System and method for co-channel interference mitigation using adaptive beamforming

Abstract

Methods and systems are described herein for adaptive beamforming and interference mitigation using a time synchronized distributed sensor array system ("distributed system"). The distributed system may be a software-defined radio using multi-input-multi-output antenna elements. The distributed system includes multiple sensor nodes which are time synchronized using status information from a sensor node (e.g., timestamp of an occurrence of an event such as receipt of a request for local timestamp, a receipt of a calibration signal). The synchronized distributed system enables the generation of an adaptive beam profile that directs highdirectionality beams toward desired targets while directing spatial nulls toward interference sources. Further, the distributed system makes use of a scalable nonuniform multidimensional array to provide telecommunication and electromagnetic wave manipulation operations for applications of varying scale.

Description

[0001] SYSTEM AND METHOD FOR CO-CHANNEL INTERFERENCE MITIGATION USING ADAPTIVE

[0002] BEAMFORMING

[0003] BACKGROUND

[0004]

[0001] With frequency spectrum becoming ever more crowded in both military and commercial domains, interference from a broad range of sources necessitates adaptive nulling and cancellation of interference signals in a variety of applications ranging from communications (wireless, cellular, satcom, and others), radar, electronic sensing, electronic warfare and any other radio frequency application where a signal must be received, and a possible interference source is present. Examples include interference by 5G technology, ubiquitous throughout the telecommunications industry, interfering with other C-band applications such as SATCOM ground systems, weather radar, 5 GHz Wifi and other applications which share spectrum with 5G. Their relatively high power and proximity to ground systems with which satellites are attempting to communicate results in significant interference; thereby reducing the ability to effectively communicate in these bands. Other applications where interference nulling is applicable is in electronic warfare. Electronic warfare has become an increasingly important aspect of military operations. Adversarial forces can disrupt a warfighting function’s capabilities by directing jamming operations at critical communications systems. By using spatially oriented jamming systems, adversaries can interrupt the flow of communications between satellites and terrestrial systems. This intentional jamming can exacerbate the existing interference caused by incumbent telecommunications systems. Adaptive nulling is a known technology for monostatic, monolithic phased arrays, however, as sensors and transceivers have become distributed, a distributed version of these adaptive techniques has not been done previously.

[0005]

[0002] In military contexts (e.g., electronic warfare), adversaries often use jamming to disrupt satellite communications, which are vital for command and control, intelligence gathering, and coordination of forces. Further, an adversary can change their jamming strategies dynamically, making it difficult for adaptive beamforming algorithms to compensate effectively. Accordingly, adversarial jamming poses a significant threat to beamforming communication systems, affecting signal quality and system performance. Thus, developing effective countermeasures and adaptive strategies to unintentional (e.g., ambient RF noise, signals from incumbent communications systems) and adversarial RF interference is crucial for maintaining reliable communication in the presence of such challenges.

[0003] The impact of interference, adversarial or otherwise, is not only limited to military hardware but can also affect civilian infrastructure that relies on satellite communications for information and services. Accordingly, there is a need for emerging 5G technologies to mitigate signal interference generated by both adversarial and incumbent telecommunication systems in order to more efficiently leverage the increasingly crowded millimeter wave (mmW) frequency band for military and civilian applications.

[0006] SUMMARY

[0007]

[0004] It is an aspect of this disclosure to provide a method for co-channel interference mitigation using adaptive beamforming. The method may include receiving, via a processor, status information for a plurality of transceiver nodes. The method may further include determining, via the processor, an interference profile for an area of interest, wherein the interference profile includes at least one interference source. The method may further include identifying, via the processor, at least one target. The method may further include generating, via the processor, an interference mitigation protocol based on target data, the status information, and the interference profile. The method may further include outputting, via the plurality of transceiver nodes, at least one adaptive beam directed toward the at least one target and at least one mitigation signal in accordance with the interference mitigation protocol, wherein the mitigation signal attenuates the interference source, and wherein the plurality of transceiver nodes forms a distributed nonuniform array.

[0008] BRIEF DESCRIPTION OF THE DRAWINGS

[0009]

[0005] Figure 1 shows a distributed sensor array system, consistent with various embodiments.

[0006] Figure 2 is a block diagram of a sensor node of the distributed system of Figure 1 , consistent with various embodiments.

[0010]

[0007] Figure 3A shows an example of synchronizing the sensor nodes using a calibration signal from a sensor node of the distributed system of Figure 1, consistent with various embodiments.

[0011]

[0008] Figure 3B shows a block diagram of the distributed system of Figure 1 configured to function as a scalable multidimensional array of sensor nodes and antenna elements, consistent with various embodiments.

[0012]

[0009] Figure 4 shows a flowchart of a method for generating at least one adaptive beam directed toward the at least one target and at least one mitigation signal directed toward an interference source, consistent with various embodiments.

[0010] Figure 5A is a first block diagram illustrating the distributed system generating an adaptive beam pattern to direct spatial nulls toward interference sources and track targets with high-directionality beams, consistent with various embodiments.

[0013] [Oil] Figure 5B is a second block diagram illustrating the distributed system generating an adaptive beam pattern to track targets with high-directionality beams, consistent with various embodiments.

[0014]

[0012] Figure 5C is a third block diagram illustrating the distributed system generating an adaptive beam pattern to track targets with high-directionality beams, consistent with various embodiments.

[0015]

[0013] Figure 6 shows a flowchart of a method for generating the adaptive beam pattern by cohering time aligned data signals from sensor nodes of the distributed system of Figure 1 , consistent with various embodiments.

[0016]

[0014] Figure 7A shows a simulation of a typical antenna pattern, consistent with various embodiments.

[0017]

[0015] Figure 7B shows a demonstration of the distributed system performing adaptive antenna nulling at spatial angles of interferes, consistent with various embodiments.

[0018]

[0016] Figure 8 is a block diagram of a radar system implemented using the distributed system of Figure 1, consistent with various embodiments.

[0019]

[0017] Figure 9 shows an augmented reality visualization of the high-directionality beams projecting into space.

[0020] DETAILED DESCRIPTION

[0021]

[0018] In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It will be appreciated, however, by those having skill in the art, that the embodiments of the invention may be practiced without these specific details or with an equivalent arrangement. In other cases, well-known structures and devices are shown in block diagram form to avoid unnecessarily obscuring the embodiments of the invention.

[0022]

[0019] The disclosed concept relates to a system that leverages a software-defined radio (SDR) comprising a number of time aligned antenna nodes to provide a flexible multi-function radio frequency (RF) solution. For example, the system may provide communications, radar, and electronic intelligence (ELINT) capabilities in a rapidly deployable software-defined architecture. In some embodiments, the system leverages machine learning algorithms to enable a distributed sensor array of SDR antenna elements to mitigate, respond to, and potentially implement RF interference and jamming techniques (e.g., frequency hopping jamming, spread spectrum jamming, powerful pulse jamming, smart or adaptive jamming, low probability of intercept (LPI) techniques). For example, the system may implement or respond to jamming operations where the jamming devices rapidly switch frequencies, making it difficult for traditional static-frequency countermeasures to adapt. The system may implement or respond to jamming operations where jammers spread their energy across a wide range of frequencies, effectively diluting the power of the jamming signal but impacting a broader set of frequencies. The system may employ or respond to synchronized short bursts of high-power signals capable of overwhelming receivers (e.g., systems relying on sensitive detection equipment). The system may employ or respond to jamming operations that analyze the target's signal and adapt the jamming strategy accordingly. The system may employ or respond to jamming operations that mimic legitimate signals to create confusion or use selective jamming techniques to target specific communications while leaving others unscathed. The system may employ or respond to jamming operations that use signals with low power levels to remain undetected while still effectively disrupting communications. The system is designed to adapt with continuous advancement in both jamming techniques and beamforming countermeasures used in electronic warfare. The system's ability to rapidly adapt to the changing electronic warfare landscape is designed to both leverage and counteract the integration of advanced technologies such as Al and machine learning. The system may leverage adaptive beamforming techniques to point high-gain directional beams toward satellites of interest while simultaneously creating null beams to cancel 5G interference. These beams can be arbitrarily steered in real time to track the satellite’s transition across the sky based on orbital parameters known a priori and / or gathered in real time. Further, being able to synchronize multiple antenna nodes provides a technical discriminator enabling the system to further distribute receiving elements and create large sparse arrays. In some embodiments the system employs a method for employing a distributed antenna array to generate interference-signal attenuation of any desired signal (e.g., an in-name interferer or an electronic warfare / intentional jamming signals). The system may implement sidelobe cancellation and nulling to bolster interference mitigation operations. In some embodiments, the system may operate in a range from 5Hz to lOTHz.

[0023]

[0020] Figure 1 shows a distributed sensor array system 100, consistent with various embodiments. For example, the distributed sensor array system (“distributed system”) 100 may include several sensor nodes 104a-104n that facilitate the transmission of waveforms as beams in a desired direction. In some embodiments, the distributed system 100 is a phased array system. The sensor nodes include the SDR that is configured to operate in a wide range of radio frequencies (e.g., 1 Hz to 300 GHz). The distributed system 100 may be implemented for various applications. For example, the distributed system 100 may be implemented for surveillance as a radar system, a sonar system, in oil and gas industry for finding energy resources, in mining industry for finding metals, etc. The following paragraphs describe the distributed system 100 configured for transmission and reception of radio frequency (RF) waveforms, but the distributed system 100 is not limited to working with RF waveforms and may he configured to work with other waveforms as well (e.g., acoustic waves, seismic waves, etc.).

[0024]

[0021] A sensor node may be configured to be one of (a) a transmit only sensor node in which case it may transmit waveforms but not receive waveforms, (b) a receive only sensor node in which case it may receive waveforms but not transmit waveforms, or (c) both transmit and receive sensor node in which case it may transmit or receive waveforms. Unless stated otherwise, a sensor node may be both a transmit and receive sensor node. Each of the sensor nodes 104a-104n may be configured to transmit an outgoing waveform (e.g., referred to as a “probe signal”) that may all combine together to form a beam in a particular direction. Each of the sensor nodes 104a-104n may receive a response to the probe signal (e.g., referred to as a “data signal”) that may be “time aligned” and cohered by the distributed system 100 for further processing (e.g., by a third-party system) for one or more applications.

