Devices, systems, and methods for disrupting electromechanical systems using acoustic energy

Abstract

Devices, systems, and methods for disrupting electromechanical systems using acoustic energy are disclosed. A system for disrupting electromechanical systems using acoustic energy comprises: a waveform synthesis module configured to generate signals in an acoustic frequency band including a target resonant mode for disrupting a target electromechanical system; and one or more ultrasonic transducers configured to receive the signals and generate acoustic waves. A method of disrupting electromechanical systems using acoustic energy comprises: classifying a target electromechanical system to determine targeting parameters for targeting the electromechanical system; generating acoustic waves in an acoustic frequency band including a target resonant mode for disrupting the target electromechanical system based on the targeting parameters; and emitting the acoustic waves toward the target electromechanical system.

Description

DEVICES, SYSTEMS, AND METHODS FOR DISRUPTING ELECTROMECHANICAL SYSTEMS USING ACOUSTIC ENERGYCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to United States Provisional Patent Application No. 63 / 652,805, filed on May 29, 2024, the entire contents of which is incorporated herein by reference for all purposes.TECHNICAL FIELD

[0002] The present disclosure relates to disrupting electromechanical systems, and in particular to disrupting electromechanical systems, such as those onboard Unmanned Aerial Systems (UAS), using acoustic energy.BACKGROUND

[0003] Unmanned Aerial Systems (UAS) are becoming increasingly prevalent, posing significant threats in scenarios such as unauthorized surveillance, smuggling, and potential attacks. Existing countermeasures, including kinetic methods, signal jamming, and directed electromagnetic energy, often suffer from limitations such as collateral damage and limited effectiveness.

[0004] Accordingly, devices, systems, and methods for disrupting and / or neutralizing electromechanical systems such as UAS threats remains highly desirable.SUMMARY

[0005] In accordance with one aspect of the present disclosure, a system for disrupting electromechanical systems using acoustic energy is disclosed, comprising: a waveform synthesis module configured to generate signals in an acoustic frequency band including a target resonant mode for disrupting a target electromechanical system; and one or more ultrasonic transducers configured to receive the signals and generate acoustic waves.

[0006] In some aspects, the system further comprises: an amplification module configured to receive and amplify the signals to generate amplified waveforms; wherein the one or more ultrasonic transducers are configured to receive the amplified waveforms from the amplification module for generating the acoustic waves.

[0007] In some aspects, the amplifier module comprises one or more transistor amplifiers.

[0008] In some aspects, the waveform synthesis module comprises one or more processing devices to generate the signals.

[0009] In some aspects, the one or more processing devices comprise one or more of: field programmable gate arrays, microcontrollers, application-specific integrated circuits, and / or digital signal processors.

[0010] In some aspects, the signals are generated in a time-domain.

[0011] In some aspects, the one or more processing devices generate respective signals at a target phase, a target frequency, and / or a target amplitude.

[0012] In some aspects, the one or more processing devices are communicatively coupled to one or more controllers that communicate the target phase, the target frequency, and / or the target amplitude to the one or more processing devices.

[0013] In some aspects, a subset of the one or more processing devices are coupled to a respective controller.

[0014] In some aspects, the one or more controllers are communicatively coupled to a computing unit that provides targeting parameters of the target electromechanical system for the one or more controllers to calculate the target phase, the target frequency, and the target amplitude.

[0015] In some aspects, the targeting parameters of the target electromechanical system comprise a type of the target electromechanical system, adistance of the target electromechanical system from the ultrasonic transducers, and / or a resonant frequency of the target electromechanical system.

[0016] In some aspects, the system further comprises the computing unit.

[0017] In some aspects, the system further comprises detection equipment communicatively coupled to the computing unit and configured to detect the target electromechanical system, wherein the computing unit is configured to classify the target electromechanical system and determine the targeting parameters.

[0018] In some aspects, the detection equipment comprises distance measurement equipment for measuring the distance of the target electromechanical system from the device.

[0019] In some aspects, the computing unit is configured to execute a distance estimation Artificial Intelligence model trained to process data from the distance measurement equipment and estimate the distance of the target electromechanical system from the device.

[0020] In some aspects, the distance measurement equipment comprises one or more of a camera, a LiDAR sensor, and / or a radar sensor.

[0021] In some aspects, the computing unit is configured to execute an electromechanical system identification Artificial Intelligence model trained to process images from the camera and determine the type of the target electromechanical system.

[0022] In some aspects, the computing unit is configured to access a database storing known electromechanical systems and associated resonant frequencies to determine the resonant frequency of the target electromechanical system.

[0023] In some aspects, a subset of the one or more ultrasonic transducers are configured to detect acoustic signals from the target electromechanical system in an active listening mode, and the computing unit is configured to execute an acoustic listening Artificial Intelligence model trained to determine the resonant frequency ofthe target electromechanical system and / or the distance of the target electromechanical system from the device from the detected acoustic signals.

[0024] In some aspects, the computing unit is communicatively coupled to a central command server configured to provide auxiliary data to the computing for classifying the target electromechanical system.

[0025] In some aspects, the generated acoustic waves are beamformed.

[0026] In some aspects, the generated acoustic waves comprise a singlefrequency tone, a frequency sweep, a burst signal, and / or a random signal pattern.

[0027] In some aspects, the ultrasonic transducers comprise piezoelectric transducers.

[0028] In some aspects, the ultrasonic transducers comprise a hexagonal arrangement of transducers.

[0029] In some aspects, the ultrasonic transducers comprise a phased array.

[0030] In some aspects, the ultrasonic transducers are arranged on a planar or curved surface.

[0031] In some aspects, the acoustic waves are generated in an ultrasonic frequency range or near-ultrasonic frequency range.

[0032] In some aspects, the system further comprises a power module configured to provide power to the device.

[0033] In some aspects, the target electromechanical system is onboard an unmanned aerial system.

[0034] In accordance with another aspect of the present disclosure, a method of disrupting electromechanical systems using acoustic energy is disclosed, comprising: classifying a target electromechanical system to determine targeting parameters for targeting the electromechanical system; generating acoustic waves in an acoustic frequency band including a target resonant mode for disrupting the targetelectromechanical system based on the targeting parameters; and emitting the acoustic waves toward the target electromechanical system.

[0035] In some aspects, classifying the target electromechanical system comprises determining a distance to the electromechanical system.

[0036] In some aspects, classifying the target electromechanical system comprises determining a type of the electromechanical system.

[0037] In some aspects, the method further comprises: searching a database storing a plurality of targeting parameters corresponding to a plurality of types of electromechanical systems; and determining the targeting parameters for the type of the electromechanical system.

[0038] In some aspects, classifying the target electromechanical system comprises: performing a frequency sweep by generating a plurality of acoustic waves in a range of frequencies; capturing audio data corresponding to feedback to the frequency sweep from the electromechanical system; and analyzing the audio data to determine one or more frequency peaks for the electromechanical system.

[0039] In some aspects, generating the acoustic waves comprises determining a target phase, a target frequency, and / or a target amplitude based on the targeting parameters, and driving one or more ultrasonic transducers to generate the acoustic waves based on the target phase, the target frequency, and / or the target amplitude.

[0040] In some aspects, the generating of the acoustic waves comprises: generating, with a waveform synthesis module, signals in the acoustic frequency band corresponding to the targeting parameters; and generating, with the one or more ultrasonic transducers, acoustic waves from the signals.

[0041] In some aspects, the generating of the acoustic waves further comprises: amplifying, with an amplification module, the signals to generate amplified waveforms; wherein the acoustic waves are generated from the amplified waveforms.

[0042] In some aspects, the electromechanical system is onboard an unmanned aerial system.

[0043] In some embodiments, the disclosure describes a phased array ultrasonic beamforming system designed to neutralize UAS threats. In some embodiments, the disclosure neutralizes UAS threats by disrupting their Inertial Measurement Units (IMU) and gyroscopes. In some embodiments, the disclosure employs piezoelectric devices arranged in a specific geometry and phased to produce a focused ultrasonic beam.

