavities 436 may typically form a loop around a center or inner portion of the RF system 402 (e.g., module housing 412). The inner portion may typically include portions of the RF system 402 disposed within a plurality of internal structures, such as walls or even thermal interfaces 444a to 444d. In some embodiments, the upper housing cavities 428 and / or lower housing cavities 436 may be part of the inner portion of the RF system 402 and may help provide additional space for airflow so that system modules 438a to 438d can be cooled. For example, in this configuration, the surfaces of system modules 438a to 438d adjacent to the upper housing cavity 428 and / or the lower housing cavity 436 can be sealed.

[0200] RF system 402 may accommodate one or more cooling fans, such as an upper housing fan 426 and / or a lower housing fan 434. The upper housing fan 426 may typically be located within the upper housing 410 and / or the upper housing vent 424. The upper housing fan 426 may be located near the vent 416 to facilitate the flow of heated air out of the interior of RF system 402 through the vent 416. Alternatively, the lower housing fan 434 may typically be located within the lower housing vent 432 and / or the lower housing cavity 436. The lower housing fan 434 may be located near the vent 430 to facilitate the flow of heated air out of the vent 430. The lower housing fan 434 may be configured to draw air into the vent 430 and push the air upwards through the internal heat sinks 440a to 440d and out of the vent 416. The upper housing fan 426 may be configured to draw air in in the same direction as the lower housing fan 434. Alternatively, the upper housing fan 426 and the lower housing fan 434 may be configured to rotate in the same direction. Drawing air into the interior and upwards into the interior is advantageous because it works in harmony with a natural flow of air that is warmer than the surrounding air. Additionally, this configuration allows air to exit through the vent 416, which also helps reduce the amount of sand or other debris entering the interior and / or exterior of the RF system 402. In some embodiments, creating a positive pressure system (e.g., both fans blowing air into the interior of the RF system 402) is advantageous. In some embodiments, creating a negative pressure system (e.g., both fans blowing air out of the interior of the RF system 402) is advantageous.

[0201] One or more system modules 438a to 438d may be accommodated within at least a portion of the module housing 412. As shown in FIG4B, the system modules 438a to 438d are housed within an outer portion of the interior of the module housing 412. The outer portion typically surrounds the interior portion of the RF system 402. The outer portion may include wiring or other data / power connections that couple one or more system modules 438a to 438d to each other, as described herein (e.g., in FIG6). To enable wiring or other data / power connections, the upper housing cavity 428, the lower housing cavity 436, and the module housing cavity 446 may include openings through which wires can extend and / or position printed circuit boards (“PCBs”) to connect various modules, fans, control panels, and external connections (e.g., connections to antennas, direction finders, external power supplies, and / or the like). External connections may include various ports and associated connectors for data and / or electrical communication between components outside the module housing (e.g., antennas, direction finders, external power supplies, and / or the like) and components inside the module housing (e.g., various modules). Ports may allow power cables and / or wires to be inserted and may be provided with a waterproof or other weather-resistant seal. System modules 438a to 438d may include an RF module 438a, an SOM module 438b, an RF module 438c, and a power supply module 438d. Other modules are possible. The descriptions of specific modules and configurations of the RF system referenced below are to be understood as similarly applicable to other embodiments of such modules and configurations.

[0202] For example, RF module 438a may include an amplifier and / or a multiplexer. RF module 438a may communicate with and / or otherwise couple to a corresponding directional antenna 406a (not shown in Figure 4B). RF module 438a may include a multi-channel (e.g., four-channel) power amplifier configured to amplify the RF signal output from directional antenna 406a. The power amplifier can operate at frequencies between approximately 70 MHz and approximately 6 GHz. RF module 438a may be configured to output at least approximately 20 W of radio signal per channel. The multiplexer can switch between a transmit mode and a receive mode. Alternatively or additionally, the multiplexer may modulate the transmitted signal according to the amplitude and / or frequency of a target signal. Multiplexing can be performed in combination with one or more other components of RF system 402, such as SOM module 438b (e.g., see the discussion of SOM module 538b in Figure 6) to perform multiplexing. The multiplexer may include a receive channel switching matrix that allows RF module 438a to multiplex associated with one of the current functions of directional antenna 406a. RF module 438c may include one or more of the features described above. RF module 438c may be coupled to directional antenna 406b in one or more of the ways described above regarding how RF module 438a is coupled to directional antenna 406a.

[0203] As mentioned above, an RF system may include one or more processing modules. The processing module may include a system-on-module (“SOM”) configuration and is therefore referred to herein as an “SOM module”. The SOM module 438b may include the integration of digital and analog functions on a single processing board. The SOM module 438b may include a processor, memory, computer-executable code, and / or other components configured to perform some of the functions described herein. In some embodiments, the SOM module 438b may include a trained machine learning model trained to identify target RF signals that may originate from a source of interest. Alternatively or additionally, a trained machine learning model may be stored in one or more other components described herein. The SOM module 438b may include a software-defined radio (“SDR”) transceiver configured to perform one or more functions (e.g., signal mixing, signal filtering, signal amplification, signal modulation and / or demodulation, signal detection, and / or the like) traditionally performed by different types of hardware. SOM module 438b can receive one or more attributes of an RF signal from one or more elements of RF system 402 (e.g., from directional antenna 406a and / or directional antenna 406b) and determine a source direction (e.g., direction finder 408, directional antenna, and / or via communication with other states of operating environment 100 as described above), a source amplitude, a source frequency, a source identifier, and / or another state of the source. Based on one or more of the source direction, source amplitude, source frequency, and / or source identifier, SOM module 438b uses an ML model to determine a target type and determines one or more signals to be transmitted to the target object. In addition, SOM module 438b can transmit instructions and / or data associated with one of these states to another element of RF system 402, an element of a different RF system 402, and / or a remote computing device (e.g., a remote server). Therefore, the SOM module 438b can help identify a source and / or transmit information based on that identification. In some embodiments, the SOM module 438b can modify the direction, amplitude, frequency, and / or other properties of an RF signal transmitted by directional antennas 406a to 406b. In some embodiments, the RF system 402 can modify the direction of a transmitted RF signal based on identified properties of a received RF signal. The directional antennas 406a to 406b can be configured to receive signals with a relatively high bandwidth and / or angular arc to identify a source signal. Alternatively or additionally, the directional antennas 406a to 406b can be configured to transmit RF signals with a relatively narrow or more precise bandwidth and / or angular arc to interfere with or disrupt a target source signal or hardware transmitting a source signal. In some embodiments, the primary receiving and / or transmitting angular arc of one of the directional antennas can be between approximately 80° (80 degrees) and approximately 110° (110 degrees), but other arc systems are possible.

[0204] Power supply module 438d can provide sufficient power to perform their respective functions to one or more of other system modules 438a to 438c and / or other components of RF system 402. Power supply module 438d may include and / or be coupled to a power source (e.g., a battery, grid, generator). Power supply module 438d may be coupled to an external and / or internal power source (e.g., in some embodiments, the RF system includes an internal battery power source that supplies power to the components of the RF system via power supply module(s)). Power supply module 438d can convert power from one character to another. For example, power supply module 438d can convert AC power to DC power and can output DC power in multiple voltages and amperes as needed by the various components of RF system 402. Alternatively or additionally, power supply module 438d can output at least 1200 W of power at a voltage between about 16 and 50 V. Alternatively or additionally, power supply module 438d can output at least 25 A of power. The power supply module 438d can be configured to transmit data to and from one or more of the other system modules 438a to 438c. In some embodiments, the power supply module 438d can be replaced by another system module and the RF system 402 can be connected to a power source or to another power source (e.g., another RF system) via a wire.

[0205] System modules 438a to 438d and power / data communication may be at least partially or completely housed within the portion outside the RF system 402. Since each of the system modules 438a to 438d may generate heat that may need to be released into the atmosphere, this additional portion is configured to allow airflow through the portion inside the RF system 402. Inside the portion, the RF system 402 may include components configured to allow heat from the system modules 438a to 438d to be transferred from the system modules 438a to 438d to one or more respective heat sinks 440a to 440d. Each system module may include a respective housing made of a thermally conductive material (such as metal). One or more of the heat sinks 440a to 440d may be at least partially housed within the portion inside the module housing 412. The heat sinks 440a to 440d may be thermally coupled to (e.g., adjacent to) corresponding thermal interfaces 444a to 444d. Each of the thermal interfaces 444a to 444d may be coupled to one or more corresponding system modules 438a to 438d. For example, thermal interface 444a may be thermally coupled to RF module 438a, and thermal interface 444b may be thermally coupled to SOM module 438b. The thermal interfaces may be made of a thermally conductive material (such as metal). Each of the heat sinks 440a to 440d may be shaped to increase thermal radiation from it and / or allow increased airflow through it to promote heat transfer away from any corresponding component via convection, conduction, and / or radiation. For example, one or more of the heat sinks 440a to 440d may include a snap-on fin shape. The snap-on fin shape may include a plurality of peaks and valleys that can provide high heat transfer while providing high structural integrity. Heat sinks 440a to 440d may include, for example, a plurality of metal (e.g., copper, aluminum, iron, and / or similar) fins. In some embodiments, each fin is electroplated in a corrosion-resistant layer (such as a metal (e.g., nickel plating)). Upper housing fan 426 and / or lower housing fan 434 may help facilitate airflow through the central portion of the RF system 402 to improve heat transfer away from system modules 438a to 438d. In some embodiments, the RF system 402 includes a plug 442 that may facilitate airflow through the heat sinks 440a to 440d, for example, by directing airflow through a structure of the heat sinks 440a to 440d rather than through a gap between the four heat sinks 440a to 440d. The plug 442 may be mounted on a shaft of the RF system 402. In some embodiments, the system module may include additional thermal management features for directing heat to a surface of a contact thermal interface 444a to 444d. The system module may also include a heat paste or material placed between the thermal interfaces 444a to 444d and the adjacent surfaces of the corresponding system module to improve heat transfer efficiency.

[0206] Figure 4C shows a side view of the RF system 402 shown in Figure 4A. Figure 4D shows a cross-section of the top view of the RF system 402 in Figure 4A along section 4D shown in Figure 4C. As shown in Figure 4D, the RF system 402 may include one or more module housing cavities 446. The module housing cavity 446 may contain communication links (e.g., wiring) and / or other elements described herein.

[0207] As shown in Figure 4D, one or more of the heat sinks 440a to 440d (e.g., heat sink 440a and heat sink 440c) may have heating elements (e.g., fins) longer than those of the other heat sinks 440a to 440d (e.g., heat sink 440b and heat sink 440d) (or otherwise have a larger surface area). Heating elements with a larger surface area can facilitate improved heat transfer and / or heat dissipation. Therefore, system modules 438a to 438d (such as RF modules 438a and RF modules 438c) can generate a higher amount of heat and thus couple to corresponding heat sinks (heat sink 440a and heat sink 440c) having a larger surface area than the other combined heat sinks.

[0208] Figure 5A illustrates a perspective view of an exemplary embodiment of an RF system comprising two modular housings according to various embodiments of the present invention. The illustrated embodiment includes an RF system 502 (in an embodiment having a dual modular housing, which may correspond to the RF system 102 described above), comprising an upper housing 410, a module housing 412, a connecting housing 504, a second module housing 512, a lower housing 414, directional antennas 406a to 406b, and a direction finder 408. The module housing 512 may include one or more features of the module housing 412. The module housing 512 may be disposed between the connecting housing 504 and the lower housing 414. Alternatively or additionally, the module housing 412 may be disposed between the upper housing 410 and the connecting housing 504. The RF system 502 may represent a dual or dual modular assembly. In some embodiments, other RF systems 502 may include triple, quadruple, or higher-order modular assemblies. Higher-order modular assemblies may include additional module housings and connecting housings between adjacent or consecutive module housings. Higher-order modular assemblies may also include additional directional antennas, if applicable. As mentioned above, RF system 502 can be configured for manual assembly. For example, a user may be able to convert RF system 402 (e.g., a single-modular assembly) into RF system 502 (e.g., a dual-modular assembly) without machines or certain tools (e.g., uncommon tools).

[0209] The housing 504 may include coupling elements (e.g., screws, pins, snaps, adhesives, and / or the like) that couple the modular housing to the housing. The coupling elements may be configured for manual adjustment, such as wing nuts, wing screws, and the like.

[0210] The RF system 502 may have an overall height (e.g., length) between about 30 cm and about 250 cm. The RF system 502 may have an overall weight between about 35 kg and about 100 kg.

[0211] Figure 5B illustrates a cross-section of a vertical plane along an exemplary embodiment of the RF system of Figure 5A. Within the housing 504, one or more housing cavities 506 and housing vent cavities 508 may be included. The housing cavities 506 may be discrete or combined into a single cavity. In some embodiments, the housing cavity 506 forms a cavity surrounding a central portion of the RF system 502 (e.g., along a vertical axis). Alternatively, the upper housing 410 and / or the lower housing 414 may each form a cavity surrounding a central portion of the RF system 502. The housing vent cavity 508 may include a portion of the RF system's internal portion that provides a passage for airflow through the internal portion (e.g., through the internal portion of the module housing 512, the housing vent cavity 508, and the internal portion of the module housing 412).