[0025]

[0022] The distributed system 100 time synchronizes the sensor nodes 104a-104n to time align the transmitted probe signals or the data signals received by the sensor nodes. In some embodiments, time aligning the data signals includes applying at least one of a time offset, phase, or amplitude to the data signals such that the data signals of all sensor nodes 104a-104n have the same time offset, phase and amplitude. The distributed system 100 may synchronize the sensor nodes 104a-104n in several ways. In one example, each sensor node may have a corresponding local clock (e.g., a quartz oscillator) and the local clock may be synchronized with a phase lock loop, which is synchronized with an external signal such as (a) an external clock signal that is wired to each receiver, or (b) a wireless external signal such as a GPS signal, an astrological signal (e.g., a quasar signal, the cosmic microwave background signals or other signals from radio astronomy), waveforms from television towers, acoustic waveform, or a calibration signal from a transmitter node in the distributed system 100. In another example, each sensor node’s local clock may be made up of an atomic clock with a low drift (e.g., that may not drift more than a microsecond over the period of days or even months), where the atomic clock for each sensor node may be synchronized and aligned at the factory before the sensor nodes are deployed.

[0026]

[0023] In some embodiments, each of the sensor nodes 104a-104n shares status information with the distributed system 100 (e.g., one or more other sensor nodes) such that any received signals or signals transmitted by the sensor nodes are calibrated and synchronized with each other to synchronize data collection across the distributed system 100. The status information may include, location of interference sources and targets, terrain data, temperature in an environment of the sensor node; location (such as determined by GPS) of the sensor node; calibration metrics such as phase and amplitude offsets of the RF components (or optical components in the case of optics); or a timestamp of an occurrence of an event such as (a) a receipt of a signal (e.g., calibration signal, GPS signal, or any other known waveform) or (b) a receipt of a request for local timestamp of the sensor node. In some embodiments, the distributed system 100 synchronizes each of the sensor nodes 104a-104n with a reference node 106 of the distributed system 100 by computing a time offset between a timestamp of an occurrence of the event at a reference node 106 of the distributed system 100 and a timestamp of an occurrence of the event at the corresponding sensor node. For example, in the event the sensor nodes 104a- 104n are implemented using factory-calibrated atomic clocks, a sensor node of the distributed system 100 (e.g., a central processing node 108) sends a request to each of the sensor nodes 104a-104n, including a reference node 106 of the distributed system 100, for a local timestamp of the corresponding sensor node and obtains a response including the local timestamp (e.g., a time at which the request is received at the corresponding sensor node). The distributed system 100 synchronizes a first sensor node 104a with the reference node 106 by computing a time offset between a reference timestamp of the reference node 106 and a first timestamp of the first sensor node 104a.

[0027]

[0024] In another example where the distributed system 100 is configured to synchronize the sensor nodes 104a-104n using a calibration signal, the distributed system 100 synchronizes each of the sensor nodes 104a-104n with a reference node 106 of the distributed system 100 by computing a time offset between a timestamp of a receipt of a calibration signal at a reference node 106 of the distributed system 100 and a timestamp of receipt of the calibration signal at the corresponding sensor node. For example, the distributed system 100 synchronizes a first sensor node 104a with the reference node 106 by computing a time offset between a timestamp of a receipt of a calibration signal at the reference node 106 and a first timestamp of receipt of the calibration signal at the first sensor node 104a.

[0025] When a probe signal is transmitted or a data signal is received by the sensor nodes 104a-104n, the distributed system 100 (e.g., a central processing node 108) may apply the corresponding time offsets to the probe signals or the data signals of the sensor nodes 104a- 104n to generate time aligned data signals for each of the sensor nodes 104a- 104n.

[0028]

[0026] After the data signals are time aligned, the distributed system 100 coheres the time aligned data signals to generate a combined data signal with a coherent gain such that power level of the combined signal may be a function of the individual time aligned signals being combined. For example, the power level of the cohered signal is a sum of the power levels of the individual time aligned signals of the different sensor nodes. In another example, the power level of the cohered signal is greater than the power levels of any of the individual time aligned signals of the different sensor nodes. In some embodiments, the data signals are cohered by adding the time domain signals together from the different sensor nodes 104a-104n such that the data signals are time aligned and coherently added together. The cohered signal may then be intelligently signal processed by the distributed system 100, or provided to a third-party system, for one or more applications. One such application may include a surveillance application, such as a radar system to determine one or more parameters of an object (e.g., speed and distance of an aircraft) in an environment of the distributed system 100. Another application may include detection of radar pulses. Another application may include digital receive beamforming.

[0029]

[0027] In some embodiments, one of the sensor nodes 104a-104n is designated as a reference node 106, whose clock acts as a reference clock for synchronizing the clocks of the other sensor nodes 104a- 104n. In some embodiments, a central processing node 108 is one of the sensor nodes 104a-104n that is configured to perform various types of processing, such as computing time offsets, generating time aligned data signals, cohering time aligned data signals, etc. In some embodiments, the central processing node 108 and the reference node 106 are the same sensor node. In further embodiments, each individual sensor node is able to perform the requisite processing and coordination operations of the central processing node 108. Accordingly, each sensor node 104a-104n may operate independently without losing the capability to perform adaptive beamforming and interference mitigation operations. Further, while descriptions included herein reference the operations (e.g., time synchronization) being performed by a single sensor node, such as the central processing node 108, the operations may be performed by another sensor node, such as the reference node 106, or by more than one sensor node. For example, any number of sensor nodes may perform the time synchronization or time alignment of data signals in the case where processing is done in a distributed way. Furthermore, time synchronization operations can be performed on a scheduled basis, prior to transmitting a probe signal, or prior to receiving the data signal.

[0030]

[0028] In some embodiments, the sensor nodes 104a-104n may operate independent of each other, may not be physically connected to one another as they can communicate with other entities of the distributed system 100 wirelessly, which enables the distributed system 100 to be not only easily scalable but also to be configured to operate at low frequencies for ultra-long range and high-speed detection while keeping the size the distributed system 100 to minimum, which is a significant advantage over conventional phased array systems. The conventional phased array systems would have been very large or infeasible to implement for low-frequency operations as the size of the antenna is inversely proportional to the transmission / reception frequency, and the circuit boards that would house such antennas would be significantly large that is either difficult or infeasible to manufacture. In the distributed system 100, the sensor nodes 104a-104n can be spaced k / 2 (where X is wavelength of the signal) distance units apart from each other. For example, if the frequency of the waveform transmitted by the sensor nodes 104a-104n is 50MHz, which corresponds to a wavelength of approximately six meters, the sensor nodes 104a-104n may be placed approximately three meters apart from each other. The sensor nodes 104a-104n, including the reference node 106 and the central processing node 108, may be co-located (e.g., located within a specified number of wavelengths of the operating frequency) or may be remotely located (e.g., located beyond the specified number of wavelengths of the operating frequency). For example, the first sensor node 104a and the second sensor node 104b may be co-located, while the reference node 106 may be remotely located. In another example, the first sensor node 104a and the second sensor node 104b may be co-located, while a third sensor node 104c may be remotely located. Regardless of how the sensor nodes 104a-104n are located, the sensor nodes 104a- 104n may be synchronized as long as the location information of the sensor nodes 104a-104n, the reference node 106 or the central processing node 108 is available. For example, as mentioned above, the sensor nodes 104a- 104n may have the capability to self-organize (e.g., share location information such as latitude, longitude, and elevation via the status information) or self-calibrate (e.g., synchronize themselves to the reference node 106). The distributed system 100 may have location information of the reference node 106 and sensor nodes 104a-104n that may be used in determining a time difference in arrival of the calibration signal at the sensor nodes with respect to the reference node 106, which may be further used in determining the time offset between the sensor nodes 104a- 104n and the reference node 106. The sensor nodes may self-calibrate using the factory-calibrated atomic clocks, the calibration signal, or other known waveforms on a scheduled basis, prior to transmitting a probe signal, or prior to receiving a response to the probe signal.

[0031]

[0029] The distributed system 100 may be easily scaled up or scaled down by adding or removing sensor nodes, respectively. Furthermore, since each sensor node 104a-104n may communicate with the reference node 106 or the central processing node 108 directly, all the sensor nodes 104a- 104n are a single hop away from the reference node 106 or the central processing node 108, and any scaling of the distributed system 100 may not result in degradation of the time synchronization accuracy. In some embodiments, by having the sensor nodes distributed widely in space, interferometry data between the sensor nodes may be calculated and increased angle accuracy may be obtained even at low frequencies.

[0032]

[0030] While Figure 1 shows a single cluster of sensor nodes 104a-104n, the distributed system 100 may have several clusters in which each cluster may have several sensor nodes. Different clusters may have different number of sensor nodes or the same number of sensor nodes. In some embodiments, such a configuration enables detection of a moving object at ultra-long range and hypersonic speeds; better angle resolution than available with a single cluster. Also, some clusters may remain completely passive making the location of the cluster difficult to impossible to ascertain without active transmissions. Yet another advantage of having multiple clusters may be that one cluster could be significantly closer in distance to the received signal and suffer much less free space path loss of the signal and thus, get a much stronger signal to share between nodes. In some embodiments, the clusters may be spread over a few hundred meters or distributed throughout a massive geographic region. Each cluster may generate a cohered signal from the time aligned signals of its constituent sensor nodes and the cohered signal from all the clusters may be further cohered to generate a master cohered signal with a coherent gain such that the power level of the master cohered signal is a function of the power levels of the constituent cohered signals of the different clusters. For example, the power level of the master cohered signal is a sum of the power levels of the constituent cohered signals of the different clusters. In another example, the power level of the master cohered signal is greater than the power levels of any of the constituent cohered signals of the different clusters.

[0031] Figure 2 is a block diagram of a sensor node of a distributed system of Figure 1 , consistent with various embodiments. A sensor node (e.g., first sensor node 104a) includes an antenna 202 that facilitates radiation or reception of waveforms when connected to a transmitter or receiver (not illustrated). The antenna 202 may be configured to transmit or receive waveforms of a wide range of frequencies. The first sensor node 104a may include a clock 204 that generates a clock signal for use in synchronizing the operations (e.g., coordinate sequence of actions) of the first sensor node 104a. The clock 204 may be a quartz clock, an atomic clock, or another type of clock.

[0033]

[0032] The first sensor node 104a-104n may include a time synchronization component 208 that synchronizes the clock 204 of the first sensor node 104a in any of a number of ways mentioned above. For example, the time synchronization component 208 synchronizes the clock 204 with an external signal such as an external clock signal that is wired to the first sensor node 104a or a wireless external signal such as a GPS signal or an astrological signal. In another example, the time synchronization component 208 synchronizes the clock 204 to a clock of the reference node 106 using a calibration signal from a transmitter node (additional details of which are described at least with reference to Figures 3-5 below).