[0044] Overall, the acoustic wave transmission system, which in some embodiments is a phased array ultrasonic beamforming system, operates as a precise, adaptive, and effective countermeasure against UAS threats, leveraging advanced detection, targeting, and disruption technologies.

[0045] The devices, systems, and methods for disrupting electromechanical systems using acoustic energy in accordance with the present disclosure provide several advantages and benefits, including but not limited to:

[0046] Non-Kinetic: The devices and systems can be used to neutralize UAS threats without physical contact, reducing the risk of collateral damage. By using ultrasonic waves to disrupt the UAS's electromechanical components such as gyroscopes and IMU, the system achieves effective neutralization while maintaining safety in both rural and urban environments.

[0047] Adaptability: A dynamic phasing mechanism, which may be controlled by high-resolution digital-to-analog converters (DACs) and real-time feedback from a distance measurement subsystem, enables continuous adjustments. This allows the system to track and neutralize moving UAS effectively, even under changing environmental conditions.

[0048] Modularity: The device and system's modular design allows for easy component replacement and maintenance. Amplifiers, power supplies, and other important components may be modularized and can be switched out rapidly, ensuring that the system remains operational in real-world scenarios. This modularity also simplifies troubleshooting and enhances overall system reliability.

[0049] Precision: Embodiments with a phased array design allows for precise targeting, minimizing unintended interference. Advanced algorithms running on the processing devices can ensure that the ultrasonic beam remains accurately focused on the UAS, adapting to its movements and maintaining effectiveness throughout the engagement.

[0050] Network of Devices: In embodiments utilizing beamforming technology, multiple standalone devices can operate in unison to increase the system's range and effectively cover a larger area. This networked approach enhances the system's capability to detect, track, and neutralize multiple UAS threats simultaneously, providing comprehensive coverage for a specified radius.BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Further features and advantages of the present disclosure will become apparent from the following detailed description, taken in combination with the appended drawings, in which:

[0052] FIG. 1 shows a representation of a system architecture for disrupting electromechanical systems using acoustic energy;

[0053] FIGs. 2A and 2B show a representation of an example ultrasonic transducer array;

[0054] FIGs. 3A and 3B show a functional block diagram of the system shown in FIG. 1;

[0055] FIG. 4 shows a method of disrupting electromechanical systems;

[0056] FIG. 5 shows a further method of disrupting electromechanical systems;

[0057] FIG. 6 shows a method of classifying a target electromechanical system; and

[0058] FIG. 7 shows an example of a frequency response obtained from active listening.

[0059] It will be noted that throughout the appended drawings, like features are identified by like reference numerals.DETAILED DESCRIPTION

[0060] The present disclosure provides devices, systems, and methods for disrupting electromechanical systems using acoustic energy. More specifically, the present disclosure focuses on the application of acoustic waves, particularly in the ultrasonic (e.g. ranging from 20-70kHz, more particularly between about 20-40kHz) and near-ultrasonic (e.g. ranging from 12-20kHz) acoustic spectrums, to disrupt the functionality of electromechanical systems, including onboard electromechanical systems of airborne unmanned aerial systems (UAS) by interfering with their equipment including cameras, electrical systems, sensors, and / or control systems.

[0061] The systems and methods using acoustic waves in the ultrasonic and / or near-ultrasonic frequency ranges provide a novel technique for disrupting electromechanical systems including those onboard UAS. Unlike conventional counter-UAS technologies that rely on RF jamming or kinetic interception, the systems and methods disclosed herein induce malfunctions in electromechanical subsystems — such as MEMS gyroscopes, IMUs, flight controllers, and optical sensors — through mechanical excitation at or near their resonant modes (e.g. their resonant frequencies or integer multiples thereof). Effects achieved by transmitting acoustic waves towards UAS to interfere with on-board electromechanical equipment in accordance with the present disclosure can include, but are not limited to: disrupting the camera feed (e.g. making it blurry, rotated, over / under-exposed, or complete disablement); disrupting the stability sensors (e.g. gyroscopes, altimeters, accelerometers, inertial measurement units (IMUs)); inducing instability by causing temporary failure modes in the drivetrain / feedback circuitry; interfering with oscillators causing other means of electromechanical failure; etc.

[0062] A system for disrupting electromechanical systems using acoustic energy in accordance with the present disclosure comprises a waveform synthesis module and one or more ultrasonic transducers that are driven by signals produced by the waveform synthesis module and are configured to generate acoustic waves. A power module provides DC or AC power to the device components. The powermodule is preferably modular in nature and may be tailored to deployment context. In some embodiments, various modules (e.g. the waveform synthesis module, an amplification module, and the ultrasonic transducers) may be implemented on a single printed circuit board, however one or more of the modules may be provided on separate dedicated circuit boards within the device or across different devices. It will be appreciated that the system may be implemented within a single device or be distributed using different connected devices / components.

[0063] The waveform synthesis module is configured to generate signals in an acoustic frequency band including a target resonant mode for disrupting a target electromechanical system. The target resonant mode may for example be the resonant frequency of the target electromechanical system, or an integer multiple of the resonant frequency. The signals from the waveform synthesis module are used to drive the one or more ultrasonic transducers to generate the acoustic waves. In some embodiments, an amplification module is configured to receive and amplify the signals from the waveform synthesis module to generate amplified waveforms, which can be used to drive the ultrasonic transducer array to generate acoustic waves.

[0064] The waveform synthesis module comprises a plurality of processing devices to generate the signals, such as field programmable gate arrays, microcontrollers, application-specific integrated circuits, and / or digital signal processors (or other programmable or fixed-function hardware components that provide control functionality), which generate the signals at a target phase, a target frequency, and / or a target amplitude. The target phase, frequency, and / or amplitude may be communicated to the processing devices by one or more controllers, which may for example be microcontrollers respectively coupled to a subset of the processing devices. A computing unit of the system may communicate targeting parameters of the target electromechanical system, such as a type of the target electromechanical system, a distance of the target electromechanical system from the ultrasonic transducers, and / or a resonant frequency of the target electromechanical system, to the controllers, which execute an algorithm to calculate the target phase, frequency, and / or amplitude for the waveform synthesis module to generate the signals.

[0065] The system may further comprise detection equipment, such as distance measuring equipment including cameras and / or other sensors (including radar, LiDAR, etc.), configured to detect and determine the distance to the target electromechanical system. The computing unit may receive data from the detection equipment for classifying the target electromechanical system and determining the targeting parameters. The computing unit may run one or more Artificial Intelligence (Al) algorithms that are contextually trained for determining a location / distance of the target electromechanical system and / or a type (e.g. make / model) of the target electromechanical system.

[0066] Additionally or alternatively, one or more transducer elements of the ultrasonic transducers may be configured to detect acoustic signals from the target electromechanical system in an active listening mode. In the active listening mode, a subset of the transducer elements may be used to detect an acoustic signature by powering off the amplification stage to the subset of transducers in the array and sampling the signal produced at the terminals of the transducer elements. The computing unit may execute a further Artificial Intelligence model (e.g. referred to as an acoustic listening Artificial Intelligence model) trained to determine the resonant frequency of the target electromechanical system and / or the distance of the target electromechanical system from the device using the detected acoustic signals.

[0067] A central command and control server may be in communication with the system to communicate auxiliary data to the computing unit and coordinate actions of multiple systems.

[0068] A method for disrupting electromechanical systems using acoustic energy is also disclosed, such as using the system described herein. The method comprises classifying a target electromechanical system to determine targeting parameters for targeting the electromechanical system; generating acoustic waves in an acoustic frequency band including a target resonant mode for disrupting the target electromechanical system based on the targeting parameters; and emitting the acoustic waves toward the target electromechanical system.

[0069] The devices, systems, and methods in accordance with the present disclosure thus provide a flexible, non-kinetic, precise, and effective method to neutralize UAS threats (or other electromechanical systems) in real-time with minimal collateral risk and without causing unintended harm. These solutions are particularly valuable as they can be safely deployed in both rural and urban environments, addressing a wide range of security concerns, in both civilian and defense contexts, with applicability across defense, security, and critical infrastructure use cases.