[0212] The second module housing 512 may contain one or more components, which are included in the module housing 412 of the RF system 402 described above. The module housing 512 may contain one or more system modules 538a to 538d, thermal interfaces 544a to 544d, heat sinks 540a to 540d, and / or other components described above. As shown in FIG5B, the module housing 512 accommodates an RF module 538a, an SOM module 538b, a second RF module 538c, a power supply module 538d, four heat sinks 540a to 540d, and corresponding thermal interfaces 544a to 544d. Although two housing fans 426 and 434 are shown, more or fewer such housing fans may be included. The RF system 502 may further include one or more plugs 542 for a purpose similar to or substantially the same as the plugs 442 described above.

[0213] As shown, RF modules 438a, 438c, 538a, and 538d are configured to be operatively coupled to corresponding directional antennas 406a, 406b, 406c, and 406d. In some embodiments, each modular assembly of a multi-module assembly can be configured to provide power, control, and / or amplification / multiplexing to up to two directional antennas. Thus, the RF system 402 described above includes two directional antennas 406a to 406b as a single modular assembly, while the RF system 502 can support up to four directional antennas 406a to 406d as a dual modular assembly. Each of the directional antennas 406a to 406d can be oriented at approximately 90° to a continuous directional antenna. This configuration can help improve the sensing capability (e.g., accuracy and / or precision) of a target source.

[0214] Figure 5C shows a side view of the RF system 502 shown in Figure 5A. Figure 5D shows a cross-section of the top view of the RF system 502 in Figure 5A along section 5D shown in Figure 5C. As shown in Figure 5D, the RF system 502 may include one or more module housing cavities 546. The module housing cavity 546 and the connecting housing cavity 506 may include communication links (e.g., wiring) and / or other elements described herein. For example, as mentioned above, to facilitate wiring or other data / power connections, the upper housing cavity 428, lower housing cavity 436, module housing cavity 446, module housing cavity 546, and connecting housing cavity 506 may include various openings and spaces through which wires can extend and / or position printed circuit boards ("PCBs") to connect various modules, fans, control panels, and external connections (e.g., connections to antennas, direction finders, external power supplies, and / or the like).

[0215] As shown in Figure 5D, one or more of the heat sinks 540a to 540d (e.g., heat sink 540a and heat sink 540c) may have heating elements (e.g., fins) longer than those of the others (e.g., heat sink 540b and heat sink 540d) (or otherwise have a larger surface area). Heating elements with a larger surface area can facilitate improved heat transfer and / or heat dissipation. Therefore, system modules 538a to 538d (such as RF modules 538a and RF modules 538c) can generate a higher amount of heat and thus couple to corresponding heat sinks (heat sink 540a and heat sink 540c) having a larger surface area than the other combined heat sinks.

[0216] Figure 5E illustrates an exemplary antenna mount 420 according to some embodiments described herein. Antenna mount 420 may correspond to any of the antenna mounts 420a to 420b described above. Antenna mount 420 may include an antenna bracket 572 mounted to one side 570 of RF system 502 (or RF system 402). Antenna bracket 572 may include an antenna interface 578 coupled to an antenna (e.g., any of directional antennas 406a to 406d). The antenna may be coupled via a coupling device (e.g., an attachment device), an adhesive, or the same other coupling device. In some embodiments, the antenna and antenna interface 578 are machined, molded, or otherwise formed together. Antenna interface 578 may define an antenna orientation 580, which may be related to an angle or tilt of the antenna. Antenna mount 420 may use structural configurations associated with antenna mount 420 to modify antenna orientation 580 (e.g., antenna tilt or angle).

[0217] Antenna bracket 572 may be coupled to side 570 via one or more coupling features. A first coupling feature may include a sliding bracket 574. Antenna bracket 572 may be coupled to sliding bracket 574 via pivot 590. Sliding bracket 574 may be slidably coupled to side 570 via track 588. In some embodiments, track 588 may be a linear track, such as shown in FIG5E. Track 588 may allow pivot 590 to translate parallel to side 570 (e.g., at approximately the same distance from side 570 during translation). Other configurations are possible. In some embodiments, sliding bracket 574 may be secured in place along track 588 via locking pin 592. Track 588 may include one or more markings or other indicators that indicate a specific orientation (e.g., antenna orientation / angle / tilt 580) of one of the antennas associated with the indicator. The markings may indicate antenna orientation 580 and / or the directional of one of the associated antennas.

[0218] A second coupling feature may include a mounting bracket 576. The mounting bracket 576 may be coupled to the side 570 via a fixed mount 586. The mounting bracket 576 may be rotatably coupled to the fixed mount 586 via a pivot 584. The mounting bracket 576 may be coupled to the antenna bracket 572 via a pivot 582. The pivot 584 allows the mounting bracket 576 to rotate about the pivot 584, thus modifying the positioning of the pivot 582 and / or the orientation of the antenna bracket 572, thereby providing a first degree of freedom for antenna orientation 580.

[0219] In various implementations, and as described below with reference to FIG9B, when an antenna is coupled to one side of an RF module, the pivots 584 and 590 may initially be out of position, allowing the antenna bracket 572 and mounting bracket 576 to be separated from the sliding bracket 574 and the fixed mounting member 586. To couple the antenna to the RF module, the user can first insert the pivot 590 of the antenna bracket 572 into a receiving portion of the sliding bracket 574 at a first high angle. The user can then rotate the antenna bracket 572 about the pivot 590, thereby causing the pivot 590 to be rotatably locked into the receiving portion of the sliding bracket 574. The user can then insert the pivot 584 (which may include a locking pin) to fully couple the antenna to the RF system side. Thus, advantageously, the antenna can be quickly and securely coupled to the RF system without tools. Similarly, advantageously, the antenna can be quickly removed from the RF system without tools by removing the pivot 584, rotating the antenna bracket 572 to a high angle, and removing the pivot 590 from the receiving portion of the sliding bracket 574.

[0220] Using the two coupling features shown in Figure 5E, the positioning of one of the antenna interfaces 578 can be modified in at least two degrees of freedom. A first degree of freedom may include a rotational degree of freedom, which includes rotation about pivot 582. A second degree of freedom may include the distance of the antenna interface 578 from side 570 based on the combination of sliding bracket 574 and mounting bracket 576. The coupling features may further include ports for connecting the antenna (e.g., directional antennas 406a to 406d) to modules inside the module housing for data and / or electrical communication (e.g., wires, cables). The ports may allow power cables and / or wires to be inserted into one or more of the elements described herein. The ports may allow waterproof or other weather-resistant connections.

[0221] Figures 6 to 8 illustrate block diagrams of the power and / or data connections, communication, and / or transmission of one or more of the system modules (e.g., system modules 438a to 438d, system modules 538a to 538d), antennas (e.g., directional antennas 406a to 406d, direction finder 408), and external devices of an RF system, as described above. Figure 6 illustrates a block diagram of one system including first and second module housings, corresponding directional antennas, a system management module 604, and one external device. A system 602 may include module housing 412, module housing 512, directional antennas 406a to 406d, a direction finder 408, a system management module 604, and / or one or more external devices 606 (e.g., other RF systems, other systems, sensors, and / or central processing servers).

[0222] In various implementations, the direction finder 408 can transmit data with RF modules 438a, 438c, 538a, and / or 538c. For example, the direction finder 408 can detect the direction of a signal source and communication information (e.g., source direction, detection arc, magnitude, frequency, and / or similar) related to the direction of the signal source and transmit this information to one, two, three, or all of the RF modules 438a, 438c, 538a, and / or 538c. Additionally, directional antennas 406a to 406d can communicate with corresponding RF modules 438a, 438c, 538a, and 538c. For example, RF modules 438a, 438c, 538a, and 538c can receive one or more signals from corresponding directional antennas 406a to 406d and / or can cause signals to be transmitted via corresponding directional antennas 406a to 406d. RF modules 438a and 438c communicate with corresponding SOM modules 438b and can transmit information to and receive information from corresponding SOM modules 438b. Similarly, RF modules 538a and 538c communicate with corresponding SOM modules 538b and can transmit information to and receive information from corresponding SOM modules 538b. As described herein, several SOM modules 438b and 538b can receive signals, process portions of the signals (e.g., by applying one or more ML models), and determine one or more signals to be transmitted, as well as various other functionalities. For example, several SOM modules 438b and 538b can also determine the location of a target object used to transmit signals, which determination may be based on information from one or more of the direction finder 408, directional antennas 406a to 406d, and / or external devices 606. (Several) SOM modules 438b and 538b can then cause the transmission of the determined RF signal via the corresponding RF module and directional antennas 406a to 406d.

[0223] SOM modules 438b and 538b can communicate with each other via one or more communication links 610. SOM module 438b and / or SOM module 538b can determine which of the directional antennas 406a to 406d should transmit an RF signal. Alternatively, SOM module 438b and / or SOM module 538b can transmit instructions for transmission to the corresponding RF modules 438a, 438c, 538a, and 538c. RF modules 438a, 438c, 538a, and 538c can then transmit a signal to the corresponding directional antennas 406a to 406d to cause the directional antennas 406a to 406d to transmit an RF signal at a target frequency, magnitude, direction, and / or similar. RF modules 438a, 438c, 538a, and 538c can be configured to amplify and / or multiplex signals received by corresponding SOM modules 438b and 538b. Power supply modules 438d and 538d can supply power to components within their respective module housings 412 and 512, and / or to the corresponding direction finder 408 and / or directional antennas 406a to 406d.

[0224] In some embodiments, SOM modules 438b and 538b may each communicate with a system management module 604 via one or more communication links 608. The communication links 608 may be wired or wireless. Alternatively, the system management module 604 may be located remotely from module housings 412 and 512. In some embodiments, the system management module 604 may communicate with and transmit data to and from external device 606. For example, the system management module may receive updates to a machine learning model or other software, information about a detected RF signal, information about an RF signal to be transmitted, information about what is being transmitted and where an RF signal is being transmitted or received, or similar information. For example, this information may be transmitted to the SOM module for processing and coordination with other devices and components. In some embodiments, the system management module 604 may determine which of the directional antennas 406a to 406d should transmit an RF signal and / or one or more attributes of that signal. For example, the system management module 604 can determine that the two directional antennas 406a to 406d should transmit a signal with different values ​​and / or directions. Alternatively, in some embodiments, the system management module 604 and / or the SOM modules 438b, 538b can automatically control the transmission of the corresponding directional antennas 406a to 406d.

[0225] In some implementations, the RF system may not include a system management module 604. In these implementations, SOM modules 438b and 538b may be incorporated into the functionality and / or components of the system management module 604 to provide the functionality of the RF system described herein. As mentioned above, the SOM modules 438b and 538b can communicate and coordinate with each other via one or more communication links 610. The "processing module" described above can be understood as similar to the functionality of the SOM module and / or a combination of the SOM module and the system management module.

[0226] SOM modules 438b and 538b can communicate with one or more external devices 606 via communication link 612. Communication link 612 can be wired or wireless. Alternatively, external devices 606 can be located remotely from module housings 412 and 512 and / or system management module 604. System management module 604 can communicate with external devices 606 via communication link 614, which can be wired or wireless. SOM modules 438b and 538b, system management module 604, and / or external devices 606 may include one or more communication interfaces or components (e.g., wireless, wired data interfaces) configurable for transmitting and / or receiving data. In some embodiments, external device 606 may include one or more of the following: additional systems or sensors (e.g., 104), other RF systems (e.g., 106), a central processing server (e.g., 107), and / or (a number of) user devices (e.g., 110).

[0227] In various embodiments, and as mentioned above, each of the modules (e.g., several SOM modules, several RF modules, several power supply modules and / or several system management modules) may internally include various thermal couplings, heat pipes or conductors and / or the like to conduct heat to the thermal interface and thereby to the heat sink.

[0228] Figure 7A illustrates a block diagram of an exemplary SOM module according to various embodiments. The illustrated SOM module 438b (e.g., 438a and / or 438b) includes one or more communication links 716 that allow the SOM module 438b to communicate with one or more other system modules described herein. The communication links 716 may be wired and / or wireless. The SOM module 438b may include SDR transceivers 702a to 702b, one or more storage devices 704 (which may include any type of data storage, volatile or non-volatile memory, solid-state storage and / or the like, as described above), one or more processors 706, one or more GPUs 708a to 708b, one or more communication adapters and / or PHY (e.g., a physical layer or layer 1 implemented by a PHY chip or (several) similar chips) 712 and one or more physical connectors 714. For example, one or more communication interfaces 710 may include one or more buses or communication channels and may communicate with SDR transceivers 702a to 702b, (a number of) storage devices 704, (a number of) processors 706, GPUs 708a to 708b and / or (a number of) communication adapters and / or PHY 712 (e.g., wired, wireless).

[0229] GPUs 708a to 708b can be configured to perform advanced computations that are slower, less efficient, or infeasible on a general-purpose processor. For example, GPUs 708a to 708b can be configured to perform matrix calculations, linear algebra calculations, Fourier transforms, and / or other advanced computations (including the execution of ML models as described herein). Also, for example, GPUs 708a to 708b can be configured to perform many computations per second (e.g., 10, 15, 20, 30, or more Tera FLOPS per second). These GPUs 708a to 708b may include their own memory and / or processors or be able to execute instructions stored on storage device(s) 704 and / or as indicated by processor 706. Instructions can be executed by processor(s) 706 and / or GPUs 708a to 708b. For example, processors 706 may instruct GPUs 708a to 708b to apply an ML model to sampled RF data to, for example, determine one type of object, as described herein. Alternatively or additionally, processors 706 may support calculations and other determinations.