[0034]

[0033] The first sensor node 104a includes a digital signal processor (DSP) 206 that is configured to perform various signal processing operations including generating time aligned signals, match filtering received calibration signals or data signals, setting a frequency range of the first sensor node 104a, radar signal processing, etc.

[0035]

[0034] The first sensor node 104a includes an RF chain 210. In some embodiments, the RF chain 210 may be a cascade of electronic components and sub-units which may include any of amplifiers, filters, mixers, attenuators, and detectors. All these components may be combined to serve a specific application (e.g., a radar system for detection of moving objects). One or more of the components (e.g., the DSP 206 and time synchronization component 208) may be implemented using an SDR. The SDR facilitates various functionalities. For example, the SDR may facilitate obtaining of location information of the sensor nodes 104a-104n, the reference node 106, or the central processing node 108 (e.g., using a GPS). In another example, the SDR may facilitate in the generation of time aligned data signals.

[0036]

[0035] Note that one or more components of the first sensor node 104a may be communicatively coupled to another device of the distributed system 100 via a communication module to coordinate its operations. Some or all of the components of the first sensor node 104a may be combined as one component. A single component may also be divided into subcomponents, each sub-component performing a separate method step or method steps of the single component. Any one or more of the components described herein may be implemented using hardware (e.g., a processor of a machine) or a combination of hardware and software. For example, any component described herein may configure a processor 108 to perform the operations described herein for that component. Note that as used herein, processor 108 and central processing node 108 are used interchangeably to indicate the same processing portions of system 100.

[0036] Figure 3A shows an example of synchronizing the sensor nodes 104a-104n using a calibration signal 304 from a transmitter node 302 of the distributed system 100. The calibration signal 304 may be any of a wide range of frequencies (e.g., 50 MHz, 144 MHz, 30GHz, etc.). Because the time synchronization is not limited to being performed using a calibration signal, it can be performed in a number of ways as mentioned above at least with reference to Figure 1. For example, the sensor nodes 104a- 104n may be time synchronized using an external wireless signal such as a calibration signal 304 that is of a known waveform, such as a GPS signal, an astrological signal, seismic signal, acoustic signal, a signal transmitted from a transmitter (e.g., signal from television towers), or a signal transmitted from a transmitter node of the distributed system 100. In another example, time synchronization may be achieved by using factory-calibrated atomic clocks in the sensor nodes 104a-104n.

[0037]

[0037] Figure 3A is a block diagram of time synchronization of sensor nodes in the distributed system of Figure 1, consistent with various embodiments. The transmitter node 302 may be co-located with the sensor nodes 104a-104n or may be remotely located. In some embodiments, the transmitter node 302 is considered to be co-located with the sensor nodes 104a-104n if the transmitter node 302 is within a specified proximity (e.g., a specified number of wavelengths of the calibration signal 304) of the sensor nodes 104a- 104n. For example, if the transmitter node 302 frequency of transmission is 144 MHz, then the transmitter node 302 is considered to co-located with the sensor nodes 104a-104n if it is within “20”-“50” meters of any of the sensor nodes 104a-104n. If the transmitter node 302 is beyond the specified proximity (e.g., beyond 50m for 144MHz frequency) of the sensor nodes 104a-104n, then the transmitter node 302 is considered to be remotely located. In some embodiments, the transmitter node 302 can even be located beyond the horizon in the case where the transmitter is quite powerful (e.g., hundreds or thousands of watts per transmit power amp with multiple transmit antennas that create a transmit phased array, and where the transmit frequency is at 50 MHz). Further yet, the transmitter node 302 may also be configured to be mobile, in motion or moving. In some embodiments, by having the transmitter node 302 being remotely located with respect to the sensor nodes, and being in motion, a "no probability of detection" sensor system may be established (e.g., because the transmitter node 302 is not co-located with the receiver sensor nodes, an adversary may not geo-locate the receiver sensor nodes by using the transmitter signal).

[0038]

[0038] Regardless of whether the transmitter node 302 is co-located or remotely located, the transmitter node 302 is located in a known location relative to the sensor nodes 104a-104n, and the calibration signal 304 may be “seen” (e.g., calibration signal 304 is above the noise) or received by the sensor nodes 104a-104n without the need for signal processing. For example, the distributed system 100 may know the location information (e.g., latitude, longitude information) of the transmitter node 302. Such a configuration provides the flexibility of having the transmitter node 302 at any of various locations, and also eliminates the need for the sensor nodes 104a-104n to be in line of sight with each other.

[0039]

[0039] Each of the sensor nodes 104a-104n, including the reference node 106, receives the calibration signal 304 and determines a timestamp of the receipt of the calibration signal 304. The distributed system 100 computes the time offsets of the sensor nodes 104a-104n based on the timestamps of the sensor nodes 104a-104n and the timestamp of the reference node 106 to synchronize the sensor nodes 104a-104n with respect to the reference node 106.

[0040]

[0040] Figure 3B is a block diagram of an embodiment of the distributed system 100 where each sensor node 104a-104n may be a modular accumulator that includes a plurality of antenna elements 306 (e.g., a plurality of isotropic antennas). The central processing node 108 may cohere the data signals and node excitation data wa-wn for each of the sensor nodes 104a- 104n. The node excitation data wa-wn may include a matrix comprising a weight vector representation (e.g., wla-wNa, wlb-wNb, wln-wNn) for each of the plurality of antenna elements 306. Prior to or simultaneous with the cohering, the central processing node 108 may combine the node excitation data wa-wn for the nodes 104a- 104b into a weight vector representation W. Because the sensor nodes 104a-104n may function as an SDR, the weight vector W may be tuned to produce a desired beam output from each of the sensor nodes 104a- 104n. In some embodiments, the central processing node 108 dynamically tunes the weight vector W to produce an adaptive beam pattern 114 (see Figure 5A, 5B, or 5C described below) that includes a plurality of high directionality beams 116 (Figure 5 A) and null beams 118 (Figure 5A) oriented at a plurality of targets 102 (Figure 5A, 5B, or 5C) and interference sources 110 (Figure 5A). In some embodiments, the at least one adaptive beam pattern 114 may include a plurality of read beams (e.g., high directionality beams 116, probe signal 808 (see Figure 8 described below), and / or data signal 810 (see Figure 8 described below)) disposed in a desired spatial configuration within an area of interest 101 (Figure 5A), where each read beam 116 is associated with a corresponding weight vector. Further, weight vector W may be tuned to steer any arbitrary beam within the adaptive beam pattern 114 as desired. In some embodiments, the distributed system is a three-dimensional nonuniform array where a direction vector of the nonuniform array is cohered, via the processor 108, by multiplying a direction vector for each element 104a- 104n of the nonuniform array (system 100) with a reference signal, wherein the reference signal may include the time aligned data signal.

[0041] When the sensor nodes 104a-104n and the reference node 106 (Figure 3 A) receive a data signal (e.g., a response to a probe signal transmitted by the sensor nodes that is reflected off an object such as an aircraft), the sensor nodes 104a-104n and the reference node 106 transmit the received data signal to the central processing node 108. For example, the first sensor node 104a and the reference node 106 transmit the received first data signal and a reference data signal, respectively, to the central processing node 108. The central processing node 108 may then retrieve the first time offset from a storage device (not shown) and apply it to the first data signal to generate a first time aligned data signal of the first sensor node 104a. Similarly, the central processing node 108 may apply the second time offset to the second data signal of the second sensor node 104b to generate a second time aligned data signal of the second sensor node 104b.

[0041]

[0042] In some embodiments, a plurality of transceiver nodes (e.g., sensor nodes 104a- 104n) each outputs a corresponding primary beam 112 (Figure 5A, 5B, or 5C) composed of the output from the plurality of antenna elements 306, wherein each of the plurality of transceiver nodes 104a-104n is disposed to surveil at least a portion of a range and / or angle extent of the at least one target 102 (Figure 5A, 5B, or 5C). In some embodiments, the adaptive beam pattern 114 (Figure 5A, 5B, or 5C) includes a monostatic configuration used to surveil a transmit sector of the at least one target 102. Each of the plurality of transceiver nodes 104a- 104n may be a multi-input-multi-output array. In further embodiments, the adaptive beam pattern 114 includes a bistatic configuration used to surveil at least one of a range and an angle extent of a transmit sector for the at least one target 102. In supplemental embodiments, the adaptive beam pattern 114 includes an isotropic configuration used to surveil an omnidirectional area of interest 101 (Figure 5A) or field of regard 501a (Figure 5B) or 501b (Figure 5C).

[0042]

[0043] In some embodiments, the distributed system 100 (Figure 1) takes a snapshot of each of the sensor nodes 104a-104n at the synchronized timestamp. The snapshot may include a matrix X comprising a corresponding time aligned data signal (xl-xn) for each of the plurality of antenna elements 306. In some embodiments, the central processing node 108 directs the plurality of transceiver nodes 104a-104n to capture a plurality of snapshots of the at least one target 102 (Figure 5A, 5B, or 5C), where each of the plurality of snapshots is captured when a corresponding read beam 116 coincides with the at least one target 102. Accordingly, the central processing node 108 captures snapshots that may characterize the distributed system’s 100 response to target acquisition. Because sensor nodes 104a- 104b may be software-defined radios (SDR), any appropriate time aligned snapshot data may be reproduced without distortion for each of the nodes 104a-104n and their corresponding plurality of antenna elements 306. In some embodiments, the snapshot data may be included in the status information generated by the sensor nodes 104a- 104b. Additionally, the snapshot data may include information relating to interference sources 110 (Figure 5 A - e.g., 5G cell towers, jamming equipment, ambient spectrum congestion, ambient electromagnetic interference) within the area of interest 101 (Figure 5A). Multiple snapshots (xl-xn) of the sensor nodes 104a-104b may facilitate the production of multiple simultaneous read beams 116 within a field of regard 501a (Figure 5B) - 501b (Figure 5C). In some embodiments, the process of cohering the weight vector W to the time aligned snapshot data X results in the generation of the adaptive beam pattern 114 (Figure 5A, 5B, or 5C) that adapts in relation to the time aligned snapshot data X. Accordingly the coherence operations executed by the central processing node 108 uses the corresponding weight vector for each of the plurality of snapshots to form a high-gain received signal (e.g., 116 shown in Figure 5A, 810 shown in Figure 8, etc.).