[0070] While the present disclosure primarily describes disrupting electromechanical systems onboard UAS systems to neutralize the threats of UAS targets, it will be readily apparent that the systems and methods disclosed herein can be used to disrupt other targets comprising electromechanical systems, such as any target that has microelectromechanical (MEMS) devices. A resonant frequency of electromechanical systems can be determined (e.g. by performing a frequency sweep, and / or by looking up resonant frequencies from known types of electromechanical systems) and the resonant frequency (or a mode of the resonant frequency)can targeted so that acoustic waves can be used to bombard the electromechanical system and cause mechanical vibrations around the resonant frequency, and thereby disrupt the electromechanical system and / or cause malfunction.

[0071] Embodiments are described below, by way of example only, with reference to Figures 1-7.

[0072] FIG. 1 shows a representation of a system architecture for disrupting electromechanical systems using acoustic energy. System 100 includes a number of components that are integrated together or operate together to provide countermeasures against threats containing electromechanical systems. The system 100 may be implemented on a single device, or distributed across multiple devices. The system 100 can be used to detect, classify, and disrupt electromechanical targets (e.g. onboard a UAS) using acoustic energy transmitted in the ultrasonic (e.g. 20-70kHz, or more particularly 20-40kHz) and near-ultrasonic (e.g. 12-20kHz) frequency ranges. The system 100 may communicate with other devices or similar systemsand / or a central command and control center for improved countermeasure capabilities such as to coordinate attacks amongst a plurality of systems / devices.

[0073] As shown in FIG. 1, the system 100 comprises a layered architecture that includes a power module 102, an acoustic wave synthesis layer 104, and a detection / classification layer 112. The system 100 may be implemented in various form factors depending on desired application, however the high-level architecture remains the same.

[0074] The power module 102 supplies regulated energy to each active element of the device (e.g. the modules in the acoustic wave synthesis layer 104 and the detection / classification layer 112). A modular design of the power supply may be used to facilitate easy troubleshooting and maintenance, as individual components can be easily isolated and serviced without affecting the entire system. This modular approach not only simplifies repairs but also allows for scalability and adaptability in different operational scenarios, ensuring the system remains robust and efficient under various conditions. The power module 102 can take on a variety of form-factors and implementations, depending on the deployment setting and the use-cases of the system 100, which may for example be implemented as human-portable systems, vehicle-mounted systems, and ground-based systems.

[0075] For human-portable designs, the power module 102 may employ a buck-boost converter to up-convert 24V-DC from a lithium power bank (similar to those used in portable phone chargers) to operational voltage levels for the transducers. This range is typically in the 72V-DC to 120V-DC range, depending on the specific make and model of the transducer being driven. The power module 102 for human-portable applications may for example support a maximum power throughput of 600W.

[0076] For vehicle-mounted systems, the power module 102 may employ a larger-scale buck-boost converter which can handle much higher power than the human-portable system. The larger-scale buck-boost converter typically peaks at 3kW peak power. The voltage range to the transducers may be similar as in the human portable case, but the input 24V-DC may be supplied by a bank of LiPObatteries, which are strung together in a series-parallel configuration. The power module 102 may also comprise a battery-charger switching interface, which can cycle the batteries through a charge-cycle when connected to external power. The power module 102 for vehicle-mounted systems may for example be adapted to interface with police cruisers or military vehicles, which have an internal 24V-DC output from their alternators. The hardware may be customized for different makes / models of vehicles according to their alternator specs and other loads attached to the system.

[0077] For ground-based systems, power may be supplied to the system through mains electricity (120V-AC RMS, 60Hz in North America). The power module 102 for ground-based systems may employ a rectifier, transformer, and regulator to transform the AC voltage to high-voltage DC in the 70V-120V range. The power module 102 can be made to operate at powers upwards of 4.2kW. Three-phase and higher-voltage systems can increase this power throughput specification.

[0078] The acoustic wave synthesis layer 104 comprises a waveform synthesis module 106, an amplification module 108, and one or more ultrasonic transducers 110 (e.g. provided as an ultrasonic transducer array) for generating acoustic waves at a desired amplitude and frequency for disrupting electromechanical systems. In some implementations, each of the waveform synthesis module 106, amplification module 108, and ultrasonic transducers 110 may be provided on a single printed circuit board. While the amplification module 108 is a practical implementation of the acoustic wave synthesis layer 104, it is also possible that the amplification module 108 may be incorporated within the waveform synthesis module 106, or the waveform synthesis module 106 may generate signals at a suitable amplitude for driving the ultrasonic transducers and thus the amplification module 108 may not be required.

[0079] The waveform synthesis module 106 is responsible for generating signals (e.g. time-domain signals) in the required acoustic frequency bands, including a target resonant mode for disrupting a target electromechanical system. The target resonant mode may comprise the resonant frequency of the target electrotechnical system, or an integer multiple of the resonant frequency. The signals from the waveform synthesis module 106 are used to drive the ultrasonic transducer, and mayinclude single-frequency tones, frequency sweeps, burst waveforms, or pseudorandom signal patterns.

[0080] The waveform synthesis module 106 may comprise a plurality of processing devices to generate the signals. Each processing device may be independently controllable to generate respective signals at a target phase, frequency, and / or amplitude. Accordingly, the signals can be generated to allow for the coherent addition of acoustic power at specified points in space. In some embodiments, the waveform synthesis module may include phasing components or a phasing circuit mechanism that are utilized to control the timing and phase of the signals sent to each transducer. In one embodiment, the mechanism includes digitally controlled circuits that can shift the phase of a given waveform precisely. The waveform synthesis module 106 allows a waveform to be sent to each transducer and be independently controlled, allowing acoustic waves emitted by each transducer to be synchronized to create a coherent and directed ultrasonic beam. However, although coherent beamforming is supported, it is not necessarily required to achieve disruption of target electromechanical systems and non-phase-aligned transmissions may be used to achieve desired effects.

[0081] The processing devices in the waveform synthesis module 106 used to generate the signals may for example comprise field programmable gate arrays (FPGAs), microcontrollers, application-specific integrated circuits (ASICs), and / or digital signal processors (DSPs). In some embodiments, the waveform synthesis module 106 may be implemented using a FPGA chip that produces the signals using pulse width modulation (PWM)-based direct digital synthesis (DDS). The waveform synthesis module 106 may further comprise one or more controllers, e.g. one or more microcontrollers, that communicate the target phase, the target frequency, and / or the target amplitude to the plurality of processing devices.

[0082] As described in more detail below, the one or more controllers may receive targeting parameters such as a type of the target electromechanical system, a location / distance of the target electromechanical system from the ultrasonic transducer array, and / or a resonant frequency of the target electromechanical system, and compute the target phase, frequency, and / or amplitude for communication to theprocessing devices. Typically, at least the resonant frequency of the target electromechanical system should be provided for determining the target resonant mode, however in cases where the resonant frequency is not known (or not precisely known) a pseudo-random signal pattern can be generated for example. A subset of the processing devices may be coupled to a respective controller. For example, each FPGA may communicate with a microcontroller in a given ratio, e.g. 7:1 ratio. The use of microcontrollers helps to break down the computation to allow for near-real time calculations and adjustments to the phase, frequency, and / or amplitude based on changing targeting parameters (e.g. a changing real-time location of the UAS in the air). Each transducer of the ultrasonic transducers 110 as described below may be individually mapped out utilizing an algorithm, which may be used to control the signals generated by the waveform synthesis module 106 to increase constructive interference and reduce the number of antinodes in any chosen direction. The mapping also allows the transducers to form a phased array which is then capable of beamforming. This mapping can be used in varying geometries and give optimal outputs in each case. In some instances, the drivers in the waveform synthesis module are customized to operate in the ultrasonic frequency range at between about 20kHz to about 40kHzand / or the near-ultrasonic frequency range at between about 12kHz to about 20kHz.