[0230] SDR transceivers 702a to 702b include circuitry and functionality for generating, modifying, detecting, sensing, or otherwise cooperating with RF signals as described herein. For example, SDR transceivers 702a to 702b can be configured to transmit / receive signals that can be mixed, filtered, amplified, modulated / demodulated, and / or detected using one or more of the components described herein. As a further example, SDR transceivers 702a to 702b can receive instructions from processor 706 to generate one or more signals to be transmitted via the RF system (e.g., calibrating an identified object). Subsequently, SDR transceivers 702a to 702b can generate signals that can then be conveyed to an RF module (as applicable to calibration and containing instructions regarding the amount of power to be transmitted on any applicable antenna) for amplification and transmission via a directional antenna.

[0231] For example, several communication interfaces 710 may include a bus and may receive and transmit data to one or more components of the SOM module 438b via various wired and / or wireless data communication connections. Several communication interfaces 710 may transmit data to several physical connectors 714 via several communication adapters and / or PHYs 712. For example, several communication interfaces 710 may include a PCIe switch. Several communication adapters and / or PHYs 712 may include hardware transmission and / or reception adapters and / or, for example, (e.g., using a PHY chip or several other similar chips) an electrical, mechanical, and programming interface to a transmission medium, and may define the means of transmitting a raw bit stream via a physical data link connecting network nodes. For example, the bit stream may be divided into codewords or symbols and converted into a physical signal transmitted via a transmission medium. Therefore, an SOM module may include a communication adapter and associated physical connectors to provide communication with other components using various connections and protocols (including wired and wireless). For example, an SOM module may support wired or wireless Ethernet, optical connections, and / or any other type of power or data connection.

[0232] SDR transceivers 702a to 702b may include one or more analog-to-digital (“ADC”) converters and one or more digital-to-analog (“DAC”) converters. For example, a received signal (e.g., received via a directional antenna and RF module and transmitted to a processing module) may be passed through an ADC for further digital-domain sampling and analysis, as described herein. The signal to be transmitted may be generated by the SDR transceiver and passed through a DAC before being transmitted to an RF module for amplification and then transmitted via a directional antenna. In various embodiments, the ADC and DAC may be located elsewhere in the system, for example, as separate components of an SOM module and / or an RF module.

[0233] Figure 7B illustrates an exemplary system management module 604 according to one of the various embodiments. In embodiments where a given RF system includes two or more SOM modules 438 (e.g., when the RF system includes two or more module housings), the multiple SOM modules 438 may communicate directly with each other (e.g., via communication link 610 of Figure 6) to provide the functionality described herein, or may communicate with each other via a system management module 604 that provides coordinated functionality among the multiple SOM modules 438. In embodiments using a system management module, the system management module can provide communication with other external systems or sensors and can relay such communication to the multiple SOM modules 438. In various embodiments, the system management module may incorporate components and / or functionality (such as PNT component 734) of one or more SOM modules. In various implementations, when the RF system includes two or more SOM modules, one of the SOM modules can be manually and / or automatically designated as a system management module (and therefore there is no physically separate system management module) to provide the coordination and communication functionality described above. Thus, in these implementations, the functionality of the components (including (a number of) PNT components 734) and the system management module 604 described below can be incorporated into, combined with, or provided by a single SOM module (e.g., (a number of) SOM modules can provide functionality that is typically extended with the "processing module" as described above with reference to FIG1B).

[0234] The system management module 604 may include one or more processors 730, one or more storage devices 732 (which may include any type of data storage, volatile or non-volatile memory, solid-state storage and / or the like, as described above), one or more PNT components 734, one or more communication interfaces 736, one or more security modules 735, one or more communication adapters and / or PHY (e.g., a physical layer or layer 1 implemented by a PHY chip or (a number of) similar chips) 738, one or more physical connectors 740 and / or communication links 742.

[0235] (Several) PNT components 734 can be configured to allow an RF system to determine its position (longitude, latitude, and altitude / elevation) to target accuracy (e.g., within a few centimeters or meters). Position can be determined using (several) PNT components 734 and / or one or more antennas described herein (e.g., directional antennas 406a to 406d, direction finder 408) by using time signals transmitted / received along a line of sight. The system can be used to provide positioning and / or navigation for tracking the location of another device having a receiver (e.g., a target source signal). For example, (several) PNT components 734 can be configured to determine the position and / or movement of one of the RF systems described herein (e.g., RF system 402, RF system 502) or one of the same other systems that can be moved away from the system management module 604. (Several) PNT components 734 can receive and / or process signals to calculate a current local time, which allows for time synchronization with one or more other elements described herein.

[0236] In various embodiments, one or more PNT components may include, for example, Global Navigation Satellite System capabilities (e.g., Global Positioning System (“GPS”) capabilities) and other PNT functions. One or more PNT components may further provide orientation information, altitude information, angle / tilt information, and / or the like. In some embodiments, the PNT capabilities of the RF system may be provided wholly or partially in and / or by a direction finder. PNT capabilities may also be referred to herein as “positioning capabilities,” and one or more PNT components may also be referred to herein as “positioning components” and / or the like. The PNT capabilities of the RF system can be used, for example, for object location determination and / or tracking, as described herein, because this functionality can depend on the position, orientation, tilt, and / or the like of the RF system (e.g., enabling a correctly oriented antenna with correct orientation and tilt to be used to detect or calibrate an object).

[0237] (Several) security modules 735 can be configured to secure data and / or communications associated with system management module 604. For example, (several) security modules 735 can secure data received by system management module 604 or RF system from one or more external devices, systems, sensors, and / or other RF systems. Also, for example, (several) security modules 735 can secure data transmitted from system management module 604 or RF system to one or more external devices, systems, sensors, and / or other RF systems. For example, external devices may include additional systems or sensors 104, other RF systems, central processing service 107, (several) user devices 110, or (several) devices connected to such external devices. Security features may include one or more of the following: encryption (e.g., end-to-end encryption, data encryption, etc.), cryptographic compilation functions (cryptographic compilation keys, etc.), and / or similar. In some embodiments, for example, the security module 735 may also provide hardware acceleration to the system management module 604 and / or one or more other components of an RF system. In some embodiments, the security module 735 may include one or more dedicated chips for performing its configured functions. For example, such dedicated chips may provide hardware acceleration to the encryption and / or cryptographic compilation functions of the security module 735. Additional examples and details of the various functions of the security module 735 are described herein and in the '436 patent, including secure communication among the various components providing the operating environment 100.

[0238] Several processors 730 may be configured to execute software instructions stored on several storage devices 732 and / or other components of the system management module 604. Several communication interfaces 736 (which may include, for example, one or more buses or communication channels) may receive and / or transmit signals among other components of the system management module 604. Several communication interfaces 736 may transmit signals to several physical connectors 740 (similar to the description in Figure 7A above) via several communication adapters 738. These signals may be transmitted externally via communication link 742 as described herein (e.g., to provide communication with several SOM modules and / or external devices) (similar to the description in Figure 7A above).

[0239] Figure 8 illustrates a block diagram of an exemplary RF module (e.g., RF module 438a, RF module 438c, RF module 538a, RF module 538c). The illustrated RF module 438a may include one or more physical connectors 802, one or more multiplexers and / or filters 804, one or more transmit amplifiers, filters and / or limiters 806, one or more receive amplifiers, filters and / or limiters 808, and / or one or more physical connectors 810.

[0240] Several physical connectors 802 and several physical connectors 810 may communicate with other components described herein. For example, several physical connectors 802 may include components for establishing wired or wireless communication with one or more antennas (e.g., directional antennas 406a to 406d, direction finder 408) described herein via one or more communication links 812. Alternatively, several physical connectors 810 may include components for establishing wired or wireless communication with a corresponding SOM module (e.g., SOM module 438b, SOM module 538b) via one or more communication links 814. Thus, the RF module may include components (e.g., physical connectors and, as appropriate, communication adapters) for providing communication with other components using various connections and protocols (including wired and wireless). For example, the RF module may support dedicated wired connections for high power, wired or wireless Ethernet, optical connections, and / or any other type of power or data connection.

[0241] (Several) multiplexers and / or filters 804 may include one or more multiplexers. Multiplexers may, for example, switch between a transmit function and / or a receive function of the RF module 438a. In some embodiments, multiplexers may modulate and / or demodulate a received and / or transmitted signal. Alternatively or additionally, (Several) multiplexers and / or filters 804 may include one or more filters. Filters may include a wideband, narrowband, high-pass, low-pass, notch filter, and / or other types of filters for RF signals. In some embodiments, filters may be configured to reduce noise in a received and / or transmitted RF signal. (Several) multiplexers and / or filters 804 may function as a total multiplexing and / or filtering. Signals may be further (e.g., more finely granularly) amplified, filtered, and / or limited by (Several) corresponding transmit amplifiers, (Several) filters, and / or (Several) limiters 806 and / or (Several) receive amplifiers, (Several) filters, and / or (Several) limiters 808. Whether to use (a number of) transmission amplifiers, (a number of) filters and / or (a number of) limiters 806 or (a number of) receiver amplifiers, (a number of) filters and / or (a number of) limiters 808 may be at least partially based on the multiplexing settings associated with (a number of) multiplexers and / or filters 804.

[0242] Figure 9A illustrates an exemplary flow or method for assembling an RF system according to various embodiments of the present invention. Although Figure 9A discloses an example, other methods for assembling an RF system are feasible and described elsewhere herein. Furthermore, in various embodiments, various blocks of the flow or method may be reconfigured, selected, and / or omitted, and / or additional blocks may be added. The blocks schematically illustrated in Figure 9A will be described with reference to certain hardware components of the present invention. However, it should be understood that the hardware components of Figure 9A and / or individual components or subsets thereof may be implemented in combination with other hardware components, software components, and / or systems equivalently without departing from the scope of the present invention. For example, additional details regarding coupling an antenna to a module housing are described herein with respect to Figures 4A through 5D.

[0243] At block 902, the method may include providing one or more module housings (e.g., module housing 412, module housing 512). At block 904, the method may include embedding one or more system modules (e.g., system modules 438a to 438d, system modules 538a to 538d) into one or more housings. The method may include assembling one or more components without tools. The system modules may be locked and / or adhered (e.g., glued, welded) to one or more portions (e.g., the inside) of the module housings. The module housings may include a locking mechanism that presses the system modules against a thermal interface side of the module housing to maximize heat transfer.

[0244] At block 906, the method may include embedding a heat sink into a portion of the module housing. The heat sink is coupled to a thermal interface that is coupled to the system module, as described above.

[0245] At block 908, the method includes coupling upper, lower, and / or combined housings together to form a modular assembly. The modular assembly can be a single, dual, triple, or higher-order modular assembly. The coupling may include a coupling element that can be coupled (e.g., assembled) without power and / or other tools. For example, the coupling element may include various coupling features such as friction fits (e.g., snap-fit ​​fits), coupling elements (e.g., screws, nails, attachment devices, and / or the like).

[0246] At block 910, the method includes coupling (e.g., attaching, connecting, and / or the like) one or more antennas (such as the directional antennas 406a to 406d described above and / or the direction finder 408). The antennas can be coupled as described in Figure 5E above and / or Figure 9B below. For example, an antenna bracket can be rotatably coupled to one or more coupling features. Alternatively, the antenna bracket and / or one or more coupling features can be fixedly coupled to one side of the RF system. In some embodiments, all antennas can be coupled to the same portion of a modular assembly (e.g., to an upper module housing).

[0247] At block 912, the method includes providing communication links and power connections among various components (including system modules of the RF system), and is included during the assembly steps described above. The communication links may include various wires and / or optical connections (e.g., cables, such as Ethernet) that can be provided via various cavities of the RF system housing, as described above. Block 912 may include coupling one or more connectors that can be configured to protect certain components including the communication links and / or power connections from adverse weather or other environmental conditions described herein. The communication links may be wired links and / or wireless data interfaces. In some embodiments, the method includes coupling one or more antennas to an external portion or outer surface of the module housing and providing communication links between one or more antennas and one or more modules. These external-to-internal communication links may be provided via one or more plugs or ports located on the external surface of the RF system. These plugs or ports may be configured to isolate the interior of the housing from the external environment and to provide secure connections to corresponding connectors for wires, such as those from an antenna, an external power source, and / or the like.

[0248] At block 914, the method may include mounting the RF system. This may include mounting a portion of a modular assembly (e.g., a bottom, a side, a top) to a mounting surface (such as another RF system) and / or to a mounting system (e.g., a tripod, a building, the ground, and / or the like). Mounting may include a coupling element that can be coupled (e.g., assembled) without power and / or other tools. At block 916, the method may include providing power, starting and / or operating the RF system. Operating the RF system may include receiving RF signals via one or more antennas, processing the received RF signals, and / or transmitting RF signals at one or more frequencies.