[0043]

[0044] Example Flowchart(s)

[0044]

[0045] The example flowchart(s) described herein convey example processing operations of methods that enable the various features and functionality of the system as described in detail above. The processing operations of each method presented below are intended to be illustrative and non-limiting. In some embodiments, for example, the methods may be accomplished with one or more additional operations not described, and / or without one or more of the operations discussed. Additionally, the order in which the processing operations of the methods are illustrated (and described below) is not intended to be limiting.

[0045]

[0046] In some embodiments, the methods may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and / or other mechanisms for electronically processing information). The processing devices may include one or more devices executing some or all of the operations of the methods in response to instructions stored electronically on an electronic storage medium. The processing devices may include one or more devices configured through hardware, firmware, and / or software to be specifically designed for execution of one or more of the operations of the methods.

[0046]

[0047] Figure 4 shows a flowchart of a method 400 for employing the sensor nodes 104a- 104n of the distributed system 100 (Figure 1) to perform interference mitigation (e.g., 5G cochannel interference mitigation) using adaptive beamforming. In some embodiments, the sensor nodes 104a-104n are a plurality of transceiver nodes 104a-104n (e.g., a plurality of isotropic antenna arrays). The method 400 may begin by receiving, via a processor (e.g., central processing node 108 shown in Figure 1, DSP 206 shown in Figure 2) status information for the plurality of transceiver nodes 104a-104b (operation 402 shown in Figure 4). The transceiver nodes 104a-104n may be used to gather status information about the area ofinterest 101 (Figure 5 A) and to identify targets within a field of regard 501a (Figure 5B) or 501b (Figure 5C) of the distributed system 100. The method 400 may continue by determining, via the processor 108 (Figure 1), an interference profile for the area of interest 101, wherein the interference profile includes at least one interference source 110 (Figure 5 A) (operation 404). The interference profile may include a covariance matrix comprising all relevant characteristics (e.g., angle or direction of approach, location, frequency, directionality, signal strength) for the at least one interference source 110. Further, the interference profile may contain information gathered from external sources and may be updated based on user preference. For example, the interference profile may specify the adaptive beam pattern 114 (Figure 5A, 5B, 5C) directs null beams and / or spatial nulls 118 (Figure 5 A) toward all but one interference source 110 within the area of interest 101.

[0047]

[0048] The method 400 may continue by identifying, via the processor 108, at least one target 102 (Figure 5A, 5B, 5C) (operation 406). For example, the distributed system may scan the field of regard 501a (Figure 5B) or 501b (Figure 5C) to determine the presence of the target 102 (Figure 5 A, 5B, 5C and also see Figure 6). Additionally, the distributed system may be directed to acquire a target at a known position. The method 400 may continue by generating, via the processor 108, an interference mitigation protocol based on target data, the status information, and the interference profile (operation 408). The interference mitigation protocol may contain the tuning values for W that enable the plurality of transceiver nodes 104a- 104n and the plurality of antenna elements 306 (Figure 3B) to output the desired adaptive beam pattern 114.

[0048]

[0049] Figure 5A illustrates an embodiment where the method 400 may continue by outputting, via the plurality of transceiver nodes 104a-104n, at least one adaptive beam (e.g., high directionality beam 116) directed toward the at least one target 102 and at least one mitigation signal (e.g., null beam 118) directed at the at least one interference source 110 in accordance with the interference mitigation protocol, wherein the mitigation signal (or null beam 118) attenuates the interference source, and wherein the plurality of transceiver nodes 104a-104n forms a nonuniform array (system 100) (operation 410). The term “nonuniform array” may refer to embodiments of the distributed system 100 where the plurality of transceiver nodes 104a-104n is distributed without a uniform distance between the nodes. Accordingly, the distributed system 100 may retain similar operational capabilities with a large number of transceiver nodes 104a-104n distributed within a large region as with a relatively small number of transceiver nodes 104a-104n distributed through the same region. In some embodiments, generating the adaptive beam pattern 114 may employ dynamic frequency allocation adaptive beamforming techniques that can rapidly change frequencies in response to jamming, similar to frequency hopping, to evade jamming attempts. In some embodiments, generating the adaptive beam pattern 114 may employ Al and machine learning cognitive beamforming systems to predict and adapt to jamming strategies more effectively. Further, system 100 may learn from past jamming attempts and continually improve interference countermeasures. Generating the adaptive beam pattern 114 may further include adjusting the power of the transmitted signal to minimize the impact of pulse jamming and managing sensor node 104a-104n power output to maintain effective communication without wasting power resources. Generating the adaptive beam pattern 114 may further include encrypting communications and producing jamming-resistant signals(e.g., through signal modulation or redundant signal production).

[0049]

[0050] Figure 5B and Figure 5C illustrate an embodiment where the distributed system 100 tracks a position of the at least one target 102 along a path of travel that moves through changing fields of regard 50 la-50 lb. To facilitate this functionality, the operation 408 (Figure 4) for generating the interference mitigation protocol may include a subprocess that begins by plotting, via the processor 108 (Figure 1, Figure 3B), a path of travel 502 for the at least one target 102. For example, the at least one target 102 may be a satellite with an orbital path of travel 502 (e.g., polar orbit, walking orbit, sun synchronous orbit, Lagrange point orbit). In some embodiments, the at least one target 102 includes at least one of a fixed satellite service (FSS) and a fixed service (FS) device. Additionally, the distributed system 100 may track terrestrial targets (e.g., vehicles, ships, aircraft, missiles, projectiles, guided munitions). The subprocess may continue by comparing, via the processor 108, the status information and the interference profile to determine appropriate output characteristics for the at least one adaptive beam (e.g., high directionality beam 116 (Figure 5 A)) and the at least one mitigation signal (or null beam 118) along the path of travel 502. For example, a response of a direction vector for a mainlobe 708 (see Figure 7B described below) of the at least one primary beam 112 to a signal at an angle of approach may be adaptively modified as the angle of approach is varied over the area of interest 101 (Figure 5A) or through the field of regard 501a (Figure 5B) or 501b (Figure 5C). In some embodiments, the subprocess may continue by directing, via the processor 108, the plurality of transceiver nodes 104a-104n to output the at least one adaptive beam 116 (Figure 5 A) along the path of travel 502 and the at least one mitigation signal (or null beam 118) (Figure 5 A) as at least one spatial null coincident with a direction of the at least one interference source 110 (Figure 5 A) along the path of travel 502. Accordingly, the distributed system 100 prevents the at least one interference source 110 from undesirably impacting the adaptive beam pattern 114 (e.g., reducing gain, directionality, SNR) regardless of the position, angle of approach, or orientation of the at least one target 102 relative to each element of the distributed system 100. In some embodiments, the spatial null is greater than 50 decibels (dB).

[0050]

[0051] In some embodiments, the distributed system 100 may recruit sensor nodes 104n+l that initially exist outside of the area of interest 101 (Figure 5 A) to track the at least one target 102 as it travels from a first field of regard 501a (Figure 5B) to a second field of regard 501b (Figure 5C). In this embodiment, the plurality of transceiver nodes 104a-104n includes a plurality of active nodes and a plurality of inactive nodes (Figure 5B and Figure 5C). The inactive nodes do not contribute to generating the adaptive beam pattern 114, and the active nodes contribute to beam pattern 114 production. The central processing node 108 (Figure 1, Figure 3B) may elect to reconfigure the group of active nodes to achieve a number of desired objectives (e.g., increase gain or directionality, reduce noise, improve reception, reduce power consumption, limit the possibility of node detection). To achieve this functionality, the central processing node 108 may employ a subprocess that begins by directing a successive series of inactive nodes to become active nodes when the path of travel 502 enters a field of regard 501a- 501b for each corresponding inactive node from the plurality of inactive nodes. The subprocess may continue by modifying, via the processor 108, the interference mitigation protocol based on sensor information received from the plurality of active nodes. The subprocess may continue by deactivating, via the processor 108, a successive series of active nodes when the path of travel exits the field of regard for each corresponding active node from the plurality of active nodes Figure 5C. For example, the number of spatially separated active nodes may be dynamically increased and adaptively combined to generate a large field of regard 501a-501b when the distributed system 100 transitions from a targeted tracking operation to a wide area scanning operation.

[0051]

[0052] As described hereinabove, the distributed system 100 may transmit high directionality beams (beam patterns) 114, 116 to each of the plurality of targets 102 simultaneously (Figure 5A, 5B, and 5C). In some embodiments, the distributed system 100 may include a subprocess that begins by determining, via the processor 108, a target profile for the area of interest 101, wherein the target profile includes the plurality of targets 102, and wherein each of the plurality of targets 102 is associated with a corresponding travel 502 path from a plurality of travel paths. In some embodiments, the target profile includes a matrix comprising all relevant information associated with each of the plurality of targets 102. The subprocess may continue by directing, via the processor 108, the plurality of transceiver nodes 104a-104n to output a first adaptive beam pattern 114, wherein the first adaptive beam pattern 114 includes a plurality of high directionality beams 116, and wherein each of the plurality of high directionality beams 116 is directed toward a corresponding target 102 along the corresponding travel 502 path (Figure 5A), and wherein the corresponding target is from the plurality of targets 102.

[0052]

[0053] Figure 6 shows a block diagram illustrating signal processing operations 600 for some embodiments of the distributed system 100 (Figure 1). An external signal (e.g., data signal 810 (Figure 8)) received by the antenna array 602 (e.g., antenna elements 306 (Figure 3B)) is transferred to an analog receiver 604 to be adjusted to account for an internal equalization signal (e.g., calibration signal 304 (Figure 3A)). The adjusted signal may then be transferred to an analog-to-digital converter 606 before being transferred to a complex value converter 608 where the received signal is then downconverted to baseband such that the subsequent processing can be done in a complex-valued baseband domain. The signal is then sent to an equalizer 610 where the excitation data (e.g., wa-wn (Figure 3B)) from the plurality of antenna elements 306 in each transducer node 104a- 104b is cohered to form a time aligned signal. Operations 602-610 can be seen as cohering and equalization, operations 612 and 614 facilitate the generation of an interference profile, and operations 616 and 618 are directed toward generating the adaptive beam pattern 114 (Figure 5 A, 5B, 5C) and increasing signal quality. Turning to operations 612 and 614, the interference mitigation protocol can be broadly seen as a protocol for increasing the SNR of the distributed system 100. Operation 612 may employ pulse compression techniques to provide SNR gain, isolation of the target 102 in range, and low range sidelobes to suppress interference sources 110 at other ranges and strong closerange clutter reduction. Similarly, operation 614 may further isolate the signal employ doppler filtering techniques to provide SNR gain, isolation of the target 102 in doppler (which corresponds to range-rate), clutter nulling, and low doppler sidelobes to suppress interference sources 110 at other range-rates.