[0083] The signals generated by the waveform synthesis module 106 are used to drive the transducers in the ultrasonic transducer array 110, where the signals may be amplified by the amplification module 108 to generate amplified waveforms. The purpose of the amplifiers is to convert the low-voltage, high impedance PWM signal from the direct digital synthesis stage to a high-voltage waveform which can drive the capacitive load of the transducers, at a maximal power output level. The amplification module 108 may for example comprise one more transistor amplifiers, such as push-pull, Class-D style MOSFET amplifiers. Each transducer or a cluster of transducers may be driven by a respective MOSFET-pair amplifier. Each amplifier may include gate drivers, switching transistors, output filters, and protection logic. Each amplifier can thus drives a single or small cluster of transducers at a uniform phase and frequency.

[0084] The ultrasonic transducers 110 is / are configured to receive the signals I amplified waveforms and generate acoustic waves by converting electrical energy into acoustic pressure waves. Each transducer element is capable of emitting acoustic waves at specific frequencies and amplitudes. Operating frequencies of the transducer elements are typically in the ultrasonic frequency range of between 20 and 40kHz, or near-ultrasonic frequency range of 12-20 kHz for specific failure mode excitation. An example implementation of the ultrasonic transducers may use high-power piezoelectric transducers (e.g. PZT ceramics). In some embodiments, the ultrasonic transducers may be arranged as a phased array. The ultrasonic transducers 110 may be arranged on a planar or curved surface. The ultrasonic transducers may be mounted directly to a PCB which is then mechanically and electrically connected to the rest of the system.

[0085] FIGs. 2A and 2B shows a representation of an example ultrasonic transducer array. The example ultrasonic transducer array as shown in FIGs. 2A and 2B uses a hexagonal arrangement of the ultrasonic transducers. Specifically, FIG. 2A depicts a transducer array unit 200 comprising a plurality of individual transducer elements, arranged using hexagonal packing. In the transducer array unit depicted in FIG. 2A, 9 layers / rows of between 5 to 9 transducer units are arranged hexagonally, providing 61 total transducer elements. FIG. 2B depicts a transducer array 202 comprising a plurality of transducer array units 200 as shown in FIG. 2A. Note that the transducer array units can also be packed hexagonally to form the transducer array as shown in FIG. 2B. This particular transducer array embodiment makes use of hexagonal-close-packing arrangements with a packing factor of 0.74, although other packing arrangements (e.g., square packing) and packing factors are possible as well. The result is that 74% of the space on the PCB is occupied with transducer elements. Note that the size and dimension of the transducer array as shown in FIGs.2A and 2B are not to scale and are provided for the sake of example only and a re non-limiting. A different number of transducer array elements in a transducer array unit 200, and a different number of transducer array units 200 in the transducer array 202 may be possible, along with alternative arrangements of the transducer array elements and / or transducer array units.

[0086] The packing may be adjusted based on factors such as thermal considerations. In particular, the transducer elements can be spaced at a set distance (e.g. 1mm) apart along their diameters. The close packing allows for avoiding grating lobes and maximizing the acoustic power projected in a beam. In at least some embodiments, the set distance can be adjusted to balance beamforming and vibrational coupling. That is, the set distance should be small enough to not interfere with beamforming but large enough to avoid mutual vibrational coupling between adjacent transducer units.

[0087] Referring again to the system architecture shown in FIG. 1 , the system 100 also comprises a detection / classification layer 112 that may comprise Al-enabled detection, classification, and targeting based on active listening and / or visual / other sensors.

[0088] The active listening module 114 may receive spectral data from a subset of transducer elements of the ultrasonic transducers 110 that have been repurposed as microphones. That is, a subset of the ultrasonic transducers may be used for active listening to characterize the audio profile of the surrounding airspace and identify potential electromechanical targets by their acoustic signature. In accordance with some embodiments, the acoustic signature is detected by powering off the amplification stage to a small subset of transducers and sampling the signal produced at the terminals of the transducer elements. Low-noise amplifiers (LNAs) may be used to filter and boost the signal, which can then be digitized by means of an analog to digital converter (ADC) and analysed spectrally by the active listening module 114 using a Fast Fourier Transform (FFT). The listening nodes pickup the ambient sound waves produced by adjacent driven transducers, but this is effectively noise - the system is interested in sounds near (but not equal to) the driven frequency. Effectively, the system picks up the intermodulation products of the broadcasted acoustic waves and the acoustic products from interactions with the victim UAS.

[0089] The spectral data from the active listening module 114 can be passed to classification and targeting module 120, which may for example comprise a deeplearning framework which aids in providing targeting and classification information about the target electromechanical system and / or UAS. The deep learning frameworkmay comprise a machine-learning model to identify optimal disruption frequencies and assist in tracking. The machine learning model(s), such as convolutional neural networks (CNNs) for spectral feature extraction and recurrent neural networks (RNNs) or transformer-based architectures for temporal pattern recognition, are used to identify frequency bands and waveform structures that are likely to induce disruptive effects in a targeted drone. From the active listening module 114, incoming acoustic data is processed to extract dominant spectral modes, which can be converted into feature embeddings using Fast Fourier transforms (FFT) or wavelet decompositions, capturing key characteristics such as blade-passing frequencies, harmonic emissions from onboard MEMS devices, and subtle shifts in flight dynamics during prior acoustic interference events. These embeddings, optionally along with metadata from historical attack attempts, can be used as input to the model. The model is trained to classify which frequency bands are most likely to produce desired effects — such as control signal disruption, instability, or forced landing — in the target. Once classification is complete, the system may further execute a rapid frequency sweep focused on the model’s top-ranked bands. A closed-loop feedback mechanism, supported by a probabilistic model such as a Bayesian neural network or reinforcement learning policy, may continuously evaluate the acoustic response of the drone. Based on this feedback and its learned priors, the model can adaptively refine the transmitted waveform until an effective mode of disruption is achieved.

[0090] Accordingly, the outputs from the classification and targeting module 120 may comprise targeting parameters that can be provided to the waveform synthesis module to determine the frequencies which will be effective in targeting specific electromechanical systems (e.g. particular subsystems within a target UAS). Using multiple listening nodes can serve as a sonar means of tracking the UAS through space. This method of active listening uses the reverse-logic of beamforming, looking for spatial differences in the acoustic signature emanating from the victim UAS.

[0091] In addition or as an alternative to active listening, the system may comprise other detection equipment 116. such as distance measuring equipment including visual and / or other sensors for detecting and / or determining aposition / distance of a target electromechanical system and / or UAS. Visual and other sensors may for example include a distance measurement or measurement array or component that includes a camera system and / or a distance measurement sensor (e.g. LiDAR, radar, etc.). For example, a low-energy laser, which is safe and nonharmful, can be used to provide precise distance measurements.

[0092] Detection algorithm(s) 118 may receive data from the detection equipment to determine the position of the UAS and / or distance from the system to the UAS. To enhance accuracy and account for varying environmental conditions, one or more Al models may be employed. For example, Al model(s) may receive camera image inputs for detecting a target and tracking the target location. One Al model may be a distance estimation Artificial Intelligence model trained to process data from the distance measurement equipment and estimate the distance of the target electromechanical system from the device. Another Al model may be an electromechanical system identification Artificial Intelligence model trained to process images from the camera and determine the type of the target electromechanical system. These Al models may be trained using data acquired from the system over time. The integration of a laser for distance measurement and Al models for target detection and tracking allows for high accuracy in distance measurement, and they may operate in unison to provide robust and reliable data but can also function independently, if necessary. This dual-method approach may help to ensure precise distance determination, which is important for maintaining the focused effectiveness of the ultrasonic beam on a moving target. Outputs from the detection algorithm(s) 118 can be provided to the classification and targeting module 120, which develops targeting parameters that can be input to the waveform synthesis module 106 for determining the target phase, frequency, and amplitude of signals to generate, as described above.

[0093] Accordingly, the system 100 provides a feedback loop that allows for dynamically adjusting target parameters based on real-time data from a distance measurement device. The detection / classification layer 112 may be used to continuously measure the position / distance of the system to the moving target,allowing the system to maintain precise beam focus when beamforming is employed and effectiveness against the target.