[0249] Figure 9B illustrates an exemplary process or method for coupling an antenna to a module housing according to various embodiments of the present invention. Although Figure 9B discloses an example, other methods for coupling an antenna to a module housing are feasible and described elsewhere herein. Furthermore, in various embodiments, various blocks of the process or method may be reconfigured, selected, and / or omitted, and / or additional blocks may be added. The blocks schematically illustrated in Figure 9B will be described with reference to certain hardware components of the present invention. However, it should be understood that the hardware components of Figure 9B and / or individual components or subsets thereof may be implemented in combination with other hardware components, software components, and / or systems equivalently without departing from the scope of the present invention. For example, additional details regarding coupling an antenna to a module housing are described herein with reference to Figure 5E.

[0250] At block 922, a first coupling feature (e.g., sliding bracket 574) of an antenna mount (e.g., 420 in FIG. 5E and elsewhere) can be slidable onto a track (e.g., track 588) on one side (e.g., side 570) of a module housing. In some embodiments, the first coupling feature may be positioned in the track on the side of an RF system. In some embodiments, the first coupling feature may be slidably movable in the track (e.g., along a line).

[0251] At block 924, a first pivot point (e.g., pivot 590) of an antenna bracket (e.g., antenna bracket 572) can be engaged at a first angle into a receiving portion of a first coupling feature (e.g., sliding bracket 574). The first angle may include a high angle. The antenna bracket is then rotated about the first pivot point (e.g., pivot 590), causing the pivot point to be rotatably locked into the receiving portion of the first coupling feature. For example, the antenna bracket may be rotated downwards from the high angle to a lower angle where the second coupling feature may be coupled to a fixed mount. The first pivot point may include a locking portion of the antenna bracket and may include a cylindrical portion (e.g., a rod) with a notch (e.g., a notched cylinder or rod). The notch allows the locking portion of the antenna bracket to engage and disengage with the receiving portion of the first coupling feature at a first angle (e.g., a high angle) rather than at other angles. Therefore, the locking portion of the antenna bracket can be coupled to the receiving portion of the first coupling feature by a rotational movement. The coupling between the antenna bracket and the receiving portion of the first coupling feature provides a first pivot point.

[0252] At block 926, a second coupling feature (e.g., mounting bracket 576) of one of the antenna mounts can be coupled to a fixed mount (e.g., fixed mount 586) on a side of the module housing (e.g., the same side as the first coupling feature). For example, the mounting bracket (e.g., mounting bracket 576) can be rotatably coupled to the fixed mount at one end via a second pivot point (e.g., pivot 584), and the mounting bracket (e.g., mounting bracket 576) can be coupled to the antenna bracket (e.g., antenna bracket 572) at the other end via a third pivot point (e.g., pivot 582). In some embodiments, a self-locking pin (e.g., pivot 584) can be used to couple or secure the second coupling feature to the fixed mount. The second and third pivot points corresponding to the second coupling feature allow the mounting bracket to rotate about the pivot points, thus modifying the positioning of one of the pivots and / or the orientation of one of the antenna brackets, thus providing a first degree of freedom for antenna orientation.

[0253] In some embodiments, the first coupling feature may slide directly into a track (e.g., track 588) and translate parallel to the side (e.g., at approximately the same distance from the side during translation). Other coupling configurations are also possible, such as bolted connections, welded connections, threaded connections, snap-fit ​​connections, or the like. In some embodiments, the first degree of freedom provided by the coupling configuration for the first coupling feature may be absent. In some embodiments, two or more degrees of freedom provided by the coupling configuration for the first coupling feature may be present (e.g., by incorporating additional pivot points along the mounting bracket or elsewhere). In some embodiments, a fixed mount may be included on the side of the housing and / or RF system. In some embodiments, the first coupling feature may be coupled to the fixed mount, and the second coupling feature may be coupled to the antenna bracket.

[0254] At block 928, in some embodiments, and depending on the circumstances, for example, an antenna may be coupled to an antenna bracket via an antenna interface (e.g., antenna interface 578, and described elsewhere herein). Alternatively, the antenna is machined, molded, or otherwise formed together with the antenna bracket. In some embodiments, other sensing devices or components may be attached to the antenna interface.

[0255] At block 930, the first coupling feature can slide along the track to a target position and can be locked in place using a locking pin (e.g., locking pin 592). In some embodiments, the locking pin can be configured to lock into one of a plurality of slots (e.g., slots) positioned on the track. In some embodiments, the slots can be spaced at specific distances (e.g., approximately every 10 mm, 1 cm, 2 cm, 5 cm, or the like). In some embodiments, for example, the slots can be spaced at specific distances corresponding to a specific angle or tilt of an antenna (such as the antenna connected at block 928). The angle of the antenna can include an elevation or tilt angle relative to a plane on which the RF system is located (if the RF system is horizontally mounted, this can be the same as a line perpendicular to or normal to a side surface of the RF system on which the antenna is mounted). For example, there may be a first slot at a first position on the track corresponding to a tilt angle of 5° from a line perpendicular to the center of the antenna interface surface. For example, a second slot may exist at a second location on the track, corresponding to a line perpendicular to the center of the antenna interface surface at an angle of 10°, the second slot being spaced apart from the first slot. Additionally, multiple slots corresponding to specific tilt angles may exist. In some embodiments, the tilt angle may be related to placement considerations. For example, if an RF system is placed on the roof of a building, the tilt angle may need to be adjusted so that an antenna connected to an antenna bracket can be directed downwards (e.g., -10°) to monitor signals closer to the Earth's surface and the sky, rather than just the sky.

[0256] At block 932, an antenna conductor can be coupled to the RF system via a port or connection on the RF system. In some embodiments, the port or connection may be located outside or inside the RF system. In some embodiments, the port or connection may provide an interface for power and / or data transmission to and from the antenna. For example, power and signals may be transmitted from the RF module to the antenna so that the antenna transmits RF signals. Also, for example, power and signals may be transmitted from the antenna to the RF module and then to a processing module for analysis and / or processing. Additional information regarding transmission and signal processing is described elsewhere in this document.

[0257] Figure 9C illustrates an exemplary process or method for managing heat transfer in an RF system according to various embodiments of the present invention. Although Figure 9C discloses an example, other methods for managing heat transfer in an RF system are feasible and described elsewhere herein. Furthermore, in various embodiments, various blocks of the process or method may be reconfigured, selected, and / or omitted, and / or additional blocks may be added. The blocks schematically illustrated in Figure 9C will be described with reference to certain hardware components of the present invention. However, it should be understood that the hardware components of Figure 9C and / or individual components or subsets thereof may be implemented in combination with other hardware components, software components, and / or systems without departing from the scope of the present invention.

[0258] At block 942, the method includes providing one or more heat sinks (e.g., heat sinks 540a to 540d) within an internal portion of an RF system (e.g., RF system 402, RF system 502). The one or more heat sinks may be disposed within the internal portion and surrounded by a periphery of a module housing, which may extend vertically within the RF system (e.g., within module housing 412 of the RF system). The heat sink may include snap-on fins or other structures configured to draw heat away from the heat sink and / or other components of the RF system. The internal portion of the one or more module housings through which one or more heat sinks are embedded and through which air can flow may be referred to as a cavity or channel.

[0259] At block 944, the method includes providing a sealed thermal interface (e.g., thermal interfaces 544a to 544d) between the heat sink and the wall of the inner portion of the module housing opposite to the system module. The thermal interface may be fluid-sealed (e.g., liquid-sealed) from an outer portion of the interior of the RF system to an inner portion of the RF system. Providing a seal may include providing an adhesive, sealant, or other material. Alternatively or additionally, the thermal interface may be adhered to and / or formed to an inner portion of the RF system and / or to the system module.

[0260] At block 946, one or more plugs (e.g., plug 442, plug 542) may be provided at least partially between two or more of the radiators. For example, the plugs may prevent air from bypassing the radiators. Thus, they may redirect airflow to occur through the radiators.

[0261] At block 948, air movement can be facilitated via vents and / or cooling fans disposed in the upper and / or lower housing of the RF system. The cooling fans can be coordinated to push air in the same direction as each other. For example, at block 950, the method may include activating the cooling fans to cause airflow through portions within the RF system (e.g., entering from the lower housing and rising through the heatsink and exiting the upper housing). This can provide improved cooling of one or more heatsinks and / or system modules described herein. Alignment between one of the coupled modular assemblies (e.g., a dual-modular assembly) may include arranging one or more fans such that the fans draw air through the two modular assemblies (e.g., inside one of them), one or more heatsinks, a second cavity, and a second one or more heatsinks to draw heat from one or more modules and a second one or more modules.

[0262] As mentioned above, a portion of the housing containing one or more heat sinks, through which air can flow, may be referred to as a cavity or channel. The channel may extend from the lower housing through one or more module housings and to the upper housing. Air can flow through this channel, if caused by one or more fans.

[0263] Figure 9D illustrates an exemplary flow or method of operating an RF system, for example, to receive and / or transmit signals, according to various embodiments of the present invention. Although Figure 9D discloses one example, other methods of operating an RF system are feasible and described elsewhere herein. Furthermore, in various embodiments, various blocks of the flow or method may be reconfigured, selected, and / or omitted, and / or additional blocks may be added. The blocks schematically illustrated in Figure 9D will be described with reference to certain hardware components of the present invention. However, it should be understood that the hardware components of Figure 9D and / or individual components or subsets thereof may be implemented in combination with other hardware components, software components, and / or systems without departing from the scope of the present invention.

[0264] At block 962, a directional antenna can be coupled to an RF module. In some embodiments, one or more cables or wires can also be coupled to the directional antenna(s) at one end(s) and to the RF module(s) at the other ends(s). In some embodiments, the antenna can be coupled directly or indirectly (e.g., via a port or plug on the RF system, as described above) to the RF module to enable electrical communication. Additionally, in some embodiments, the antenna (e.g., using different or the same cables or wires for connection to one of the RF modules) can draw or receive power from a power supply of the corresponding RF system. At block 963, a direction finder is similarly coupled to one or more RF modules and / or one or more SOM / processing modules.

[0265] At block 964, the RF module can be coupled to the processing module. For example, the processing module may include an SOM module and / or a system management module, each of which is described in more detail herein.

[0266] At block 966, the antenna may receive a signal (e.g., an RF signal). In some embodiments, the signal may be transmitted (e.g., via coupling described at block 962) to an RF module and / or a processing module (e.g., an SOM module and / or a system management module). In various embodiments, the signal may be amplified, filtered, and / or limited by the RF module before being transmitted to the associated processing module.

[0267] At block 968, once the processing module receives the signal at block 966, it processes the signal. For example, the signal processing may be similar to any of the examples and / or methods / processes described herein and may include the application of an ML model. The processing may include determining the position or location of a target object and / or determining one or more signals to be transmitted.

[0268] At block 970, the processing module may generate one or more signals. For example, the generation of a signal may resemble any of the instances and / or methods / processes described herein.

[0269] At block 972, the processing module can send the generated signal to the RF module, and the RF module can cause the signal to be transmitted via a directional antenna. For example, the transmission of a signal can be similar to any of the examples and / or methods / processes described herein. In various embodiments, the signal can be amplified, filtered, and / or limited by the RF module before being transmitted to the associated directional antenna. VII. [, Additional illustrative software-related features and functionalities , ]

[0270] The following description of Figures 10 through 15 provides further details regarding the implementation schemes, components, and related functionality of the RF system. Although different numbers may be used to describe various states of the RF system compared to the foregoing description, it should be understood that similar components and states may contain similar or identical functionality. Therefore, the states described above are applicable to the states described below, and vice versa.

[0271] Figure 10 illustrates a block diagram 1000 illustrating an exemplary functionality of an RF system according to various embodiments of the present invention. Various configurations of the functional blocks of Figure 1000 may be implemented by one or more hardware and / or software components described above, for example, with reference to Figures 1B to 1C. The functional blocks schematically illustrated in Figure 10 will be described with reference to certain software and hardware components of the present invention. However, it should be understood that the functional blocks of Figure 10 and / or individual components or subsets thereof may be implemented in combination with other hardware and / or software components and / or systems without departing from the scope of the present invention.

[0272] In some embodiments, for example, the functional blocks of FIG10 may be implemented in conjunction with or by means of the RF systems 102 and 106 of the present invention (e.g., including any software components (such as those described with respect to FIG1C) and / or hardware components (such as those described with respect to FIG1B)). In some embodiments, for example, the processing of any software or electronic data may be performed by one or more components of processing modules 130 and / or RF modules 131. For example, in some embodiments, machine learning algorithms may be trained or applied using GPUs 138 and / or other components of processing modules 130. Additionally, in some examples, for example, an RF signal may be generated by an SDR transceiver 139 and then transmitted to RF module 131 before being transmitted via one or more directional antennas 122. Additionally, in some examples, an RF signal may be received by one or more directional antennas 122 and then transmitted via RF module 131 before being processed by processing module 130 (e.g., by applying a machine learning algorithm or model, for example, using a GPU 138). To ensure that a transmitted RF signal is sent in the appropriate direction or via the appropriate directional antenna, each RF system may use a PNT component 140 to determine features or characteristics associated with the location of the corresponding RF system, orientation, elevation / altitude, and the like. This information from PNT component 140, or received data from the PNT component 140 of another RF system, can be used to determine which directional antenna(s)(s) to activate and from which to transmit or use to track an identified object. For example, the PNT component can be used to determine the relevant location and orientation information of each and all RF systems in a network. Furthermore, in some embodiments, multiple antennas may be directly or indirectly connected to a processing module. In some embodiments, an antenna is paired with an RF module such that each RF module can control a single antenna to receive or transmit any RF signal, allowing each antenna to operate independently of the other antennas. Additional information regarding the hardware components is described herein with reference to Figures 1B and 3.