[0053]

[0054] In some embodiments, the central processing node 108 (Figure 1 , Figure 3B) may employ a machine learning algorithm to implement operations 612 and 614 (Figure 6) via a software-defined adaptive filter for separating a signal of interest from the at least one interference source 110. Further, the software-defined adaptive filter enables the distributed system 100 to account for the high variability of interference sources 110 (Figure 5A) and target 102 (Figure 5 A, 5B, 5C) locations when generating the adaptive beam pattern 114 (Figure 5A, 5B, 5C). A subprocess for implementing the software-defined adaptive filter may begin by identifying a domain for interference removal prior to separating the signal of interest, wherein the domain is at least one of temporal (fast time, slow time), spatial, polarization, or combinations thereof. Accordingly, the software-defined adaptive filter may determine the optimal combination of primary beams 112 (Figure 5 A, 5B, 5C) required to maximize directionality, gain, and SNR of the adaptive beam pattern 114. In some embodiments, the software-defined adaptive filter is trained using the current interference profile as well as previously calculated interference profiles and adaptive beam patterns 114. Accordingly, the distributed system 100 improves the SNR without the need for user-guided tuning. Further, the software-defined adaptive filter may identify hidden or foreign artifacts in the read beams or data signals exciting the transducer nodes 104a- 104b. Thus, the software-defined adaptive filter may increase the data security and fidelity of confidential transmissions. In some embodiments, the software-defined adaptive filter may be concurrently trained on the interference signal 702a-702n (see Figure 7 described below) such that the software-defined adaptive filter continuously improves interference mitigation. In some embodiments, the machine learning algorithm monitors available system data to prevent adversaries from deciphering the beamforming algorithm, predicting adaptive beam patterns 114, or in any way attaining exploitable operational intelligence.

[0054]

[0055] In some embodiments, the adaptive filter (e.g., operation 612 and 614 (Figure 6)) works in concert with the transducer nodes 104a- 104b to enable adaptive digital beamforming operations 616. Adaptive beamforming may describe the process of generating high gain and / or high-directionality beams while simultaneously generating null beams and / or spatial nulls 118 to mitigate the effects of external interference sources 110 (Figure 5 A) that may change over time. To facilitate this functionality, the adaptive filter may form a feedback loop with the transducer nodes 104a- 104b such that the efficacy of the adaptive beam pattern 114 in mitigating interference and generating beams is continuously monitored and the output for each transducer node 104a-104n can be individually tuned to accommodate for changes in the configuration of the interference sources 110 and the target 102 (e.g., moving targets 102 and interference sources 110). In some embodiments, operation 616 may execute the following processes for obtaining SNR gain on targets: isolation of the target for a given AOA, (i.e., azimuth and elevation), sidelobe reduction of targets at other AOAs, and for adaptive beamforming, adaptive nulling of interference.

[0055]

[0056] In some embodiments, the feedback loop of operation 616 (Figure 6) is further refined in operation 618 where the central processing node may predict the AOA for any number of interference sources and then perform spatial aliasing to position the interference signal between a pair of sidelobes within the adaptive beam pattern. In some embodiments, the central processing node 108 may employ the plurality of transducer nodes 104a-104b for interferometry operations to estimate a signal’s AOA. The interferometry may include pairwise phase comparison to estimate both AOA and a spacing between array elements that is greater than lambda / 2.

[0056]

[0057] Figure 7A and 7B show how the distributed system 100 can utilize adaptive beamforming techniques to create spatial nulls in the direction of interference sources 110 (Figure 5 A); thereby canceling their interference. In this case, two different sources of interference are canceled by adaptively beamforming to create two null beams in the direction of interference (Figure 7B). Figure 7A shows the output for a beamforming operation that does not apply sidelobe tuning or spatial nulling. Accordingly, the signal, or a first adaptive pattern 700a, has a relatively low level of directionality and relatively high sidelobe magnitude. Figure 7B shows the output for a beamforming operation that applies sidelobe tuning and spatial nulling to generate a broadside tapered beam where a plurality of interference signals 702a- 702n are positioned within spatial nulls 704 between the sidelobes 706. For example, a r second adaptive beam pattern 700b may simultaneously cancel the plurality of interference signals 702a-702n being generated by the plurality of interference sources 110 in a plurality of spatial directions (Figure 5A and Figure 7B). Further, the second adaptive beam pattern 700b may generate broadside tapered beam patterns capable of simultaneously nulling a plurality of spatially offset interference signals 702a-702n. Operations for generating the interference profile may further include determining, via the processor 108 (Figure 1, Figure 3B), an appropriate configuration of null beams 118 (Figure 5 A) to direct toward a corresponding interference source from a plurality of interference sources 110 (Figure 5A) whenever any of the plurality of targets 102 (Figure 5A, 5B, 5C) is within the area of interest 101 (Figure 5A) or field of regard 501a (Figure 5B) or 501b (Figure 5C). Further, the interference profile may be incorporated into the adaptive beam pattern 114 (Figure 5 A, 5B, or 5C) such that outputting the adaptive beam pattern 114 includes adaptive beamforming techniques to point high-gain and / or high directionality beams 116 (Figure 5 A) toward the at least one target 102 while simultaneously creating null beams 118 to cancel the plurality of interference sources 110. In some embodiments, the at least one adaptive beam (pattern) 114 (e.g., second adaptive beam pattern 700b) includes a mainlobe 708 and a plurality of sidelobes 706, wherein a bandwidth of the mainlobe 708 decreases as a magnitude of a direction vector for the plurality of sidelobes 706 increases. Further, the sidelobes 706 may be suppressed up to 40 dB and the adaptive beam pattern 114 (e.g., second adaptive beam pattern 700b) may implement up to 70 dB of cochannel interference mitigation through spatial beam nulling. In some embodiments, the distributed system 100 enables up to 2 Gbps communication links while mitigating interference up to 40 dB, via wideband true time achieving 200 MHz bandwidth up to QAM 1024 constellations.

[0057]

[0058] The central processing node may include a subprocess for updating the adaptive beam pattern 1 14 (Figure 5 A, 5B, 5C) in response to user commands or changes in the state of the at least one target 102 (Figure 5A, 5B, 5C), the transceiver nodes 104a-104n (Figure 5A, 5B, 5C), or the plurality of interference sources 110 (Figure 5A). The update subprocess may begin after directing, via the processor 108 (Figure 1, Figure 3B), the plurality of transceiver nodes 104a-104n to output at least one first adaptive beam pattern 700a (Figure 7B). The subprocess may continue by receiving, via the processor 108, an update request. For example, the update request may be a user supplied command that directs the distributed system 100 (Figure 1) to acquire a new target 102 or to attenuate a newly discovered interference source 110. The subprocess may continue by directing, via the processor 108, the plurality of transceiver nodes 104a- 104n to output at least one second adaptive beam pattern 700b in accordance with the update request Figure 7.B. Figure 7A and Figure 7B show the signal response as the second adaptive beam pattern 700b as the response to an update request directing the distributed system 100 to begin spatial nulling of interference signals 702a-702n.

[0059] In some embodiments, the distributed system 100 (Figure 1) may be configured to monitor the area of interest 101 (Figure 5A) for changes in the plurality of interference sources 110. For example, the central processing node 108 may generate an alert when new interference sources are detected and may automatically update the interference profile to account for changes in the state of any identified interference sources 110 (e.g., modifications to frequency, position, directionality, signal strength). The subprocess may begin by identifying, via the processor 108, a change in the interference profile for the area of interest 101 and updating the interference mitigation protocol based on the identified change. The subprocess may continue by directing, via the processor 108, the plurality of transceiver nodes 104a-104b to output at least one updated adaptive beam pattern 114 and the at least one mitigation signal (or null beam 118) as at least one spatial null coincident with the at least one interference source 110, in accordance with the updated interference mitigation protocol.

[0058]

[0060] Figure 8 is a block diagram of a radar system 800 implemented using the distributed system 100 of Figure 1, consistent with various embodiments. The radar system 800 includes a number of sensor nodes (e.g., sensor nodes 804a, 804b, 804c, 804d, and 804e) that are configured to facilitate surveillance of a moving object (e.g., detection of an aircraft 802). In some embodiments, the sensor nodes 804a-804e are similar to the sensor nodes 104a-104n of the distributed system 100. In some embodiments, one of the sensor nodes 804a-804e may be designated as a reference node and a central processing node. In some embodiments, all the sensor nodes 804a-804e are configured as transmit and receive sensor nodes. The sensor nodes 804a-804e may be time synchronized as described at least with reference to Figures 4 and 5A- 5C above. Further, the time aligned signals may be cohered as described at least with reference to Figure 6 above. The radar system 800 may be configured to work in a wide range of frequencies (e.g., 50MHz to 36GHz).

[0059]

[0061] The sensor nodes 804a- 804e are configured to transmit a probe signal 808 in a beamforming pattern. The signals reflected from the aircraft 802 may be received by the sensor nodes as data signals 810. The data signals 810 are time aligned, cohered, and processed to determine one or more parameters of the aircraft 802 (e.g., distance or speed of the aircraft).

[0062] While Figure 8 shows a single cluster of sensor nodes 804a-804e, the radar system 800 may have several clusters. In some embodiments, each black dot in Figure 8 may be a cluster of sensor nodes. For example, the black dot 804a can be a first cluster, the block dot 804b can be a second cluster and so on, each of which includes several sensor nodes. In some embodiments, such a configuration enables detection of a moving object at ultra-long range and hypersonic speeds. In some embodiments, the sensor nodes or clusters may be spread over a few hundred meters or distributed across a large geographic region.

[0060]

[0063] The distributed system 100 may also be implemented as a mobile sensor array system. For example, the sensor nodes 104a-104n may be designed as mobile sensor nodes that are battery powered, solar powered, etc. and may be installed in an automobile, an unmanned aerial vehicle (UAV), or other mobile devices.

[0061]

[0064] While Figure 8 describes implementation of the distributed system 100 as a radar system, the distributed system 100 may also be implemented as a sonar system to facilitate surveillance of objects moving underwater (e.g., a submarine). For example, the sensor nodes 104a-104n may be configured as hydrophone sensor nodes, which can be installed as buoys or as mobile hydrophones (e.g., in submarines). The hydrophone sensor nodes 104a-104n may be associated with above water components that communicate with satellites and have GPS capability.

[0062]

[0065] In yet another example, the distributed system 100 may be implemented for oil and gas and mining industry to facilitate detection of oil (or any other energy) and metals. For example, the sensor nodes 104a-104n may be configured to work with seismic or acoustic waveforms and the cohered signals may be used to detect oil (or any other energy) and metals.