[0094] The effect of the acoustic waveform on the target electromechanical system is determined by the parameters of the acoustic waveform being sent towards the target. Globally, these are the amplitude and frequency of the acoustic waves. The sound produced by the ultrasonic array covers a solid angle wherein the acoustic pressure is maximal. By controlling the phase of the sound produced by each individual element, the direction and width of the beam can be controlled. Furthermore, by methodically controlling the phases for a large number of transducers, secondary lobes of maximal acoustic radiation can be created, along with dead spots where the pressure amplitude is at a null. Creating these nodes and anti-nodes can allow for multi-engagement of enemy UAS, while friendly UAS can fly, unimpeded, in the airspace.

[0095] As the waveform synthesis algorithm generally relies on some form of localization or tracking of the target electromechanical system, the waveform synthesis module 106 preferably receives as an input a position signal, refreshed every few milliseconds (or faster). The position signal may for example be in the format of a spherical position vector, relative to some fixed datum on the earth (or vehicle, if moving), which is set during calibration. At calibration, the distances of each transducer element may be calculated, precisely, to the datum, which allows for calculating the phase-shift produced by each element, at a point, as a function of the frequency. These are used as the constants in a linear system which is constructed and used in the waveform synthesis module 106.

[0096] The frequency of the wave broadcasted is the primary determinant of what sorts of effects a targeted electromechanical system will experience. The effects are expected to occur when the frequency is a resonant mode of the target system (e.g., a MEMS gyroscope) and the amplitude is above a certain threshold (e.g. such that oscillations induced in the MEMS are sufficient to saturate the analog backend of the device).

[0097] FIGs. 3A and 3B shows a functional block diagram of the system shown in FIG. 1. While the components of the system as shown in FIGs. 3A and 3B can be implemented as a singular unified system, each of the individually depicted components may be independent modular components or modularly assembled.

[0098] Referring first to FIG. 3A, a power supply unit 302 is depicted, configured to provide power to the system and / or associated components and devices. In a particular embodiment, the power supply unit 302 can correspond to the power module 102. The power supply unit 302 can comprise an AC / DC voltage converter 304. The converter 304 is configured to suitably convert the received current for powering any devices and components connected thereto, as described above. For example, the converter 304 can convert conventionally received AC current to DC current. The power supply unit may also comprise a DC voltage supply 306, which can be implemented as a power source for the system, for example as chargeable batteries, although the voltage supply may also be provided through an automotive or mains supply (depending on system implementation). During charging, current can be passed through the converter 304 to convert the current to DC for powering the voltage supply 306, which subsequently powers the system using DC voltage. In order to power components and devices, current from the voltage supply 306 can first pass through a low voltage supply 310, configured to regulate / convert the higher voltage currents to a lower voltage as well as to store the low voltage current. Similarly, a converter module 308 can transform current from the voltage supply 306 to higher voltage currents to power the appropriate high voltage component / devices. It should be noted that high and / or low voltage current can be utilized within the system. Functionalities and parameters within the power supply unit 302 can be suitably controlled via a sensor and control microcontroller unit 312, which monitors and controls the components within the power supply unit 302 and operations thereof. As described above, the power supply unit 302 can be modularly implemented, utilizing multiple power supplies to enhance the system's flexibility and reliability. Note that the components of the power supply unit 302 (e.g., power supplies) can be switchable and may incorporate built-in fail-safes and fuses to improve safety.

[0099] The power supply unit 302 can be controlled and monitored by a computing unit 318, which may be implemented on the device or in remote communication with the device. The power supply unit 302 may be in communication with the computing unit 318 through Universal Asynchronous Receiver / Transmitter, although other forms of communication such as Inter- Integrated Circuit (l2C), Controller Area Network (CAN), Serial Peripheral Interface (SPI), Universal Serial Bus (USB), and Bluetooth are also possible.

[0100] The computing unit 318 is also configured to perform the classification / detection and to control acoustic wave generation, as described above. For example, the computing unit 318 can provide and implement the Al model(s) and / or other algorithms for electromechanical system identification, classification, and targeting (e.g. which may be stored as non-transitory computer-readable instructions and executed by one or more processing devices). The computing unit is in communication with one or more functional units 316, which may for example correspond to the acoustic wave synthesis layer 104 and the detection / classification layer 112 described with reference to FIG. 1. Accordingly, the computing unit 318 integrates with the functional units 316 and any other detection equipment to provide detection and tracking functionality and provide targeting parameters to the functional units 316 for waveform generation. This integration allows the system to respond rapidly to dynamic changes in the UAS's position and movement. In some embodiments, the computing unit 318 (or some functionality of the computing unit 318) can be implemented on the functional units 316.

[0101] The functional units 316 may be modular units controlled to independently or jointly perform acoustic wave generation and / or signal detection. Note that the functional units 316 may be physically coupled (e.g., implemented as a unified system) or arranged separately (e.g., at different locations). In some embodiments, the system comprises 10, 20, 30, 40, or more communicatively coupled functional units. In a particular, embodiment, the system comprises 49 functional units 316. The computing unit 318 may also receive sensor data (e.g. temperature, current, etc.) from the functional units 316 to ensure that each unit is operating properly and maintain robustness of the system.

[0102] The functional units 316 may be communicatively coupled to the computing unit 318 in a local network environment (e.g., LAN) via high-speed ethernet. In particular, an optional network switch 314 can be used to manage data transmission and signal communication between the computing unit 318 and the functional units 316. Note that other forms of communication such as Wi-Fi, Bluetooth, and near-field communication are also possible. For example, the computing unit 318 can be remotely / wireless coupled to the functional units 316 via a communications network 322 (e.g., the internet). Accordingly, the power supply unit 302, computing unit 318, and functional units 316 can be implemented as separate devices coupled to each other or as a single device as required based on system use case.

[0103] The computing unit 318 may also be communicatively coupled to an external server 320, which may for example be a command and controller server, for example wireless via the communications network 322. The computing device 318 and accordingly the depicted system can be controlled and monitored via the server 320. Further, any data collected by the system may be provided to the server 320 for analysis and storage. Various auxiliary data can also be provided from the server 320. For example, if the computing unit 318 identifies a target electromechanical system (e.g. a target UAS), it can communicate the detection result to the external server 320, which can indicate whether the identified target is friendly (in which case the target should be ignored) or enemy (in which case the target should be neutralized).

[0104] The implementation of the external server(s) 320 is not restrictive and they may be implemented on-premises, on-cloud-based, or a hybrid thereof, for example. A user may interact with the servers 320 via a device (not shown). A graphical user interface (GUI) may be provided at the server 320 for ease of communication and operation control by the user. The implementation of the GUI is not restrictive and may be, for example, a mobile / computer application or a web page. The GUI can be used to provide input to and receive output from the servers 320, or vice versa. The GUI can also allow the user to monitor and make manual adjustments to the system, thus providing users with real-time status updates and the ability to fine-tune system parameters as needed. Additionally or alternatively, other userinterfaces, such as an audio interface that allows receipt and processing of spoken commands, and that outputs spoken narratives, may be used.

[0105] As described above, the computing unit 318 can perform electromechanical system detection / classifying / targeting. The computing unit 318 can determine targeting parameters by implementing one or more machine learning / Al algorithms, and provide the targeting parameters to the functional units 316 for generating acoustic waves based on the targeting parameters. Each machine learning model can be an artificial intelligence (Al) model or algorithm, a machine learning model or algorithm, a neural network, and may comprise, in particular, a classifier. The machine learning models can stored on the computing unit 318 or the server 320.

[0106] The computing unit 318 and server(s) 320 can each comprise a CPU, a non-transitory computer-readable memory, non-volatile storage, an input / output interface. The non-transitory computer-readable memory comprises computerexecutable instructions stored thereon at runtime which, when executed by the CPU, configure the server / computing unit to execute methods as described herein. The non-volatile storage has stored thereon computer-executable instructions that are loaded into the non-transitory computer-readable memory at runtime. The input / output interface allows the server / computing unit to communicate with one or more external devices. The non-transitory computer-readable memory may also have stored thereon the machine learning models for signal processing. A graphical processing unit (“GPU”) may be used to control a display and may be used to execute machine learning models.