[0273] The functional blocks in Figure 10 typically include a receive group corresponding to software and hardware functions associated with the received signal, and a transmit group corresponding to software and hardware functions associated with the transmission of the signal. Depending on the specific configuration of an RF system (e.g., 102 or 106) associated with the software components, the receive and transmit groups of the block include communication with one or more antennas and / or direction finders. For example, the receive and transmit groups may include communication with at least one antenna or direction finder 1002, at least one directional antenna 1004, and / or any other directional antenna, direction finder, and / or other hardware components configured for transmitting and / or receiving RF signals.

[0274] In some embodiments, the direction finder 1002 may be configured to receive RF signals and the antenna(s) 1004 may be configured to receive and / or transmit RF signals. Alternatively, one or both of the direction finder 1002 and the antenna(s) 1004 may be configured to receive and transmit RF signals. The direction finder 1002 and / or the antenna(s) 1004 may comprise (e.g., via hardware) configured and / or (e.g., via software, such as in a software-defined antenna configuration) tuned to transmit and / or receive RF signals across a wide frequency range. In some embodiments, the direction finder 1002 and / or the antenna(s) 1004 may be directional antennas configured to transmit and / or receive within a defined angular range, as described elsewhere herein. In some embodiments, the use of sector and / or directional antennas can advantageously prevent the detection of signals transmitted from the system or other transmitters in the vicinity of the system, which would otherwise interfere with the detection and analysis of signals from a remote transmitter intended to be detected by the system. Similarly, in some embodiments, the use of sector and / or directional antennas can advantageously prevent the transmission of signals transmitted from the system in the vicinity of the system and in a direction close to a friendly device or system, which would otherwise interfere with the operation of the friendly device or system.

[0275] In some embodiments, the receive group of blocks is typically configured to receive and analyze data from one or more antennas (e.g., 1002 and / or 1004) corresponding to RF signals received at one or more antennas (e.g., corresponding to one or more directions or areas). In some embodiments, the receive group of blocks may include a receiver block 1008, a signal detection block 1010, an RF machine learning block 1012, a direction finding block 1014, a directional line ("LOB") block 1016, a demodulation block 1018, and / or a data extraction block 1020. Some or all of the receive group blocks (such as LOB 1016 and data extraction 1020 blocks) may include communication with a system manager 1022.

[0276] In some embodiments, the transmit group of the block is typically configured to cause the transmission of RF signals (e.g., corresponding to one or more directions or regions) using one or more antennas (e.g., 1002 and / or 1004) based on the output from one of the receive groups of the block. In some embodiments, the transmit group of the block may include a detection to generate a waveform block 1024, a waveform generator 1026, and an amplifier 1028.

[0277] In some embodiments, receiver block 1008 communicates with one or more antennas (such as antennas or direction finders 1002 and / or antenna 1004). In some embodiments, receiver block 1008 is configured to receive raw signals from one or more antennas. In some embodiments, the raw signals may be analog signals comprising one or more RF signal blocks, or digital signals generated by analog-to-digital conversion at one or more antennas. Receiver 1008 may be configured for analog-to-digital conversion of analog RF signals received from one or more antennas. In some embodiments, receiver 1008 communicates with signal detection block 1010 and may transmit signals received from one or more antennas and / or converted into digital signals to signal detection block 1010.

[0278] In some embodiments, signal detection block 1010 is configured to analyze signals received from one or more antennas via receiver block 1008. In some cases, the signal received from receiver block 1008 may comprise a superposition of one of a plurality of signals emitted from different sources within the directional range of one or more antennas. Therefore, in some embodiments, signal detection block 1010 is configured to identify and / or separate individual block signals based on the signals received from receiver block 1008. Signal detection block 1010 annotates and / or filters the received signals based on factors such as frequency, time, intensity, and / or the like.

[0279] In some embodiments, the RF machine learning block 1012 (e.g., similar to any other machine learning block or embodiment described herein) may be implemented to train and / or apply one or more AI and / or ML models or parametric functions based at least in part on raw, annotated, and / or filtered signals received from receiver block 1008. In some embodiments, the machine learning block may implement one or more machine learning or artificial intelligence algorithms or parametric functions, which may, for example, implement models executed by one or more processors for detection / recognition. The machine learning block may be configured to apply a model that helps detect which types of RF signals (e.g., a series of RF signals, a specific frequency, or a combination of frequencies, and / or the like) indicate which types of objects. One or more of these models may be used to determine an expected RF signal frequency range or additional signal properties based on the analysis of received or captured data. In some embodiments, signal monitoring criteria or signal identification criteria may be specified by a user, administrator, or automatically specified. For example, signal monitoring criteria or signal identification criteria may indicate which types of detection are to be monitored, recorded, or analyzed. By specifying a particular type of detection, resources (e.g., processing power, bandwidth, and / or similar) can be saved only for the type of detection to be performed.

[0280] Regarding any software-related features (e.g., those described herein and with respect to Figures 1C and 10 through 15), training, retraining, updating, implementing, or using any AI or ML model or parametric function can be implemented on or by one or more RF systems. For example, ML can be automatically improved through experience and by using data (e.g., RF signal data). Although the terms machine learning and / or artificial intelligence are used herein, the scope of each term should include all and every type of machine learning, artificial intelligence, neural networks, and the like known to those skilled in the art. An AI or ML model can be built or trained on sample data or training data to make predictions or decisions without explicit programming. In some embodiments, machine learning algorithms, models, and / or programs can perform tasks without explicit programming. For example, some forms of the present invention may include training an AI / ML model in a computer to perform certain desired tasks that a human might not be able to perform manually.

[0281] Several different types of AI / ML algorithms and AI / ML models or methods can be used by machine learning components to implement models. For example, some embodiments described herein may use a logistic regression model, decision tree, random forest, convolutional neural network, deep network, or others. However, other models are feasible, such as a linear regression model, a discrete choice model, or a generalized linear model. Machine learning models can be configured to adaptively develop and update over time based on new inputs. For example, as new received data becomes available, the model can be trained, retrained, or otherwise updated on a periodic basis to help maintain more accurate predictions in the model as data is collected over time. Also, for example, the model can be trained, retrained, or otherwise updated based on configurations received from a user, administrator, or other device. Non-limiting instances of machine learning algorithms that can be used to train, retrain, or otherwise update models may include supervised and unsupervised machine learning algorithms, including regression algorithms (such as, for example, ordinary least squares regression), instance-based algorithms (such as, for example, learned vector quantization), decision tree algorithms (such as, for example, classification and regression trees), Bayesian algorithms (such as, for example, simple Bayesian), clustering algorithms (such as, for example, k-means clustering), association rule learning algorithms (such as, for example, prior algorithms), artificial neural network algorithms (such as, for example, perception), deep learning algorithms (such as, for example, deep Boltzmann machines), dimensionality reduction algorithms (such as, for example, principal component analysis), ensemble algorithms (such as, for example, stacked generalization), support vector machines, joint learning, and / or other machine learning algorithms. These machine learning algorithms may include any type of machine learning algorithm, including hierarchical clustering algorithms and cluster analysis algorithms, such as k-means algorithms. In some cases, performing a machine learning algorithm may involve using an artificial neural network. By using machine learning techniques, large amounts of received data (such as megabytes or gigabytes) can be analyzed to generate or implement models with minimal or no manual analysis or review by one or more humans.

[0282] In some embodiments, a supervised learning algorithm can construct a mathematical model of a dataset containing either an input or a desired output. For example, training data comprising a set of training or labeled / automated instances can be used. Each training instance has one or more inputs and a desired output (also referred to as a supervised signal). In the mathematical model, for example, each training instance is represented by an array or vector (e.g., an eigenvector), and the training data is represented by a matrix. Through iterative optimization of a target function, a supervised learning algorithm can learn a function that can be used to predict the output associated with a new input. For example, an optimal function allows the algorithm to correctly determine the output of an input that is not part of the training data. For example, an algorithm that claims to improve its output or prediction accuracy over time has learned to perform the task. Types of supervised machine learning algorithms may include, but are not limited to, active learning, classification, and regression. For example, classification algorithms are used when the output is limited to a finite set of values. For example, regression algorithms are used when the output can have any value within a range. As an example, for a classification algorithm for filtering emails, the input is an incoming email, and the output is the name of the folder where the email will be archived. In some embodiments, similarity learning (a region of supervised machine learning) is closely related to regression and classification, but the goal is to learn from instances using a similarity function that measures the similarity or relevance of two objects. In some embodiments, similarity learning is applied to ranking, recommendation systems, visual recognition tracking, facial verification, and speaker verification.

[0283] In some embodiments, unsupervised learning algorithms may acquire only one set of data containing inputs and search for structure within the data, such as grouping or clustering data points. For example, the algorithm may learn from test data that has already been labeled, classified, or categorized. Instead of responding to feedback, unsupervised learning algorithms may identify commonalities in the data and react based on the presence or absence of such commonalities in each new set of data. In some embodiments, unsupervised learning encompasses generalizing and interpreting data features. In some embodiments, cluster analysis assigns a set of observations to subsets (e.g., clusters) such that observations within the same cluster are similar according to one or more predefined criteria, while observations derived from different clusters are different. In some cases, different clustering techniques may make different assumptions about the structure of the data, which is typically defined by the same similarity measure and assessed, for example, by internal tightness or similarity between members of the same cluster and separation or difference between clusters. For example, other methods may be based on estimated density and graph connectivity.

[0284] In some embodiments, semi-supervised learning may be a combination of unsupervised learning (without any labeled training data) and supervised learning (with fully labeled training data). For example, some training instances may lack training labels, and in some cases, such training instances may produce a significant improvement in training accuracy compared to supervised learning. In some embodiments, and in weakly supervised learning, the training labels may be noisy, limited, or inaccurate; however, such labels are often more inexpensive to obtain, resulting in a larger effective training set.

[0285] In some embodiments, one area of ​​machine learning relates to the same concept of how a software agent should act in an environment to maximize cumulative reward. In some embodiments, the environment is typically represented as a Markov decision program (MDP). In some embodiments, reinforcement learning algorithms use dynamic programming techniques. In some embodiments, reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when an exact model is not feasible.

[0286] In addition to supervised learning algorithms, unsupervised learning algorithms and semi-supervised learning can also be implemented. In some embodiments, other types of machine learning methods can be implemented, such as: reinforcement learning (e.g., the same concept of how a software agent should act in an environment to maximize cumulative rewards); dimensionality reduction (e.g., a procedure to reduce the number of random variables under consideration by obtaining a set of principal variables); self-learning (e.g., learning without external rewards and without external teacher advice); feature learning or representation learning (e.g., retaining information in its inputs but also transforming the information in a way that makes it useful); anomaly detection or outlier detection (e.g., identifying rare items, events, or observations that arouse suspicion by being significantly different from most data); association rules (e.g., discovering relationships between variables in a large database); and / or similar.

[0287] In some embodiments, direction-finding block 1014 may be configured to determine a direction associated with a signal or signal component detected at signal detection block 1010. In some embodiments, the direction-finding block may be configured to find the direction of a signal or signal component based on two or more measurements from different locations and / or different antennas. For example, in some embodiments, direction-finding block 1014 may employ a phase and / or Doppler technique to find a direction more accurately based on a matched signal received at a plurality of antennas, as described elsewhere herein. For example, the data analyzed by direction-finding block 1014 may include data captured by one or more direction finders 1002 and / or (a plurality of) antennas 1004. For example, data from multiple receiving antennas may be used together to determine a more accurate location of an identified object emitting a detected signal.

[0288] In some embodiments, the azimuth line ("LOB") block 1016 can be configured to generate and provide an LOB in conjunction with the direction finding block 1014. For example, an LOB can be an azimuth angle from a direction finder 1002 or (a plurality of) antennas 1004 to a transmitter associated with a received signal. In the case of a mobile transmitter (such as a ground or air vehicle), the LOB block 1016 can be further configured to determine multiple LOBs associated with an individual transmitter over time to track the position and / or movement of the transmitter.

[0289] In some embodiments, demodulation block 1018 can be configured to extract information bearer signals from signals received at one or more antennas. In some embodiments, for example, demodulation block 1018 may be selected and the system may move vertically from signal detection block 1010 to data extraction 1020. For example, in some cases, the RF signal may be a carrier modulated using an information bearer signal that may contain useful information associated with a line of objects or other objects transmitting signals. Demodulation block 1018 can extract this information, which may be, for example, an audio signal, a video signal, a binary data signal, or other information bearer signals transmitted in a carrier signal received at one or more antennas.