[0066] Figure 9 shows a visualization 900 of the plurality of high directionality beams 116 being projected into space. This visualization 900 may be adapted into an augmented reality display that corresponds to a digital twin of each of the plurality of high directionality beams 116. When the sensor nodes 104a-104n (Figure 1) and the reference node 106 (Figure 1) receive a data signal (e.g., a response to a probe signal transmitted by the sensor nodes 104a- 104n and that is reflected off by an object such as an aircraft), the sensor nodes 104a-104n and the reference node 106 transmit the received data signal to the central processing node 108 (Figure 1). For example, the first sensor node 104a and the reference node 106 transmit the received first data signal and a reference data signal, respectively, to the central processing node 108. The central processing node 108 may then retrieve the first time offset from the storage device and apply it to the first data signal to generate a first time aligned data signal of the first sensor node 104a. Similarly, the central processing node 108 may apply the second time offset to the second data signal of the second sensor node 104b to generate a second time aligned data signal of the second sensor node 104b.

[0063]

[0067] In some embodiments, the various components or modules illustrated in the Figures or described in the foregoing paragraphs may include one or more computing devices that are programmed to perform the functions described herein. The computing devices may include one or more electronic storages, one or more physical processors programmed with one or more computer program instructions, and / or other components. The computing devices may include communication lines or ports to enable the exchange of information within a network or other computing platforms via wired or wireless techniques (e.g., Ethernet, fiber optics, coaxial cable, Wi-Fi, Bluetooth, near field communication, or other technologies). The computing devices may include a plurality of hardware, software, and / or firmware components operating together. For example, the computing devices may be implemented by a cloud of computing platforms operating together as the computing devices. Cloud components may include control circuitry configured to perform the various operations needed to implement the disclosed embodiments. Cloud components may include cloud-based storage circuitry configured to electronically store information. Cloud components may also include cloud-based input / output circuitry configured to display information.

[0064]

[0068] The electronic storages may include non-transitory storage media that electronically stores information. The storage media of the electronic storages may include one or both of (i) system storage that is provided integrally (e.g., substantially non-removable) with servers or client devices or (ii) removable storage that is removably connectable to the servers or client devices via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storages may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and / or other electronically readable storage media. The electronic storages may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and / or other virtual storage resources). The electronic storage may store software algorithms, information determined by the processors, information obtained from servers, information obtained from client devices, or other information that enables the functionality as described herein.

[0065]

[0069] The processors may be programmed to provide information processing capabilities in the computing devices. As such, the processors may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and / or other mechanisms for electronically processing information. In some embodiments, the processors may include a plurality of processing units. These processing units may be physically located within the same device, or the processors may represent processing functionality of a plurality of devices operating in coordination. The processors may be programmed to execute computer program instructions to perform functions described herein. The processors may be programmed to execute computer program instructions by software; hardware; firmware; some combination of software, hardware, or firmware; and / or other mechanisms for configuring processing capabilities on the processors.

[0066]

[0070] It should be appreciated that the description of the functionality provided by the components or modules described herein is for illustrative purposes, and is not intended to be limiting, as any of the components or modules may provide more or less functionality than is described. For example, one or more of the components or modules may be eliminated, and some or all of its functionality may be provided by other ones of the components or modules. As another example, additional components or modules may be programmed to perform some or all of the functionality attributed herein to one of the components or modules.

[0067]

[0071] The following list of clauses describes various aspects of the systems and methods described herein, which may be combined in any combination.

[0068] 1. A method for co-channel interference mitigation using adaptive beamforming, comprising: receiving, via a processor, status information for a plurality of transceiver nodes; determining, via the processor, an interference profile for an area of interest, wherein the interference profile includes at least one interference source; identifying, via the processor, at least one target; generating, via the processor, an interference mitigation protocol based on target data, the status information, and the interference profile; and outputting, via the plurality of transceiver nodes, at least one adaptive beam directed toward the at least one target and at least one mitigation signal in accordance with the interference mitigation protocol, wherein the at least one mitigation signal attenuates the at least one interference source, and wherein the plurality of transceiver nodes forms a nonuniform array.

[0069] 2. The method of claim 1 , wherein generating the interference mitigation protocol includes: plotting, via the processor, a path of travel for the at least one target; comparing, via the processor, the status information and the interference profile to determine appropriate output characteristics for the at least one adaptive beam and the at least one mitigation signal along the path of travel; directing, via the processor, the plurality of transceiver nodes to output the at least one adaptive beam along the path of travel; and directing, via the processor, the plurality of transceiver nodes to output the at least one mitigation signal as at least one spatial null coincident with a direction of the at least one interference source along the path of travel.

[0070] 3. The method of claim 2, wherein the at least one spatial null is greater than 50 decibels.

[0071] 4. The method of claim 2, wherein a position of the at least one target is tracked within a field of regard via the plurality of transceiver nodes.

[0072] 5. The method of claim 2, wherein the plurality of transceiver nodes includes a plurality of active nodes and a plurality of inactive nodes, the method further comprising: directing, via the processor, a successive series of inactive nodes to become active nodes when the path of travel enters a field of regard for each corresponding inactive node from the plurality of inactive nodes; modifying, via the processor, the interference mitigation protocol based on sensor information received from the plurality of active nodes; and deactivating, via the processor, a successive series of active nodes when the path of travel exits the field of regard for each corresponding active node from the plurality of active nodes.

[0073] 6. The method of claim 5, wherein each of the plurality of active nodes is spatially separated and adaptively combined.

[0074] 7. The method of claim 2, further comprising: determining, via the processor, a target profile for the area of interest, wherein the target profile includes a plurality of targets, and wherein each of the plurality of targets is associated with a corresponding travel path from a plurality of travel paths; and directing, via the processor, the plurality of transceiver nodes to output a first adaptive beam pattern, wherein the first adaptive beam pattern includes a plurality of high directionality beams, and wherein each of the plurality of high directionality beams is directed toward a corresponding target along the corresponding travel path, and wherein the corresponding target is from the plurality of targets.

[0075] 8. The method of claim 7, wherein the first adaptive beam pattern includes a plurality of null beams, the method further comprising: determining, via the processor, an appropriate configuration of null beams to direct toward a corresponding interference source from a plurality of interference sources whenever any of the plurality of targets is within the area of interest.

[0076] 9. The method of claim 7, wherein: the first adaptive beam pattern simultaneously cancels a plurality of interference sources in a plurality of spatial directions.

[0077] 10. The method of claim 7, wherein: the first adaptive beam pattern generates broadside tapered beam patterns capable of simultaneously nulling a plurality of spatially offset interference sources.

[0078] 11. The method of claim 2, further comprising: directing, via the processor, the plurality of transceiver nodes to output at least one first adaptive beam pattern; receiving, via the processor, an update request; and directing, via the processor, the plurality of transceiver nodes to output at least one second adaptive beam pattern in accordance with the update request.

[0079] 12. The method of claim 2, further comprising: identifying, via the processor, a change in the interference profile for the area of interest; updating, via the processor, the interference mitigation protocol based on the identified change; directing, via the processor, the plurality of transceiver nodes to output at least one updated adaptive beam pattern, in accordance with the updated interference mitigation protocol; and directing, via the processor, the plurality of transceiver nodes to output the at least one mitigation signal as at least one spatial null coincident with the at least one interference source in accordance with the updated interference mitigation protocol.

[0080] 13. The method of claim 2, wherein a response of a direction vector for a mainlobe of the at least one adaptive beam to a signal at an angle of approach may be adaptively modified as the angle of approach is varied over the area of interest.

[0081] 14. The method of claim 2, wherein the comparing employs a machine learning algorithm to implement a software-defined adaptive filter for separating a signal of interest from the at least one interference source.

[0082] 15. The method of claim 14, wherein the software-defined adaptive filter first identifies a domain for interference removal prior to separating the signal of interest, and wherein the domain is at least one of temporal (fast time, slow time), spatial, and polarization.

[0083] 16. The method of claim 14, wherein the software-defined adaptive filter is trained using the interference profile.

[0084] 17. The method of claim 1, wherein the at least one adaptive beam includes a plurality of read beams disposed in a desired spatial configuration within an area of interest, and wherein each read beam is associated with a corresponding weight vector, the method further comprising: directing, via the processor, the plurality of transceiver nodes to capture a plurality of snapshots of the at least one target, wherein each of the plurality of snapshots is captured when a corresponding read beam coincides with the at least one target; and cohering, via the processor, the corresponding weight vector for each of the plurality of snapshots to form a high-gain received signal.

[0085] 18. The method of claim 17, wherein the at least one adaptive beam is in a monostatic configuration used to surveil a transmit sector of the at least one target.

[0086] 19. The method of claim 17, wherein the at least one adaptive beam is in a bistatic configuration used to surveil at least one of a range and an angle extent of a transmit sector for the at least one target.

[0087] 20. The method of claim 17, wherein the at least one adaptive beam is in an isotropic configuration used to surveil an omnidirectional area of interest.

[0088] 21. The method of claim 17, wherein the plurality of transceiver nodes includes a plurality of transceiver nodes each outputting a corresponding adaptive beam, and wherein each of the plurality of transceiver nodes is disposed to surveil at least a portion of a range and / or angle extent of the at least one target, and wherein each of the plurality of transceiver nodes is associated with a corresponding beam weight vector, the method further comprising: cohering, via the processor, the corresponding beam weight vector for each of the plurality of snapshots to form the high-gain received signal.

[0089] 22. The method of claim 21 , wherein the plurality of transceiver nodes forms a nonuniform array, and wherein a direction vector of the nonuniform array is cohered, via the processor, by multiplying a direction vector for each element of the nonuniform array with a reference signal.

[0090] 23. The method of claim 1, wherein the at least one adaptive beam includes a mainlobe and a plurality of sidelobes, and wherein a bandwidth of the mainlobe decreases as a magnitude of a direction vector for the plurality of sidelobes increases.

[0091] 24. The method of claim 1, wherein the processor generates a digital twin of the at least one adaptive beam, and wherein the processor directs an augmented reality device to display the digital twin as a 3-dimensional rendering in space.

[0092] 25. The method of claim 1, wherein a field of regard of the plurality of transceiver nodes is adaptively formed to encompass a plurality of targets.

[0093] 26. The method of claim 1, wherein the at least one target includes at least one of a fixed satellite service (FSS) and a fixed service (FS) device.