[0107] The CPU and GPU may be one or more processors or microprocessors, which are examples of suitable processing units, which may additionally or alternatively comprise an artificial intelligence accelerator, programmable logic controller, a microcontroller (which comprises both a processing unit and a non-transitory computer readable medium), neural processing unit (NPU), or system-on-a-chip (SoC). As an alternative to an implementation that relies on processor-executed computer program code, a hardware-based implementation may be used. For example, an application-specific integrated circuit (ASIC), field programmablegate array (FPGA), or other suitable type of hardware implementation may be used as an alternative to or to supplement an implementation that relies primarily on a processor executing computer program code stored on a computer medium.

[0108] Referring now to FIG. 3B, each functional unit 316 can comprise one or more mini-array functional units 322. The mini-array functional units 322 can be modular functional units configured to perform acoustic wave generation and electromechanical system targeting. In some embodiments, the number of mini-array functional units 322 in each functional unit 316 can be 5 or more, and can for example be 7 in a particular implementation. Specifically, each functional unit 316 can comprise a controller such as a microcontroller or microprocessor 330 communicatively coupled to the computing unit 318 (e.g., via the network switch 314) to control the mini-array functional units 322 to perform acoustic wave generation, as described above. Again, in some embodiments the computing unit 318 may be implemented as a portion of the functional units 332, such as within microcontroller 330. Further, each mini-array functional unit 322 may comprise one or more processing devices, such as field programmable gate arrays, microcontrollers, application-specific integrated circuits, and / or digital signal processors, communicatively coupled to the microcontroller 330.

[0109] In an exemplary operation, the microcontroller 330 can determine or otherwise obtain / receive the targeting parameters, such as a type of the target electromechanical system, a distance of the target electromechanical system from the ultrasonic transducers, and / or a resonant frequency of the target electromechanical system, from the computing unit 318. The microcontroller 330 can determine, using one or more algorithms as discussed below, a target phase, a target frequency, and / or a target amplitude of acoustic waves to be generated by the ultrasonic transducers, and communicate these to the FPGA 334. The FPGA 334 generates the appropriate signals. In some embodiments, the FPGA 334 may be coupled to the microcontroller 330 using the Serial Peripheral Interface protocol to enable efficient data transfer to and from the mini-array functional units 332 for real-time data processing, although other coupling methods are possible as well.

[0110] Each FPGA 334 can be communicatively coupled with one or more transmission (TX) functional units 338. In some embodiments, the number of TXfunctional units 338 in each mini-array functional unit 332 can be 3 or 5 or more and can be 7 in a particular implementation. Each TX functional unit 338 can be coupled to a respective receiving Analog-to-Digital Converter (ADC) 346. Alternatively, a single receiving ADC can be coupled to all TX functional units 338. The TX functional unit 338 can correspond to the acoustic wave synthesis layer 104 and the receiving ADC 346 can form part of the detection layer 112 by implementing active acoustic listening. Each TX functional unit 338 can comprise an amplifier 340, filter 342, and transducer(s) 344.

[0111] The amplifier 340 can receive PWM signals from the FPGA 334 and convert the PWM signals to drive the transducers 344, which are configured to generate the acoustic wave to disrupt the electromechanical system, as described above. The filter 342 can be implemented as a part of the amplifier 340 and can be configured to filter / eliminate harmonics and out-of-band noise from the received signals. The amplifier 340 can be one or more custom amplifiers specifically rated for ultrasonic frequencies as to drive the piezoelectric devices (e.g., transducers 344). These custom amplifiers may be selected based on performance and efficiency at the required operating frequency range of the transducers 344.

[0112] The transducers 344 are configured to generate the acoustic waves in the ultrasonic or near-ultrasonic frequency range for targeting the electromechanical system, as described above. Example acoustic waves generated by the transducers 344 may include single-frequency tones, frequency sweeps, burst waveforms, or pseudo-random signal patterns. These patterns can be tuned to exploit known vulnerabilities in UAS subsystems, including MEMS gyroscopes, flight controllers, oscillators, and camera sensors. As described above, the ultrasonic beam generated by the transducers can interfere with the gyroscopes and Inertial Measurement Units (IMU) of the UAS. The (resulting) high-frequency vibrations can disrupt MEMS gyroscopes at their resonant frequency mode, inducing instability in the UAS's flight control systems, which can result in erratic movements and a loss of stable flight for the UAS.

[0113] The receiving ADC 346 can receive audio data from the transducers 344, as described, which can be transmitted to the computing unit 318 (via FPGA 334and microcontroller 330) for processing to identify UAS targets. In particular, the receiving ADC 346 can comprise listening logic circuits which digitalize audio signals from the transducers 344. The receiving ADC 346 and the FPGA 334 can be coupled using standard control bus and bitstream protocols.

[0114] In some embodiments, the FPGA 334 is responsible for the synthesis of the acoustic wave output by the transducers 344. Further, the microcontroller 330 can be used to compute the solution to the beamforming problem (e.g., to target to UAS) as described below as well as to relay phase and frequency information of the acoustic wave to be generated to the FPGA 334, for example over a communicationline (e.g., SPI bus) implemented in the functional unit 316 (e.g., on-board). The microcontroller 330 also communicates with the computing unit 318, allowing the computing unit 318 to determine the UAS targeting parameters (e.g., where, in space, the focal points for beamforming should be if applicable). To handle the real-time demands of the system, the FPGA 334 can use robust communication protocols to ensure fast and reliable data exchange among all components.

[0115] In some embodiments, the FPGA 334 can control the phase delays for each ultrasonic transducer 344 with high precision, which can be adjusted based on feedback from the sensors (e.g., cameras and lasers, not shown) to ensure that the ultrasonic beam remains accurately focused on the moving UAS, adapting swiftly to changes in distance and environmental conditions. In particular, the computing unit 318 can process data using Al algorithms that analyze visual and / or other sensor input to identify and track the target UAS, determine targeting parameters which are communicated to the microcontroller 330, which in turn determines the target phase, the target frequency, and / or the target amplitude for communicating to the FPGA 334.

[0116] As described above, the various controllers of the system can jointly operate to orchestrate the operation of the system, ensuring seamless integration and coordination among its various components. In one specific embodiment, the FPGA 334 may be selected to control various aspects of acoustic wave generation and beamforming for its superior speed and capability to handle complex, high-speed signal processing tasks. The FPGA 334 can be important for managing the precisephase delays required by the system, as microcontrollers may not be not fast enough to meet these demands.

[0117] As depicted in FIG. 3B, each mini-array functional unit 332 can also comprise various sensors 336 configured to provide a status of the mini-array functional unit 332 to the FPGA 334 to ensure that the mini-array functional unit 332 is functional. For example, the sensors 336 can determine a temperature status (e.g., overheated, via temp-sense), power-status (e.g. ON / OFF), and component status (e.g. components working, via i-sense) of the mini-array functional unit 332. Additionally, the sensors 336 can comprise audio / visual sensors for detecting the UAS, as described above.

[0118] Note that redundancy and fail-safe mechanisms can be built into the system. For example, in the event of a component failure (e.g., functional units 316, 332, 338), the system can automatically switch to backup units (e.g., one of the other functional units 316, 332, 338) or adjust its operational parameters to maintain functionality. This can ensure that the electromechanical system neutralization process is both reliable and resilient, capable of withstanding unexpected disruptions.

[0119] Note that at least some portions of the system (e.g., the functional units 316, 332, 338) can comprise or be implemented using circuit(s) designed to regulate the amplitude and frequency of the signals sent to the piezoelectric drivers (e.g., transducers 344). This circuit can ensure that the transducers 344 operate within the optimal frequency range, maintaining the necessary power and consistency for effective acoustic wave generation.

[0120] FIG. 4 shows a method 400 of disrupting electromechanical systems (e.g. a UAS). The method 400 can be implemented by the system as described with reference to FIGs. 1-3B.