[0290] In some embodiments, data extraction block 1020 may receive and analyze any demodulated signal generated by demodulation block 1018. For example, the data extraction block may identify any data payload transmitted in a received signal. Data extraction block 1020 may be further configured for associated functionality, such as decryption of extracted data or other analysis.

[0291] In some embodiments, the system manager 1022 communicates with the data extraction block 1020, the LOB block 1016, and / or any other functional blocks illustrated in FIG. 10. In some embodiments, the system 1022 may receive information output from one or more connected components and provide it to an administrator or other systems on the network. In some embodiments, the system 1022 may configure one or more connected components to adjust functionality or settings as needed.

[0292] In some embodiments, a waveform to be transmitted is determined by detecting, for example, the output of one of the signal detection 1010 and / or RF machine learning 1012 blocks, to generate a waveform block 1024. The waveform may include, for example, signal components and / or operating instructions based at least in part on the output of the RF machine learning model and / or on the frequencies transmitted and / or received by known transmitting devices on the load set 1030.

[0293] In some embodiments, waveform generator 1026 receives the desired waveform from a detector to generate waveform block 1024 and converts the waveform into an analog waveform for transmission via one or more antennas. Waveform generator 1026 sends the analog waveform to amplifier 1028 (e.g., a power amplifier).

[0294] In some embodiments, amplifier 1028 may be an amplifier or enhancer configured to amplify an analog waveform for transmission via one or more antennas (e.g., antennas 1004). An output from amplifier 1028 is sent to antennas 1004 for transmission (e.g., in one or more directions or regions).

[0295] In some embodiments, the functional block may further include one or more load sets 1030. A load set 1030 may include one or more predetermined parameters corresponding to one of the operating environments in which the system is deployed. For example, in some embodiments, the load set 1030 may determine parameters associated with an operating detection frequency range, transmission frequency range, rate of change, or other attributes of the system. The load set 1030 may further include known properties and / or signal components, transmission frequencies, and / or similar parameters associated with the type of transmitter known to exist in the operating environment and likely to be detected by the system. For example, the load set 1030 may include a whitelist and / or a blacklist corresponding to known friendly or unfriendly devices. The load set 1030 may be predetermined and / or may be generated and / or updated during operation when a transmitter is detected, discovered, identified, tracked, recovered, or similarly operated.

[0296] Figures 11 and 12 illustrate exemplary flows related to the training and application of an RF AI / ML model according to various embodiments of the present invention. The blocks in the flowcharts illustrate exemplary implementations, and in various other implementations, the blocks may be reconfigured, selected, and / or omitted, and / or additional blocks may be added. In various implementations (e.g., with respect to Figures 11 and 12), the various functionalities described may be performed substantially on an ad-hoc basis; for example, received data may be processed as it is received. Alternatively, (e.g., with respect to Figures 11 and 12) the various functionalities described may be performed in batches and / or in parallel, and / or by means of multiple systems or devices. In some embodiments, for example, the exemplary software components of Figures 11 and 12 may be implemented in conjunction with or by means of one or more of any systems or devices described herein (such as the systems or devices described in Figure 1A). In some embodiments, training of a machine learning algorithm (e.g., FIG. 11) can be performed on a single device (e.g., using input from a single person), and the application of the trained machine learning model can be implemented by one or more RF systems. In some embodiments, for example, the exemplary software components of FIG. 11 and FIG. 12 can be implemented in conjunction with or by the RF systems 102 and 106 of the present invention (e.g., including any software components (such as those described with respect to FIG. 1C) and / or hardware components (such as those described with respect to FIG. 1B)). In some embodiments, for example, processing of any software or electronic data can be performed by one or more components of processing module 130 and / or RF module 131. For example, in some embodiments, machine learning algorithms can be trained or applied using GPU 138 and / or other components of processing module 130. Additionally, in some examples, an RF signal may be generated by an SDR transceiver 139 and then transmitted to the RF module 131 before being transmitted via one or more directional antennas 122. In other examples, an RF signal may be received by one or more directional antennas 122 and then transmitted via the RF module 131 before being processed by the processing module 130 (e.g., by applying a machine learning algorithm or model, for example, using a GPU 138). Regarding ensuring that a transmitted RF signal is sent in the appropriate direction or via the appropriate directional antenna, each RF system may use a PNT component 140 to determine characteristics or features associated with the location of the corresponding RF system, orientation, elevation / altitude, and the like. This information from the PNT component 140, or received data from the PNT component 140 of another RF system, can be used to determine which directional antenna(s)(s) to activate and from which to transmit or use to track the identified object. For example, the PNT component is an important component in determining the relevant location and orientation information of each and all RF systems in a network. Furthermore, in some embodiments, multiple antennas may be directly or indirectly connected to a processing module.In some embodiments, an antenna is paired with an RF module such that each RF module can control the reception or transmission of any RF signal by a single antenna, allowing each antenna to operate independently of the other antennas. Additional information regarding the hardware components is described herein with reference to Figures 1B and 3. Figure 11 illustrates an exemplary flow 1100 for training an RF AI / ML model according to various embodiments of the invention. Flow 1100 is generally related to collecting and inputting raw RF signal data (e.g., filtered and / or sampled raw RF signal data and / or a subset thereof) into an RF AI / ML model to train the model to produce outputs such as predicted categories and probabilities. Although the following description of blocks 1102 to 1116 describes an exemplary RF system as an implementation block, one or more of blocks 1102 to 1116 can be implemented by any of one or more systems or devices (e.g., sensors, devices, antennas, servers, or one or more RF systems).

[0297] At block 1102, raw RF signal data may be collected by one or more systems (e.g., sensors, devices, antennas, one or more RF systems) in a region. For example, an RF system (e.g., 102 or 106) may use one or more of its corresponding broadband antennas to detect and record raw RF signal data. In some embodiments, the broadband antenna may be configured (e.g., via hardware) or tuned (e.g., via software) to detect frequencies within a specified range. In some embodiments, the broadband antenna may be configured or tuned to detect all RF frequencies. The raw RF signal data used for model training may be RF signal data associated with a known transmitter or transmitter class such that the RF signal data can be associated with a desired output (e.g., a known transmitter or transmitter class and / or characteristics associated with a known transmitter or transmitter class).

[0298] At block 1104, the RF system can be configured to filter the raw RF signal data collected from block 1102. For example, in some embodiments, the RF system can remove RF signals from the collected raw RF signal data that correspond to RF signals associated with friendly devices (devices that have been manually or automatically marked as friendly), or filter the raw RF signal data based on a whitelist and / or blacklist (e.g., manually and / or automatically filled in). In some embodiments, block 1104 may be optional and, in some embodiments, or for some RF systems in a network, filtering may not be present. For example, one of the first RF systems in an area may be directed towards a friendly device emitting one or more RF signals. The first RF system may exclude / filter one or more RF signals from the raw RF signal data collected at block 1102. In some embodiments, filtering may involve complete or partial suppression of one or more of the raw RF signal data or any of the raw RF signal data that has been processed in any way (e.g., by sampling or the like). For example, complete or partial suppression of one or more patterns may include removing a specific RF signal from the original RF signal data (e.g., as described in this paragraph or elsewhere in this document) or an attribute associated with a specific RF signal (e.g., a portion or similar of an RF signal corresponding to noise or low-quality data).

[0299] In some embodiments, the RF system may include hardware and / or software filtering techniques. For example, there may be nearby devices in which an antenna corresponding to an RF system is directed toward transmitting signals within a specific frequency range, and the antenna may be (1) designed / configured to operate in certain frequency bands (e.g., excluding specific RF bands), (2) programmed (e.g., using software) to filter the received signal so as not to interfere with the analysis of the received signal, and / or (3) (e.g., using software or additional digital signal filtering devices) to filter the transmitted signal to minimize or remove interference to the operation of nearby devices.

[0300] At block 1106, the RF system may (depending on whether any filtering is implemented by the specific RF system) sample the raw RF signal data received from block 1102 or 1104. In some embodiments, sampling at block 1106 may occur before, or vice versa, filtering at block 1104, or simultaneously. In some embodiments, very large amounts of data are collected very quickly, and processing such large amounts of data (e.g., using an RF AI / ML model) may require significant energy, processing power, and / or time. In practice, an RF system should be able to detect an object within seconds, identify a signal associated with that object, and transmit a signal toward that object (e.g., see Figures 13-15). Any delay can lead to poor performance and unreliability of the RF system. Therefore, sampling of the raw RF data can be used to reduce the amount of data processed by the RF AI / ML model while maintaining a high quality (e.g., due to the unchanging signal data within this short time). Therefore, in some embodiments, the sampling of raw RF signal data may vary based on several factors: (1) the hardware or processing limitations of the RF system (e.g., processing power as a product of local temperature or hardware selection), (2) the quantity / quality of the collected raw RF signal data to be processed (e.g., fewer samples would be required if a large amount of raw RF signal data is filtered at block 1204), and any other factors affecting the processing speed of the RF system. The application of RF AI / ML models may require significant processing power to process all collected data, and sampling can provide a balance between reducing computational demands, maintaining system performance, and providing accurate results. For example, the RF system may sample the raw signal within the same time period (e.g., the same number of milliseconds, such as 1 ms, 2 ms, 3 ms, 5 ms, 10 ms, 50 ms, or the same other time period). In various embodiments, other or additional sampling methods may be employed. For example, in some embodiments, sampling of raw signal or raw signal (e.g., RF) data may include any form of data sampling, such as: (1) probability-based sampling (e.g., a method of using random numbers corresponding to points in a dataset to ensure that there is no correlation between points selected for the sample), such as system sampling (e.g., generating a sample by setting a time interval or time step at which it extracts data from a larger group (e.g., which may be automatically pre-configured or determined by the system) (e.g., selecting all raw RF signal data every 1 ms, 5 ms, 10 ms, 20 ms, 50 ms and / or the like), or (2) non-probability-based sampling (e.g., a method of determining and extracting a data sample based on an analyst's judgment), or (3) a combination thereof. In some embodiments, for example and as mentioned above, the sampling of raw RF signal data may vary based on multiple factors (e.g., hardware or processing limitations of the RF system, or the quantity or quality of raw RF signal data collected and / or the like).In some embodiments, a time interval or time step may be determined automatically (e.g., in real-time) by a specific RF system based on one or more of a plurality of factors. In some embodiments, the time interval or time step may be manually pre-configured. In some embodiments, a maximum or minimum time interval or time step may be pre-configured, wherein each RF system can automatically determine one of the actual time intervals or time steps within a range. For example, an operator may manually or the RF system may automatically set the time interval or time step to a range of 0 ms (e.g., no sampling) to 15 ms (e.g., or any other time interval).

[0301] At block 1108, the RF system can generate a spectrum based on raw RF signal data (e.g., signal data received at block 1102) and / or based on sampled raw RF signal data (e.g., signal data sampled at block 1106). The spectrum can describe and / or display the raw or sampled raw RF signal data varying according to frequency, time, and / or intensity. For example, in some embodiments, a Fourier transform can be used to generate the spectrum based on time-domain RF signal data.

[0302] At block 1110, the RF system can receive annotations or markings in the spectrum corresponding to a signal of interest occurring within a time period. These annotations or markings can indicate various characteristics that can be used for AI / ML model training, including but not limited to individual frequency components of the signal of interest, combinations of individual frequency components, the intensity of individual frequency components, the relative intensity among different frequency components, and / or any temporal variations associated with the signal of interest, such as temporal variations in the intensity of a signal or its frequency components, gradual or periodic variations of a signal or its frequency components, and / or similar variations.

[0303] At block 1112, the RF system may filter or generate a subset of the original RF signal data corresponding to the signal of interest based on the annotations or tags applied at block 1110. In some embodiments, filtering may include removing portions of the signal data (e.g., signal noise and / or signal components) that are not intended for use in training the AI / ML model. Filtering may further include selecting a time-based segment of the annotated spectrogram (e.g., a segment of 0.5 ms, 1 ms, 2 ms, and / or the like). In some embodiments, filtering may include removing from the original RF signal data one or more of the following RF signals: (1) one or more baseline RF signals, (2) one or more previously identified RF signals, (3) one or more RF signals that are already identifiable or otherwise known, (4) RF signals associated with friendly devices, (5) RF signals associated with devices that have been manually or automatically marked as friendly, or (6) a pre-configured whitelist or blacklist.

[0304] At block 1114, the RF system can input a subset of the raw RF signal data (e.g., a subset obtained by filtering at block 1112) into a machine learning model (e.g., in the RF machine learning component 1012 of Figure 10) (such as an RF AI / ML model) for training the machine learning model. The subset of the raw RF signal data can be associated with additional data (such as the spectrogram or a portion thereof generated at block 1108, and / or data corresponding to annotations or tags received at block 1110).