[0094] 27. The method of claim 1 , wherein the outputting includes adaptive beamforming techniques to point high gain directional beams toward the at least one target while simultaneously creating null beams to cancel the at least one interference source.

[0095] 28. The method of claim 1 wherein the plurality of transceiver nodes functions as a software-defined radio.

[0096] 29. The method of claim 1 wherein the interference profile includes a covariance matrix describing the at least one interference source.

[0097] 30. The method of claim 1 wherein generating the interference profile includes spatial aliasing techniques to attenuate the at least one interference source.

[0098] 31. A system for co-channel interference mitigation using adaptive beamforming, comprising: a processor configured to: receive status information for a plurality of transceiver nodes; determine an interference profile for an area of interest, wherein the interference profile includes at least one interference source; identify at least one target; generate an interference mitigation protocol based on target data, the status information, and the interference profile; and output, via the plurality of transceiver nodes, at least one adaptive beam directed toward the at least one target and at least one mitigation signal in accordance with the interference mitigation protocol, wherein the at least one mitigation signal attenuates the at least one interference source, and wherein the plurality of transceiver nodes forms a nonuniform array.

[0099] 32. The system of claim 31, wherein generating the interference mitigation protocol includes: the processor being further configured to: plot a path of travel for the at least one target; compare the status information and the interference profile to determine appropriate output characteristics for the at least one adaptive beam and the at least one mitigation signal along the path of travel; direct the plurality of transceiver nodes to output the at least one adaptive beam along the path of travel; and direct the plurality of transceiver nodes to output the at least one mitigation signal as at least one spatial null coincident with a direction of the at least one interference source along the path of travel.

[0100] 33. The system of claim 32, wherein the at least one spatial null is greater than 50 decibels.

[0101] 34. The system of claim 32, wherein a position of the at least one target is tracked within a field of regard via the plurality of transceiver nodes.

[0102] 35. The system of claim 32, wherein the plurality of transceiver nodes includes a plurality of active nodes and a plurality of inactive nodes, the system further comprising: the processor being further configured to: direct a successive series of inactive nodes to become active nodes when the path of travel enters a field of regard for each corresponding inactive node from the plurality of inactive nodes; modify the interference mitigation protocol based on sensor information received from the plurality of active nodes; and deactivate a successive series of active nodes when the path of travel exits the field of regard for each corresponding active node from the plurality of active nodes.

[0103] 36. The system of claim 35, wherein each of the plurality of active nodes is spatially separated and adaptively combined.

[0104] 37. The system of claim 32, further comprising: the processor being further configured to: determine a target profile for the area of interest, wherein the target profile includes a plurality of targets, and wherein each of the plurality of targets is associated with a corresponding travel path from a plurality of travel paths; and direct the plurality of transceiver nodes to output a first adaptive beam pattern, wherein the first adaptive beam pattern includes a plurality of high directionality beams, and wherein each of the plurality of high directionality beams is directed toward a corresponding target along the corresponding travel path, and wherein the corresponding target is from the plurality of targets.

[0105] 38. The system of claim 37, wherein the first adaptive beam pattern includes a plurality of null beams, the system further comprising: the processor being further configured to: determine an appropriate configuration of null beams to direct toward a corresponding interference source from a plurality of interference sources whenever any of the plurality of targets is within the area of interest.

[0106] 39. The system of claim 37, wherein: the first adaptive beam pattern simultaneously cancels a plurality of interference sources in a plurality of spatial directions.

[0107] 40. The system of claim 37, wherein: the first adaptive beam pattern generates broadside tapered beam patterns capable of simultaneously nulling a plurality of spatially offset interference sources.

[0108] 41. The system of claim 32, further comprising: the processor being further configured to: direct the plurality of transceiver nodes to output at least one first adaptive beam pattern; receive an update request; and direct the plurality of transceiver nodes to output at least one second adaptive beam pattern in accordance with the update request.

[0109] 42. The system of claim 32, further comprising: the processor being further configured to: identify a change in the interference profile for the area of interest; update the interference mitigation protocol based on the identified change; direct the plurality of transceiver nodes to output at least one updated adaptive beam pattern, in accordance with the updated interference mitigation protocol; and direct the plurality of transceiver nodes to output the at least one mitigation signal as at least one spatial null coincident with the at least one interference source in accordance with the updated interference mitigation protocol.

[0110] 43. The system of claim 32, wherein a response of a direction vector for a mainlobe of the at least one adaptive beam to a signal at an angle of approach may be adaptively modified as the angle of approach is varied over the area of interest.

[0111] 44. The system of claim 32, wherein the comparing employs a machine learning algorithm to implement a software-defined adaptive filter for separating a signal of interest from the at least one interference source.

[0112] 45. The system of claim 44, wherein the software-defined adaptive filter first identifies a domain for interference removal prior to separating the signal of interest, and wherein the domain is at least one of temporal (fast time, slow time), spatial, and polarization.

[0113] 46. The system of claim 44, wherein the software-defined adaptive filter is trained using the interference profile.

[0114] 47. The system of claim 31, wherein the at least one adaptive beam includes a plurality of read beams disposed in a desired spatial configuration within an area of interest, and wherein each read beam is associated with a corresponding weight vector, the system further comprising: the processor being further configured to: direct the plurality of transceiver nodes to capture a plurality of snapshots of the at least one target, wherein each of the plurality of snapshots is captured when a corresponding read beam coincides with the at least one target; and cohere the corresponding weight vector for each of the plurality of snapshots to form a high-gain received signal.

[0115] 48. The system of claim 47, wherein the at least one adaptive beam is in a monostatic configuration used to surveil a transmit sector of the at least one target.

[0116] 49. The system of claim 47, wherein the at least one adaptive beam is in a bistatic configuration used to surveil at least one of a range and an angle extent of a transmit sector for the at least one target.

[0117] 50. The system of claim 47, wherein the at least one adaptive beam is in an isotropic configuration used to surveil an omnidirectional area of interest. 51. The system of claim 47, wherein the plurality of transceiver nodes includes a plurality of transceiver nodes each outputting a corresponding adaptive beam, and wherein each of the plurality of transceiver nodes is disposed to surveil at least a portion of a range and / or angle extent of the at least one target, and wherein each of the plurality of transceiver nodes is associated with a corresponding beam weight vector, the system further comprising: the processor being further configured to: cohere the corresponding beam weight vector for each of the plurality of snapshots to form the high-gain received signal.

[0118] 52. The system of claim 51, wherein the plurality of transceiver nodes forms a nonuniform array, and wherein a direction vector of the nonuniform array is cohered, via the processor, by multiplying a direction vector for each element of the nonuniform array with a reference signal.

[0119] 53. The system of claim 31, wherein the at least one adaptive beam includes a mainlobe and a plurality of sidelobes, and wherein a bandwidth of the mainlobe decreases as a magnitude of a direction vector for the plurality of sidelobes increases.

[0120] 54. The system of claim 31 , wherein the processor generates a digital twin of the at least one adaptive beam, and wherein the processor directs an augmented reality device to display the digital twin as a 3-dimensional rendering in space.

[0121] 55. The system of claim 31, wherein a field of regard of the plurality of transceiver nodes is adaptively formed to encompass a plurality of targets.

[0122] 56. The system of claim 31 , wherein the at least one target includes at least one of a fixed satellite service (FSS) and a fixed service (FS) device.

[0123] 57. The system of claim 31, wherein the outputting includes adaptive beamforming techniques to point high gain directional beams toward the at least one target while simultaneously creating null beams to cancel the at least one interference source.

[0124] 58. The system of claim 31 wherein the plurality of transceiver nodes functions as a software-defined radio.

[0125] 59. The system of claim 31 wherein the interference profile includes a covariance matrix describing the at least one interference source.

[0126] 60. The system of claim 31 wherein generating the interference profile includes spatial aliasing techniques to attenuate the at least one interference source.

[0127] 61. A non-transitory computer-readable medium for co-channel interference mitigation using adaptive beamforming, the non-transitory computer-readable medium storing instructions that, when executed by one or more processors, program the one or more processors to: receive status information for a plurality of transceiver nodes; determine an interference profile for an area of interest, wherein the interference profile includes at least one interference source; identify at least one target; generate an interference mitigation protocol based on target data, the status information, and the interference profile; and output, via the plurality of transceiver nodes, at least one adaptive beam directed toward the at least one target and at least one mitigation signal in accordance with the interference mitigation protocol, wherein the at least one mitigation signal attenuates the at least one interference source, and wherein the plurality of transceiver nodes forms a nonuniform array.

[0128] 62. The non-transitory computer-readable medium of claim 61 , wherein generating the interference mitigation protocol includes further programming the processor to: plot a path of travel for the at least one target; compare the status information and the interference profile to determine appropriate output characteristics for the at least one adaptive beam and the at least one mitigation signal along the path of travel; direct the plurality of transceiver nodes to output the at least one adaptive beam along the path of travel; and direct the plurality of transceiver nodes to output the at least one mitigation signal as at least one spatial null coincident with a direction of the at least one interference source along the path of travel.

[0129] 63. The non-transitory computer-readable medium of claim 62, wherein the at least one spatial null is greater than 50 decibels.

[0130] 64. The non-transitory computer-readable medium of claim 62, wherein a position of the at least one target is tracked within a field of regard via the plurality of transceiver nodes.

[0131] 65. The non-transitory computer-readable medium of claim 62, wherein the plurality of transceiver nodes includes a plurality of active nodes and a plurality of inactive nodes, the non-transitory computer-readable medium further programming the processor to: direct a successive series of inactive nodes to become active nodes when the path of travel enters a field of regard for each corresponding inactive node from the plurality of inactive nodes; modify the interference mitigation protocol based on sensor information received from the plurality of active nodes; and deactivate a successive series of active nodes when the path of travel exits the field of regard for each corresponding active node from the plurality of active nodes.

[0132] 66. The non-transitory computer-readable medium of claim 65, wherein each of the plurality of active nodes is spatially separated and adaptively combined.

[0133] 67. The non-transitory computer-readable medium of claim 62, further programming the processor to: determine a target profile for the area of interest, wherein the target profile includes a plurality of targets, and wherein each of the plurality of targets is associated with a corresponding travel path from a plurality of travel paths; and direct the plurality of transceiver nodes to output a first adaptive beam pattern, wherein the first adaptive beam pattern includes a plurality of high directionality beams, and wherein each of the plurality of high directionality beams is directed toward a corresponding target along the corresponding travel path, and wherein the corresponding target is from the plurality of targets.