[0121] At 402, the target electromechanical system is classified to determine targeting parameters for targeting the electromechanical system. For example, classifying the target electromechanical system may comprise determining a distance to the electromechanical system. Additionally or alternatively, classifying the target electromechanical system may comprise determining a type (e.g. a make and / ormodel) of the electromechanical system (or a type of UAS having the electromechanical system onboard). Additionally or alternatively, classifying the target electromechanical system may comprise performing a frequency sweep by generating a plurality of acoustic waves in a range of frequencies; capturing audio data corresponding to feedback to the frequency sweep from the electromechanical system; and analyzing the audio data to determine one or more frequency peaks for the target electromechanical system.

[0122] At 404, acoustic waves are generated based on the targeting parameters. The acoustic waves are generated in an acoustic frequency band including a target resonant mode for disrupting the target electromechanical system. At 406, the device emits the acoustic waves toward the target electromechanical system, for example until the electromechanical system is disabled. The targeting parameters may for example comprise a type of the target electromechanical system, a distance of the target electromechanical system from the ultrasonic transducer array, and / or a resonant frequency of the target electromechanical system. In some embodiments, a database may store a plurality of targeting parameters corresponding to a plurality of types of electromechanical systems; and the targeting parameters for the type of the electromechanical system can be looked up (e.g. to determine resonant frequencies associated with that target system). Based on the targeting parameters, a target phase, a target frequency, and / or a target amplitude can be determined for disrupting the electromechanical system. One or more ultrasonic transducers may be driven to generate the acoustic waves based on the target phase, the target frequency, and / or the target amplitude.

[0123] FIG. 5 depicts a further method 500 for disrupting electromechanical systems, according to an example embodiment. The method 500 can be used for disrupting a target UAS. At 502, the target UAS can be identified by the system. The UAS can be identified as described above, for example by the detection / classification layer 112 in the system architecture. Based on the identification of the UAS, suitable acoustic waveforms for disrupting the UAS can be determined.

[0124] For example, once identified (“Yes” at 502), the distance from the system, in particular the transducers generating the acoustic wave, to the UAS canbe measured at 504. Specifically, to identify the position of the UAS relative to the system, cameras 508 and lasers 506 may be used. In particular, the presence of the UAS can be detected and measured using a combined approach of cameras 508 coupled to Al model(s) and (low-energy) laser ranging 506. The Al model can utilize visual data from the cameras to identify and track the UAS, while the laser can provide precise distance measurements to the UAS. This dual-method approach can ensure robust and accurate detection and tracking of the UAS in real-time, thus providing continuous location data to control system.

[0125] At 510, the position of the UAS as well as any data or feedback corresponding a previous iteration of generated acoustic waves are processed by the system, for example by the FPGA. At 512, the system (e.g., FPGA) determines the parameters of the acoustic waves suitable for disrupting the UAS, as described above, which in this example method are used to provide beamforming (although in other implementations beamforming may not be required to disrupt a target electromechanical system). The parameters can be determined / adjusted based on the real-time updates to the position of the UAS as well as the response of the UAS to the previous iteration of generated acoustic waves (e.g., previously transmitted acoustic waves).

[0126] In one embodiment, for a UAS at a distance of 15m (focus), aligned directly (e.g., on the same axis) with the system and with a 25kHz signal, the below considerations and calculations can provide the phase delay for the transducer:

[0127] Note that the system may comprise a large number of transducers that are densely packed. Accordingly, it is possible to approximate the transducer array to be radial and continuous. With reference to the equations below, A is the wavelength of the acoustic wave, vpis the phase velocity of the wave (e.g. the speed of sound), f is the frequency of the signal being produced, k is the wavenumber, p is the phase of the transducer, r is the radial distance from the centre of the array to another transducer on the array, and x is the distance to the target. Assuming the centre of the array is on the same axis as the target. As such, — can represent the phase drgradient of the array.V 343 [—1[L00128]JA = ^ = - L^L = 0.01372[m]f 25OOO|Hz|L J

[0129] k = Y = 458.2 [^]

[0130] ^ = k^— k-, tf r « x. Therefore,— = 458.2 [— I x .dr / r2+x2x dr L m J 15[ ]kr2

[0131] As such, <p « — can be used to calculate the phase of a transducer located at a distance r from the centre of the array.

[0132] This process can be repeated for each strategically placed driver (e.g., transducer) until the generated acoustic waves are focused on a desired point (e.g., the UAS). Note that, for the same transducer, if the desired point or target (e.g., the UAS) were to move, the focus changes and hence changes the < >phase delay.

[0133] At 514, the signals are provided to the transducers, which generate the corresponding waveform to disrupt the UAS. That is, once the suitable phase / frequency settings are determined, the signals are generated and drive the transducers to emit a coherent ultrasonic beam directed at the UAS. The signals from the FPGA (which may be subsequently amplified) can drive the piezoelectric devices (e.g., the transducers) within an ultrasonic frequency range of about 20kHz to about 40kHz.

[0134] To neutralize the UAS, continuous disruption by the ultrasonic beam may be required to cause the UAS to lose control. Accordingly, effects of the generated acoustic wave on the UAS can be captured by sensors and provided as feedback, as shown in FIG. 5. That is, as the position of the UAS changes and based on the response of the UAS, the acoustic wave parameters are adjusted accordingly until the UAS is neutralized at 516 or the targeting is terminated. In particular, the FPGA can dynamically adjust the beam's frequency, amplitude, and direction in realtime to ensure it remains focused on the moving target. As the UAS loses control by means of the method 500, the induced instability can grow over time, eventually causing critical failure(s) in the UAS's control systems. The UAS then becomes unable to maintain flight and crashes. The system can ensure that this process occurs withminimal or reduced risk of collateral damage, making it suitable for both rural and urban environments.

[0135] In some embodiments, to target a UAS, a prediction is made by the system as to where the UAS will be in some small amount of time in the future, 8t. This corresponds to a linear system formulated as an optimization problem where the sound intensity at the point of interest, at time 8t will be maximal. Note that for many UASs and for the presence of friendly UAS, additional constraints can be introduced for running the optimizer. In one embodiment, a limitation is the ability for the system to solve the optimization problem in time 8t. That is, the system aims to subject the position of the UAS in the future with the maximum intensity acoustic waves by considering the distance to said position and the travel time of the acoustic waves. Also note that computational cost scales quadratically with the number of constraints, and therefore large swarms of UAS may require greater computational capacity. However, this can be addressed by distributing the workload over multiple processing units.

[0136] FIG. 6 shows a method 600 of classifying a target electromechanical system, in particular a UAS, according to an example embodiment. The method 600 may be used in combination with the method 500, for example to identify and classify the UAS at 502. The method 600 begins at 602 by identifying / determining / selecting a particular UAS to be targeted by the system. For example, the UAS may be visually identified by one or more visual sensors (e.g., cameras) and selected for targeting by the user or automatically selected by the system. Data from the visual sensor can used to determine a make and / or model (e.g., specifications) of the target UAS, for example using a classification algorithm or Al model. As described above, the make / model of the UAS determines acoustic waves suitable to disrupt the UAS, in particular the internal components thereof.

[0137] The UAS can be classified to determine the make / model of the UAS by processing sensor data with an internal or external UAS database at 604 to identify any matches, for example using a matching algorithm. The database can comprise a plurality of UAS data records, each data record can identify a particular make or model of UAS as well as parameters of the acoustic waves effective in disrupting said UAS.In some embodiments, each record can also comprise a list of components within the UAS (e.g., gyroscopes, IMU, etc.) as well as the parameters (e.g., resonant frequency) of the acoustic waves effective in disrupting said components. In some embodiments, a plurality of sets of parameters can be included in the database for each component / UAS, each set corresponding to a particular disruption effect. That is, each component and / or UAS can be associated with a plurality of disruption effects (e.g. movement disruption, flight disruption, etc.) and their corresponding acoustic wave parameters. The parameters may be pre-determined or empirically determined. For example, data for a plurality of known (e.g., publicly available) UAS may be retrieved and analyzed by algorithms / AI model(s) to determine the corresponding acoustic wave parameters, which are subsequently stored. Alternatively or additionally, parameters of acoustic waves successful in disrupting the UAS during previous device operations can be stored with the corresponding UAS.