[0305] At block 1116, RF machine learning component 1012 trains a machine learning model. Training at block 1116 can train the machine learning model to generate output based on subsequent signals. For example, the machine learning model can be trained to analyze subsequent signals and output a predicted category corresponding to the transmitter receiving the signal and an associated probability of identifying a specific category of transmitters for a signal of interest. In another example, the machine learning model can be trained to output data corresponding to RF signal data as a function of time corresponding to the time period during which the signal of interest was identified as occurring. For example, complex data associated with the signal of interest can be compiled into a segment of data corresponding to the time period during which the signal of interest occurred. For example, a signal of interest may have been detected for 3 seconds, and complex data associated with that original RF signal data can be output along with the predicted category and probability for subsequent use when the machine learning model is applied to new data (e.g., as described in more detail in Figure 12). In some embodiments, training at block 1116 may be supervised training in which the input at block 1114 is associated with a known transmitter or transmitter category or one or more of its characteristics, such that the machine learning model is trained to associate the characteristics of a subset of the raw RF signal data input at block 1114 with the correct transmitter or transmitter category. Method 1100 may be repeated any number of times based on a large number of RF signal data samples and / or subsets to iteratively train the machine learning model to accurately classify transmitters or other objects based on RF signal emissions detected in subsequent operations of the system.

[0306] Figure 12 illustrates an exemplary process 1200 for applying a trained RF artificial intelligence / machine learning model according to various embodiments of the present invention. Process 1200 generally relates to: collecting raw RF signal data (e.g., filtered and / or sampled) and inputting it into an RF AI / ML model; receiving the category and probability output from the RF AI / ML model; and then identifying an object type based on the category and probability. Although the following description of blocks 1202 to 1214 describes an exemplary RF system as an implementation block, blocks 1202 to 1214 can be implemented by any of one or more systems or devices (e.g., sensors, devices, antennas, servers, one or more RF systems).

[0307] At block 1202, raw RF signal data may be collected or received by one or more systems (e.g., sensors, devices, antennas, one or more RF systems) in an area. For example, an RF system (e.g., 102 or 106) may use one or more of its corresponding broadband antennas to detect, receive, and / or record raw RF signal data. In some embodiments, a broadband directional antenna may be configured (e.g., via hardware) or tuned (e.g., via software) to detect frequencies within a specified range. In some embodiments, a broadband directional antenna may be configured or tuned to detect all RF frequencies. In some embodiments, a broadband directional antenna may be selectively activated. For example, each broadband directional antenna (e.g., associated with an RF system) may be configured to be turned on or off by the associated system, and / or the associated system may determine or select to receive or use signals from specific individuals or groups of directional antennas. For example, each broadband directional antenna may be in a specific location and / or facing a specific direction in which RF signal detection is not required (e.g., a friendly device may be transmitting a signal in an area where no target object or similar object is detected), and the associated system will be able to turn off or on or selectively receive or use signals from this(e.g.) broadband directional antenna.

[0308] At block 1204, the RF system can be configured to filter the raw RF signal data collected from block 1202. For example, in some embodiments, the RF system can remove RF signals from the collected raw RF signal data that correspond to RF signals associated with friendly devices (devices that have been manually or automatically marked as friendly), or filter the raw RF signal data based on a whitelist and / or blacklist (e.g., manually and / or automatically filled in). Block 1204 may be optional, and in some embodiments, or for some RF systems in a network, filtering may not be present. For example, one first RF system in an area may be directed towards a friendly device emitting one or more RF signals. The first RF system can exclude / filter one or more RF signals from the raw RF signal data collected at block 1202. In some embodiments, filtering may include removing from the original RF signal data one or more of the following RF signals: (1) one or more baseline RF signals, (2) one or more previously identified RF signals, (3) one or more RF signals that are already identifiable or otherwise known, (4) RF signals associated with friendly devices, (5) RF signals associated with devices that have been manually or automatically marked as friendly, and (6) a pre-configured whitelist or blacklist.

[0309] In some embodiments, the RF system may include hardware and / or software filtering techniques. For example, there may be nearby devices in which an antenna corresponding to an RF system is directed toward transmitting signals within a specific frequency range, and the antenna may be (1) designed / configured to operate in certain frequency bands (e.g., excluding specific RF bands), (2) programmed (e.g., using software) to filter the received signal so as not to interfere with the analysis of the received signal, and / or (3) (e.g., using software or additional digital signal filtering devices) to filter the transmitted signal to minimize or remove interference to the operation of nearby devices.

[0310] At block 1206, the RF system may (depending on whether any filtering is implemented by the specific RF system) sample the raw RF signal data received from blocks 1202 or 1204. In some embodiments, sampling at block 1206 may occur before, or vice versa, filtering at block 1204, or simultaneously. In some embodiments, very large amounts of data are collected very quickly, and processing such large amounts of data (e.g., using an RF AI / ML model) may require significant energy, processing power, and / or time. In practice, an RF system should be able to detect an object within seconds, identify a signal associated with that object, and transmit a signal toward that object (e.g., see Figures 13-15). Any delay can lead to poor performance and unreliability of the RF system. Therefore, sampling of the raw RF data can be used to reduce the amount of data processed by the RF AI / ML model while maintaining a high quality (e.g., due to the unchanging signal data within this short time). Therefore, in some embodiments, the sampling of raw RF signal data may vary based on several factors: (1) the hardware or processing limitations of the RF system (e.g., processing power as a product of local temperature or hardware selection), (2) the amount of collected raw RF signal data to be processed (e.g., less sampling would be required if a large amount of raw RF signal data is filtered at block 1204), and any other factors affecting the processing speed of the RF system. The application of RF AI / ML models may require significant processing power to process all collected data, and sampling can provide a balance between reducing computational demands, maintaining system performance, and providing accurate results. For example, the RF system may sample the raw signal within the same time period (e.g., the same number of milliseconds, such as 1 ms, 2 ms, 3 ms, 5 ms, 10 ms, 50 ms, or the same other time period). In various embodiments, other or additional sampling methods may be employed. For example, in some embodiments, sampling of raw signal or raw signal (e.g., RF) data may include any form of data sampling, such as: (1) probability-based sampling (e.g., a method of using random numbers corresponding to points in a dataset to ensure that there is no correlation between points selected for the sample), such as systematic sampling (e.g., generating a sample by setting a time interval for extracting data from a larger population (e.g., selecting all raw RF signal data every 1 ms, 5 ms, 10 ms, 50 ms and / or similar), or (2) non-probability-based sampling (e.g., a method of determining and extracting a data sample based on an analyst's judgment), or (3) a combination.

[0311] At block 1208, the raw RF signal data (e.g., filtered at block 1204 and / or sampled at block 1206) is input into a machine learning model (such as an RF AI / ML model). For example, the machine learning model may be the same as that described elsewhere in this document (such as in Figure 11) regarding the training model.

[0312] At block 1210, the RF system receives output from a machine learning model (e.g., an RF AI / ML model) that includes predicted categories and probabilities. In some embodiments, each category corresponds to a signal or object type that the machine learning model is trained to identify, and the probability indicates the likelihood that a signal analyzed from the raw RF signal data (e.g., as input to the machine learning model at block 1208) corresponds to an RF signal or object type that the machine learning model has previously trained to identify (e.g., using the methodology described herein and / or with respect to FIG11). In some embodiments, the methodology may stop here and may output categories and probabilities for the RF system or any other system or device implementing the machine learning model as data to identify an object associated with a specific signal of interest identified in the collected raw RF signal data. For example, block 1213 in FIG13 reveals the determination of additional features associated with a signal of interest identified in the raw RF signal data corresponding to a first object type applicable here.

[0313] At block 1212, the RF system identifies an object type associated with one or more signals of interest identified in the collected raw RF signal data, based on the output of one of the machine learning models (e.g., the category and probability from the machine learning model output at block 1210). In some embodiments, method 1212 may implement a methodology similar to that described with respect to block 1213 in FIG13, which reveals the determination of additional features associated with the signal of interest identified in the raw RF signal data corresponding to a first object type applicable here. Additionally, in some embodiments, a lookup table may be implemented and used to cross-reference the signal of interest with a database including information about known devices / objects and corresponding signal ranges, such that the identified signal of interest can be matched with one or more known devices / objects in the lookup table. In some embodiments, the lookup table may be proprietary, public, or both, or generated using information from public or proprietary resources. For example, a specification table may be used to supplement the data in the lookup table. Also, for example, identifications confirmed by a machine learning model may be added to the lookup table. The lookup table can be updated automatically (by using an API that connects to the manufacturer's or third-party server) or manually, either periodically or semi-periodically.

[0314] At block 1214, the RF system may, as appropriate (e.g., based on the location of the associated directional antenna), determine the location of an object associated with one or more signals of interest identified in the collected raw RF signal data. For example, if an antenna faces north and detects an object (e.g., by collecting RF signal data associated with the object), the RF system may determine that the object is located (or positioned) to the north of the RF system. In some embodiments, a more precise location or position of an object may be determined using a series of RF systems and corresponding antennas. In some embodiments, the signal strength received by various antennas may be additionally used to determine the object's location or position. This location or position may be determined over time, allowing it to be determined that the object has moved. In some embodiments, the RF system may also transmit the location and / or movement information associated with the object to another external system, such as a second RF system, an additional system or sensor (e.g., 104), and / or a central processing server (e.g., 107). For example, such external systems may then initiate or implement an appropriate response based on the detection provided by the first RF system. Furthermore, for example, such external systems can be used to determine a more precise location or movement information associated with an object. Additionally, as mentioned above, further examples of communication and coordination with one or more external or other systems, devices, and / or sensor components are provided, for example, in '059 and '824 disclosures. For example, any information relating to detection, tracking, identification, determination, or the like performed by an RF system (e.g., and / or other RF systems) can be provided to one or more external or other systems, devices, and / or sensors, enabling one or more external or other systems, devices, and / or sensors to work independently and / or with each other (e.g., including with the originating RF system (and / or other RF systems)) to initiate one or more responses, such as intercepting an object (e.g., as described in '824 disclosure) and / or making one or more determinations.

[0315] Figures 13 to 15 are flowcharts illustrating exemplary methods and functionalities of one or more RF systems according to various embodiments of the present invention. The blocks in the flowcharts illustrate exemplary embodiments, and in various other embodiments, various blocks may be reconfigured, selected, and / or omitted, and / or additional blocks may be added. The terms and concepts described in Figures 13 to 15 are similar to and related to those described in Figures 10 to 11 and elsewhere herein, and are intended to provide additional clarification and understanding of Figures 13 to 15. In various embodiments (e.g., with respect to Figures 13 to 15), various forms of functionality may be performed substantially immediately; for example, received data may be processed upon its reception. Alternatively, (e.g., with respect to Figures 13 to 15) various forms of functionality described may be performed in batches and / or in parallel and / or by multiple systems or devices. In some embodiments, for example, the exemplary software components of Figures 13 to 15 may be incorporated into or implemented by the RF systems 102 and 106 of the present invention (e.g., including any software components (such as those described with respect to Figure 1C) and / or hardware components (such as those described with respect to Figure 1B)). For example, in some embodiments, machine learning algorithms may be trained or applied using GPU(s) 138 and / or other components of processing module(s) 130. Additionally, in some examples, for example, an RF signal may be generated by an SDR transceiver 139 and then transmitted to RF module 131 before being transmitted via one or more directional antennas 122. Additionally, in some examples, an RF signal may be received by one or more directional antennas 122 and then transmitted through RF module 131 before being processed by processing module 130 (e.g., by applying a machine learning algorithm or model, for example, using GPU 138). To ensure that once an RF signal is transmitted, it is in the appropriate direction or via an appropriate directional antenna, each RF system can use a PNT component 140 to determine characteristics or properties associated with the location of the corresponding RF system, orientation, elevation / altitude, and the like. This information from the PNT component 140, or received data from the PNT component 140 of another RF system, can be used to determine which directional antenna(s)(s) to activate and from which to transmit or use to track an identified object. For example, the PNT component is an important component in determining the relevant location and orientation information of each and all RF systems in a network. Furthermore, in some embodiments, multiple antennas may be directly or indirectly connected to a processing module. In some embodiments, an antenna is paired with an RF module such that each RF module can control a single antenna to receive or transmit any RF signal, allowing each antenna to operate independently of the other antennas. Additional information regarding the hardware components is described herein with reference to Figures 1B and 3.

[0316] Figure 13 illustrates an exemplary process 1300 for transmitting and tracking an object using, for example, an RF system 1301. Process 1300 generally relates to: identifying a first set of signals corresponding to the first object while tracking its movement and (e.g., in the direction of the first object) transmitting a second set of signals. According to various embodiments of the invention, process 1300 can also be coordinated among multiple connected systems and devices. For example, data can move between a first RF system (e.g., 102), one or more additional RF systems (e.g., 106), additional systems or sensors (e.g., 104), and / or a central processing server (e.g., 107). Data can also be provided by a user via a user device (e.g., 110).

[0317] At block 1302, an RF system 1301 may receive updates to a machine learning model (e.g., from one or more other systems or sensors, such as 104 in FIG. 1A). For example, in some embodiments, the RF system 1301 may include a machine learning model that can be used to identify one of the object types (e.g., the model described in FIG. 10-11 and elsewhere herein). The machine learning model may be updated or trained using (e.g., FIG. 10) data captured by the RF system 1301, (2) data received from one or more other systems or devices (e.g., 104 or 106 in FIG. 1A), or (3) data received from a central processing server (e.g., 107 in FIG. 1A). In some embodiments, the central processing server may provide packets of data captured from multiple devices. The machine learning model may also be updated or trained based on additional input received from a user (e.g., 110 in FIG. 1A) (e.g., FIG. 10).