[0134] 68. The non-transitory computer-readable medium of claim 67, wherein the first adaptive beam pattern includes a plurality of null beams, the non-transitory computer-readable medium further programming the processor to: determine an appropriate configuration of null beams to direct toward a corresponding interference source from a plurality of interference sources whenever any of the plurality of targets is within the area of interest.

[0135] 69. The non-transitory computer-readable medium of claim 67, wherein: the first adaptive beam pattern simultaneously cancels a plurality of interference sources in a plurality of spatial directions.

[0136] 70. The non-transitory computer-readable medium of claim 67, wherein: the first adaptive beam pattern generates broadside tapered beam patterns capable of simultaneously nulling a plurality of spatially offset interference sources.

[0137] 71. The non-transitory computer-readable medium of claim 62, the non-transitory computer-readable medium further programming the processor to: direct the plurality of transceiver nodes to output at least one first adaptive beam pattern; receive an update request; and direct the plurality of transceiver nodes to output at least one second adaptive beam pattern in accordance with the update request. 72. The non-transitory computer-readable medium of claim 62, the non-transitory computer-readable medium further programming the processor to: identify a change in the interference profile for the area of interest; update the interference mitigation protocol based on the identified change; direct the plurality of transceiver nodes to output at least one updated adaptive beam pattern, in accordance with the updated interference mitigation protocol; and direct the plurality of transceiver nodes to output the at least one mitigation signal as at least one spatial null coincident with the at least one interference source in accordance with the updated interference mitigation protocol.

[0138] 73. The non-transitory computer-readable medium of claim 62, wherein a response of a direction vector for a mainlobe of the at least one adaptive beam to a signal at an angle of approach may be adaptively modified as the angle of approach is varied over the area of interest.

[0139] 74. The non-transitory computer-readable medium of claim 62, wherein the comparing employs a machine learning algorithm to implement a software-defined adaptive filter for separating a signal of interest from the at least one interference source.

[0140] 75. The non-transitory computer-readable medium of claim 74, wherein the software- defined adaptive filter first identifies a domain for interference removal prior to separating the signal of interest, and wherein the domain is at least one of temporal (fast time, slow time), spatial, and polarization.

[0141] 76. The non-transitory computer-readable medium of claim 74, wherein the software- defined adaptive filter is trained using the interference profile.

[0142] 77. The non-transitory computer-readable medium of claim 61, wherein the at least one adaptive beam includes a plurality of read beams disposed in a desired spatial configuration within an area of interest, and wherein each read beam is associated with a corresponding weight vector, the non-transitory computer-readable medium further programming the processor to: direct the plurality of transceiver nodes to capture a plurality of snapshots of the at least one target, wherein each of the plurality of snapshots is captured when a corresponding read beam coincides with the at least one target; and cohere the corresponding weight vector for each of the plurality of snapshots to form a high-gain received signal.

[0143] 78. The non-transitory computer-readable medium of claim 77, wherein the at least one adaptive beam is in a monostatic configuration used to surveil a transmit sector of the at least one target.

[0144] 79. The non-transitory computer-readable medium of claim 77, wherein the at least one adaptive beam is in a bistatic configuration used to surveil at least one of a range and an angle extent of a transmit sector for the at least one target.

[0145] 80. The non-transitory computer-readable medium of claim 77, wherein the at least one adaptive beam is in an isotropic configuration used to surveil an omnidirectional area of interest.

[0146] 81 . The non-transitory computer-readable medium of claim 77, wherein the plurality of transceiver nodes includes a plurality of transceiver nodes each outputting a corresponding adaptive beam, and wherein each of the plurality of transceiver nodes is disposed to surveil at least a portion of a range and / or angle extent of the at least one target, and wherein each of the plurality of transceiver nodes is associated with a corresponding beam weight vector, the non-transitory computer-readable medium further programming the processor to: cohere the corresponding beam weight vector for each of the plurality of snapshots to form the high-gain received signal.

[0147] 82. The non-transitory computer-readable medium of claim 81 , wherein the plurality of transceiver nodes forms a nonuniform array, and wherein a direction vector of the nonuniform array is cohered, via the processor, by multiplying a direction vector for each element of the nonuniform array with a reference signal.

[0148] 83. The non-transitory computer-readable medium of claim 61 , wherein the at least one adaptive beam includes a mainlobe and a plurality of sidelobes, and wherein a bandwidth of the mainlobe decreases as a magnitude of a direction vector for the plurality of sidelobes increases.

[0149] 84. The non-transitory computer-readable medium of claim 61, wherein the processor generates a digital twin of the at least one adaptive beam, and wherein the processor directs an augmented reality device to display the digital twin as a 3-dimensional rendering in space.

[0150] 85. The non-transitory computer-readable medium of claim 61, wherein a field of regard of the plurality of transceiver nodes is adaptively formed to encompass a plurality of targets.

[0151] 86. The non-transitory computer-readable medium of claim 61 , wherein the at least one target includes at least one of a fixed satellite service (FSS) and a fixed service (FS) device.

[0152] 87. The non-transitory computer-readable medium of claim 61, wherein the outputting includes adaptive beamforming techniques to point high gain directional beams toward the at least one target while simultaneously creating null beams to cancel the at least one interference source. 88. The non-transitory computer-readable medium of claim 61 wherein the plurality of transceiver nodes functions as a software-defined radio.

[0153] 89. The non-transitory computer-readable medium of claim 61 wherein the interference profile includes a covariance matrix describing the at least one interference source.

[0154] 90. The non-transitory computer-readable medium of claim 61 wherein generating the interference profile includes spatial aliasing techniques to attenuate the at least one interference source.

[0155]

[0072] Although the present invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.

Claims

WHAT IS CLAIMED IS:

1. A method for co-channel interference mitigation using adaptive beamforming, comprising: receiving, via a processor, status information for a plurality of transceiver nodes; determining, via the processor, an interference profile for an area of interest, wherein the interference profile includes at least one interference source; identifying, via the processor, at least one target; generating, via the processor, an interference mitigation protocol based on target data, the status information, and the interference profile; and outputting, via the plurality of transceiver nodes, at least one adaptive beam directed toward the at least one target and at least one mitigation signal in accordance with the interference mitigation protocol, wherein the at least one mitigation signal attenuates the at least one interference source, and wherein the plurality of transceiver nodes forms a nonuniform array.

2. The method of claim 1 , wherein generating the interference mitigation protocol includes: plotting, via the processor, a path of travel for the at least one target; comparing, via the processor, the status information and the interference profile to determine appropriate output characteristics for the at least one adaptive beam and the at least one mitigation signal along the path of travel; directing, via the processor, the plurality of transceiver nodes to output the at least one adaptive beam along the path of travel; and directing, via the processor, the plurality of transceiver nodes to output the at least one mitigation signal as at least one spatial null coincident with a direction of the at least one interference source along the path of travel.

3. The method of claim 2, wherein the at least one spatial null is greater than 50 decibels.

4. The method of claim 2, wherein a position of the at least one target is tracked within a field of regard via the plurality of transceiver nodes.

5. The method of claim 2, further comprising: determining, via the processor, a target profile for the area of interest, wherein the target profile includes a plurality of targets, and wherein each of the plurality of targets is associated with a corresponding travel path from a plurality of travel paths; and directing, via the processor, the plurality of transceiver nodes to output a first adaptive beam pattern, wherein the first adaptive beam pattern includes a plurality of high directionality beams, and wherein each of the plurality of high directionality beams is directed toward a corresponding target along the corresponding travel path, and wherein the corresponding target is from the plurality of targets.

6. The method of claim 5, wherein the first adaptive beam pattern includes a plurality of null beams, the method further comprising: determining, via the processor, an appropriate configuration of null beams to direct toward a corresponding interference source from a plurality of interference sources whenever any of the plurality of targets is within the area of interest.

7. The method of claim 5, wherein: the first adaptive beam pattern simultaneously cancels a plurality of interference sources in a plurality of spatial directions.

8. The method of claim 5, wherein: the first adaptive beam pattern generates broadside tapered beam patterns capable of simultaneously nulling a plurality of spatially offset interference sources.

9. The method of claim 2, further comprising: directing, via the processor, the plurality of transceiver nodes to output at least one first adaptive beam pattern; receiving, via the processor, an update request; and directing, via the processor, the plurality of transceiver nodes to output at least one second adaptive beam pattern in accordance with the update request.

10. The method of claim 2, wherein the comparing employs a machine learning algorithm to implement a software-defined adaptive filter for separating a signal of interest from the at least one interference source.

11. The method of claim 10, wherein the software-defined adaptive filter first identifies a domain for interference removal prior to separating the signal of interest, and wherein the domain is at least one of temporal (fast time, slow time), spatial, and polarization.

12. The method of claim 10, wherein the software-defined adaptive filter is trained using the interference profile.

13. The method of claim 1 , wherein the at least one adaptive beam includes a plurality of read beams disposed in a desired spatial configuration within an area of interest, and wherein each read beam is associated with a corresponding weight vector, the method further comprising: directing, via the processor, the plurality of transceiver nodes to capture a plurality of snapshots of the at least one target, wherein each of the plurality of snapshots is captured when a corresponding read beam coincides with the at least one target; and cohering, via the processor, the corresponding weight vector for each of the plurality of snapshots to form a high-gain received signal.

14. The method of claim 13, wherein the at least one adaptive beam is in a bistatic configuration used to surveil at least one of a range and an angle extent of a transmit sector for the at least one target.

15. The method of claim 13, wherein the plurality of transceiver nodes includes a plurality of transceiver nodes each outputting a corresponding adaptive beam, and wherein each of the plurality of transceiver nodes is disposed to surveil at least a portion of a range and / or angle extent of the at least one target, and wherein each of the plurality of transceiver nodes is associated with a corresponding beam weight vector, the method further comprising: cohering, via the processor, the corresponding beam weight vector for each of the plurality of snapshots to form the high-gain received signal.

16. The method of claim 15, wherein the plurality of transceiver nodes forms a nonuniform array, and wherein a direction vector of the nonuniform array is cohered, via the processor, by multiplying a direction vector for each element of the nonuniform array with a reference signal.

17. The method of claim 1, wherein the processor generates a digital twin of the at least one adaptive beam, and wherein the processor directs an augmented reality device to display the digital twin as a 3-dimensional rendering in space.

18. The method of claim 1, wherein a field of regard of the plurality of transceiver nodes is adaptively formed to encompass a plurality of targets.

19. The method of claim 1 wherein the plurality of transceiver nodes functions as a software-defined radio.

20. The method of claim 1 wherein generating the interference profile includes spatial aliasing techniques to attenuate the at least one interference source.

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