[0318] Additionally, at block 1302, and in some embodiments, RF system 1301 may receive information or data packets associated with the identification of a first object. For example, the information or data packets may include information indicating a type of the first object, a location of the first object, a speed of the first object, a confidence score associated with any of the type, speed, or location, or any other relevant information about the first object.

[0319] At block 1302, an RF system 1301 may also receive information regarding the identification of a first object. For example, the RF system 1301 may receive data from one or more other systems (e.g., 106 or 107) indicating the location of the first object, including the estimated size, distance, speed, type, or other relevant information of the object (if available and / or already determined). Furthermore, for example, the RF system 1301 may receive data that assists it in detecting the first object, tracking the first object, determining the object type associated with the first object, and / or determining the RF signal used for transmission (e.g., based on an object type corresponding to the first object).

[0320] At block 1303, RF system 1301 may implement or enable a search mode as appropriate. For example, in some embodiments, the RF system (e.g., 102, 106, 1301) may perform a search function or include functionality for a search mode. The software components may be adapted to optimize the RF system for searching for: (1) a specific frequency or a set of frequencies; (2) several specific regions or locations in three-dimensional space; (3) one or more identified objects; and (4) and / or the like. Regarding (1), in some embodiments, an RF system may use one or more antennas to monitor several specific frequencies. For example, in one scenario, an object type may be identified (e.g., it may be associated with a specific frequency) but its location is unknown. By utilizing some or all of the antennas facing or around an area to collect and process frequencies only in (a few) specific frequencies (e.g., similar to the filtering of raw signal data by block 1204), an RF system (and in some cases, in conjunction with other RF systems) can more efficiently identify (a few) specified frequencies by processing only the signals corresponding to (a few) specific frequencies. Regarding (2), in some embodiments, information relating to an object may be received / determined (e.g., information suggesting that an object may enter or be within the range of an RF system) and transmitted to one or more RF systems. For example, information relating to an object may be received from other systems and / or sensors that may include other RF systems. One or more RF systems may activate antennas facing or near the area where the object is located to monitor the object. As in (2) but regarding (3), in some embodiments, information relating to the actual or approximate location of an object may be received / determined (e.g., a sensor or camera may detect an object but may otherwise fail to identify it or continue tracking it) and transmitted to one or more RF systems. One or more RF systems may activate antennas facing or close to areas of the object located therein to monitor the object.

[0321] In some embodiments, all antennas used for the entire RF system in a network or area may be monitored. However, in some embodiments, depending on a threat level determined, the antennas may be placed in a low-power mode or a high-power mode. In some embodiments, for example, a high-power mode may be activated for some antennas based on information indicating that an object is nearby and needs to be identified. However, in some embodiments, for example, a low-power mode may be activated for some antennas during certain parts of the day or after periods when the collected underlying RF signals remain unchanged.

[0322] At block 1304, RF system 1301 collects a first set of RF signals associated with the first object. For example, the first set of RF signals may be emitted by the first object and / or transmitted to the first object via a transmitter (e.g., a controller configured to control the first object). In some embodiments, RF system 1301 may use one or more antennas pointing in a direction toward the first object to collect the first set of RF signals.

[0323] At block 1306, RF system 1301 can monitor and / or track the movement of a first object. In some embodiments, RF system 1301 may use one or more antennas pointing in a direction toward the first object to determine whether the first object is in a specific location corresponding to one or more antennas and to monitor the movement of the first object (e.g., velocity, acceleration, and / or the like). In some embodiments, RF system 1301 may use a direction finder to determine whether the first object is in a specific location and to monitor the movement of the first object (e.g., velocity, acceleration, and / or the like). In some embodiments, RF system 1301 may combine a direction finder with one or more antennas pointing in a direction toward the first object to determine whether the first object is in a specific location. Depending on the type of hardware used, the tracking or monitoring of a first object can be improved by using multiple devices (e.g., multiple antennas, direction finders and / or the like) from the same RF system 1301 or multiple devices (e.g., 102 and 106) from multiple RF systems, because collecting data from different points of interest can limit the effects of interference and limitations attributable to hardware configuration (e.g., antennas covering a large area such as 90 degrees).

[0324] At block 1308, RF system 1301 inputs data corresponding to the collected first set of RF signals into a machine learning model to determine a first object type corresponding to a first object. In some embodiments, the application of the machine learning model may be based on the steps described with respect to FIG12 or elsewhere herein (e.g., 162 in FIG1C). In some embodiments, the training of the machine learning model may be based on the steps described with respect to FIG11 or elsewhere herein (e.g., 162 in FIG1C). In some embodiments, the machine learning model is stored and accessed locally to improve the efficiency of data transmission. For example, the collection of the first set of RF signals may include a large amount of data that is not feasible to transmit to another device in a timely manner for processing and to provide an output that allows RF system 1301 to efficiently track or transmit. However, in some embodiments, the first set of collected RF signals may also be filtered and / or sampled so that the data can be transmitted to another device or system to assist or process the input data and effectively return the output within a sufficient time to allow the RF system 1301 to complete process 1300 within a desired time frame (e.g., less than 5 seconds, less than 30 seconds, less than 2 minutes, and / or the like) depending on the detected object or object characteristics (e.g., type, size, position, or speed and / or similar) or the location / placement of the specific RF system 1301.

[0325] At block 1310, RF system 1301 determines a first object type corresponding to a first object based on the received output from a machine learning model (e.g., as described in FIG. 12) interacting with it at 1308. In some embodiments, RF system 1301 may also transmit location and / or movement information, as well as any data corresponding to the received output from the machine learning model associated with the first object, to another external system, such as a second RF system, an additional system, or one or more of a sensor (e.g., 104) and / or a central processing server (e.g., 107). For example, such external systems may then initiate or implement an appropriate response based on the detection determination provided by RF system 1301.

[0326] At block 1312, RF system 1301 may determine, as appropriate, additional characteristics associated with the first set of RF signals or the first object type. For example, additional characteristics may include bandwidth, channels, signal rate, or the like. RF system 1301 may determine such additional characteristics based on data received or accessed by itself and / or in conjunction with data from one or more other devices or systems (e.g., 104, 106, 107, or 110 in FIG. 1A).

[0327] At block 1314, RF system 1301 determines a second set of RF signals to be transmitted (e.g., in the direction of the first object). In some embodiments, the second set of RF signals is based on object type (e.g., determined by using a machine learning model) and / or additional features determined at block 1312.

[0328] At block 1316, RF system 1301 generates a second set of RF signals and causes the second set of RF signals to be transmitted using one or more antennas connected to RF system 1301. In some embodiments, RF system 1301 may transmit the second set of RF signals determined at block 1314 to another RF system (e.g., 106 in FIG. 1A) for transmission. In some embodiments, RF system 1301 transmits the second set of RF signals in the direction of the first object. In some embodiments, RF system 1301 transmits the second set of RF signals in all directions surrounding RF system 1301. In some embodiments, RF system 1301 transmits the second set of RF signals in one or more directions surrounding RF system 1301. In some embodiments, RF system 1301 transmits the second set of RF signals while simultaneously tracking the movement of the first object. Advantageously, tracking the position and / or movement of the first object is used to determine whether the antenna used by RF system 1301 to transmit the second set of RF signals is unnecessary and whether it is necessary to use another antenna instead. For example, if a first object moves out of a coverage area associated with a first antenna connected to one of the RF systems 1301 and into a coverage area associated with a second antenna connected to the same RF system 1301, the first antenna should be turned off to save power and / or the second antenna should be turned on to maintain effective transmission in the area or direction of the first object. Similarly, for example, if a first object moves out of a coverage area associated with a first antenna connected to one of the RF systems 1301 and into a coverage area associated with a second antenna connected to a different RF system (e.g., 106), the first antenna should be turned off or deactivated to save power and / or the second antenna should be turned on or activated to maintain effective transmission in the area or direction of the first object. In some embodiments, the first antenna may be turned off or deactivated simultaneously with the second antenna being turned on or activated. In some embodiments, the first antenna is turned off or deactivated once a period of time (e.g., 1 second, 5 seconds, 30 seconds, or similar) has elapsed after the second antenna has been turned on or activated. Additionally, when an RF system (e.g., 1301) begins transmitting a second set of RF signals, the transmission can be initiated after a delay or after a time period (e.g., 1 second, 5 seconds, 30 seconds, or the like), or it can be initiated immediately with a power ramp (e.g., 10% power for one time period, followed by 20% power for another time period and / or the like). For example, in some situations where power stability can be a problem (e.g., in high temperatures, damaged equipment, old equipment, faulty equipment, and / or the like), a power ramp can be useful.

[0329] This document describes in more detail, with respect to Figures 14 and 15, further examples of such switching (e.g., on / off, delayed shutdown, ramping / lowering and / or similar) or coordination between the antennas of one RF system and the antennas of multiple RF systems. Additionally, as mentioned above, further examples of methods for determining the position of an object by communication among various components are provided, for example, in '059 Publication and '824 Publication.

[0330] In some embodiments, blocks 1314 and / or 1316 may not occur, resulting in no RF signal transmission, and alternatively, another system or device may initiate a response based on the detection and / or identification of the first object.

[0331] Figure 14 illustrates an exemplary flow 1400 for coordinating the application of an RF artificial intellig...

Claims

1. A computer implementation method for applying a machine learning model to identify one or more RF signals, the computer implementation method comprising executing program instructions from one or more hardware processors: receiving raw RF signal data through two or more directional antennas corresponding to a first RF system, wherein the two or more directional antennas are configured to be selectively activated, wherein the first RF system includes a processing module, and wherein the processing module includes a machine learning component; sampling the raw RF signal data by the processing module of the first RF system to generate sampled RF signal data; transmitting the sampled RF signal data to the machine learning component for input to a machine learning model; receiving an output from the machine learning model from the machine learning component; and identifying a type of object based on RF signal attributes and the output, wherein the RF signal attributes include bandwidth, channel, and signal rate each associated with the raw RF signal data.

2. The computer implementation method of claim 1 further includes, by executing program instructions, one or more hardware processors: selecting to activate one or both of the two or more directional antennas for receiving the raw RF signal data, wherein the two or more directional antennas include broadband directional antennas.

3. The computer implementation method of claim 2 further includes, by means of one or more hardware processors executing program instructions: determining the position of one of the objects based on the positioning of the two or more directional antennas.

4. The computer implementation method of claim 1 further includes filtering the raw RF signal data by means of one or more hardware processors executing program instructions.

5. The computer implementation method of claim 4, wherein the filtering includes complete or partial suppression of the original RF signal data, the sampled original RF signal data, or one or more samples of a subset of the RF signal data.

6. The computer implementation method of claim 4, wherein the filtering includes removing from the original RF signal data one or more of the following RF signals: (1) RF signals associated with friendly devices, (2) RF signals associated with devices that have been manually or automatically marked as friendly, or (3) RF signals that have been pre-configured as whitelists or blacklists.

7. The computer implementation method of claim 1, wherein the sampling of the original RF signal data includes sampling the original RF signal data at a preconfigured timestep.

8. The computer implementation method as described in request item 7, wherein the pre-configured time step is between 0 ms and 15 ms.

9. The computer implementation method of claim 7, wherein the pre-configured time step is further based at least in part on hardware components associated with one of the RF systems implementing the method.

10. The computer implementation method of claim 1, wherein the output includes predicted classes and probabilities corresponding to a subset of the RF signal data from the sampled RF signal data.

11. The computer implementation method of claim 10, wherein the identification of the type of the object is further based on the bandwidth, channel, and signal rate associated with the respective subset of RF signal data.

12. The computer implementation method of claim 1, wherein the output of one of the machine learning models includes a predicted category and probability corresponding to one or more RF signals identified by the machine learning model.

13. The computer implementation method of claim 1, further comprising training the machine learning model, wherein training the machine learning model includes: A first subset of RF signal data corresponding to a signal of interest is generated, at least in part, based on annotations corresponding to one of the signals of interest contained in a spectrogram, wherein the spectrogram is generated based on original RF signal training data; and the first subset of RF signal data used to train the machine learning model to identify the signal of interest is input into the machine learning model.

14. The computer implementation method of claim 13, wherein the spectrogram includes training data of the original RF signal that varies according to frequency, time, and / or intensity.

15. The computer implementation method of claim 13, wherein the annotations further correspond to a time period in which the signal of interest exists on the spectrogram.

16. The computer implementation method of claim 13, wherein the annotations further correspond to one or more frequencies or bands associated with the signal of interest.

17. The computer implementation method of claim 13, wherein the generation of the first subset of RF signal data includes at least a portion of the original RF signal training data from which portions that are not of the signal of interest are removed.

18. The computer implementation method of claim 1, wherein the raw RF signal data is sampled at a rate based on at least one of the following: (1) the temperature of one or more hardware processors, or (2) the quantity of one of the raw RF signal data.

19. A system for applying a machine learning model to identify one or more RF signals, comprising: A computer-readable storage medium having program instructions embodied therewith; and one or more processors configured to execute the program instructions to cause the system to perform the computer implementation method as requested in item 1.

20. A computer program product comprising a non-transitory computer-readable storage medium having program instructions embodied therewith, the program instructions being executable by one or more processors to cause the one or more processors to perform the computer implementation method as claimed in claim 1.

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