[0138] If a match is found (“Yes” at 604), that is, the UAS data (e.g., make / model) is present in the database, the corresponding data can be output. In particular, the parameters corresponding to the target UAS and the desired disruption effects (e.g. target electromechanical system onboard the UAS) can be retrieved at 618. The parameters retrieved from the database can be used to generate the acoustic wave targeting the UAS, as described above. At 620, the system can capture the audio data / profile of the UAS in response to being targeted by the generated acoustic wave. For example, the sound profile of the acoustic waves re-emitted by the UAS after being targeted by the generated acoustic wave can be captured, digitized, and processed using FFT, as described above. At 622, the sound emitted by the UAS is analyzed (e.g. with the Al model) to determine whether the emitted sound (e.g., FFT results) matches the desired disruption effect (e.g., expected UAS response / behaviour). Further, peaking in the emitted sound profile from the UAS, would indicate that the generated acoustic waves match the resonant frequency of the UAS, thus effectively targeting the UAS, as described above. The confirmation of the disruption effect can also be confirmed using the visual sensors. If the desired disruption effect is observed (“Yes” at 622), the system will continue to target the UAS (616) until the UAS is disabled, the desired disruption effect is achieved, or thetargeting is terminated. However, if the desired disruption effect is not observed (“No” at 622), the method 600 proceeds to 606, as described below.

[0139] If a match for the UAS is not found within the database (“No” at 604), a frequency sweep is performed at 606. In particular, a desired disruption effect can be selected (e.g., by the user), the system can then generate a series of acoustic waves corresponding to a range of frequencies, directed at the UAS. At 608, the system can capture the audio data / profile of the UAS in response to being targeted by the frequency sweep. For example, the sound profile of the acoustic waves re-emitted by the UAS after being targeted by the generated acoustic wave can be captured, digitized, and processed using FFT, as described above. In particular, the sound emitted by the UAS can be analyzed (e.g. with the Al model) to determine whether any peaking is observed in the frequencies emitted by the UAS. Peaking (e.g., peak(s)) observed in the range of frequencies corresponding to the frequency sweep (e.g., resonant peaking) can be used to identify the resonant frequency of the UAS (where the frequency of the peak indicates the resonant frequency), which can be utilized to effectively target the UAS, as described above. If peaking is not observed (“No” at 612), the method 600 returns to 608 after adjusting the range of the frequency sweep 610. However, if peaking is observed at 612 (“Yes” at 612), the visual data captured by visual sensors may be used to confirm whether the targeting is successful at 614 (e.g., if the selected disruption effect is observed). The range of frequency sweep may be adjusted if the selected disruption effect is not visually confirmed. If the selected disruption effect is observed, the system proceeds to 616, targeting the UAS until the UAS is disabled, the desired disruption effect is achieved, or the targeting is terminated.

[0140] FIG. 7 shows an example of a frequency response 700 obtained from active listening. The large spur on the spectrum is the frequency of the wave being broadcasted (secondary spur). The smaller spur on the left side of the graph is the frequency re-emitted by the device in the UAS which is being affected (primary spur). In this case, the electromechanical device onboard the UAS is the camera. By maximizing the amplitude of the secondary spur, which can be set as a function of theamplitude and frequency of the primary spur, it is possible to maximize the disruption effect on the UAS.

[0141] As described above, the systems and methods disclosed herein can effectively target and disrupt electromechanical systems using acoustic energy. Various applications that may utilize the devices, systems, and methods disclosed herein are possible by means of the present disclosure.

[0142] As an example, the system can be implemented as a human portable unit (e.g., integrated with a weapon / rifle). In particular, the system can have a compact design with an integrated lithium power bank and lightweight directional array. The Human Portable Unit can be appliable for single-operator field use.

[0143] As another example, the system can be implemented as a vehiclemounted system. The system can be integrated into vehicular power infrastructure (e.g., police cruiser 24V-DC bus). Example embodiments can includes a larger transducer array and enhanced power module. The system can be activated using a switch inside the vehicle. A command-and-control dashboard can display information for the user inside the vehicle (e.g., identification of enemy targets, position of the beam, engagements, and power). The system itself can incorporate beam-steering, which can be performed automatically using algorithmic electronic control of the array elements.

[0144] As a further exam pie, the system can be implemented as a fixed ground installation. The system can have a stationary configuration connected to mains power. Embodiments can be designed for perimeter defense and extended operation. Similar to the vehicle-mounted system, these types of systems can comprise units networked together where information is displayed on a central command-and-control server, which can command any number of units simultaneously.

[0145] It would be appreciated by one of ordinary skill in the art that the system and components shown in the figures may include components not shown in the drawings. For simplicity and clarity of the illustration, elements in the figures are not necessarily to scale and are only schematic. It will be apparent to persons skilled inthe art that a number of variations and modifications can be made without departing from the scope of the invention as described herein.

[0146] It is contemplated that any part of any aspect or embodiment discussed in this specification can be implemented or combined with any part of any other aspect or embodiment discussed in this specification.

[0147] It should be recognized that features and aspects of the various examples provided above can be combined into further examples that also fall within the scope of the present disclosure.

[0148] When used in this specification and claims, the terms "comprises" and "comprising" and variations thereof mean that the specified features, steps, or components are included. The terms are not to be interpreted to exclude the presence of other features, steps, or components.

[0149] The invention may also broadly consist in the parts, elements, steps, examples and / or features referred to or indicated in the specification individually or collectively in any and all combinations of two or more said parts, elements, steps, examples, and / or features. In particular, one or more features in any of the embodiments described herein may be combined with one or more features from any other embodiment(s) described herein.

Claims

CLAIMS:

1. A system for disrupting electromechanical systems using acoustic energy, comprising:a waveform synthesis module configured to generate signals in an acoustic frequency band including a target resonant mode for disrupting a target electromechanical system; andone or more ultrasonic transducers configured to receive the signals and generate acoustic waves.

2. The system of claim 1 , further comprising:an amplification module configured to receive and amplify the signals to generate amplified waveforms;wherein the one or more ultrasonic transducers are configured to receive the amplified waveforms from the amplification module for generating the acoustic waves.

3. The system of claim 2, wherein the amplifier module comprises one or more transistor amplifiers.

4. The system of any one of claims 1 to 3, wherein the waveform synthesis module comprises one or more processing devices to generate the signals.

5. The system of any one of claims 1 to 4, wherein the one or more processing devices comprise one or more of: field programmable gate arrays, microcontrollers, application-specific integrated circuits, and / or digital signal processors.

6. The system of any one of claims 1 to 5, wherein the signals are generated in a time-domain.

7. The system of any one of claims 4 to 6, wherein the one or more processing devices generate respective signals at a target phase, a target frequency, and / or a target amplitude.

8. The system of claim 7, wherein the one or more processing devices are communicatively coupled to one or more controllers that communicate the target phase, the target frequency, and / or the target amplitude to the one or more processing devices.

9. The system of claim 8, wherein a subset of the one or more processing devices are coupled to a respective controller.

10. The system of claim 8 or 9, wherein the one or more controllers are communicatively coupled to a computing unit that provides targeting parameters of the target electromechanical system for the one or more controllers to calculate the target phase, the target frequency, and the target amplitude.

11. The system of claim 10, wherein the targeting parameters of the target electromechanical system comprise a type of the target electromechanical system, a distance of the target electromechanical system from the ultrasonic transducers, and / or a resonant frequency of the target electromechanical system.

12. The system of claim 10 or claim 11 , further comprising the computing unit.

13. The system of claim 12, further comprising detection equipment communicatively coupled to the computing unit and configured to detect the target electromechanical system, wherein the computing unit is configured to classify the target electromechanical system and determine the targeting parameters.

14. The system of claim 13, wherein the detection equipment comprises distance measurement equipment for measuring the distance of the target electromechanical system from the device.

15. The system of claim 14, wherein the computing unit is configured to execute a distance estimation Artificial Intelligence model trained to process data from the distance measurement equipment and estimate the distance of the target electromechanical system from the device.

16. The system of claim 14 or 15, wherein the distance measurement equipment comprises one or more of a camera, a LiDAR sensor, and / or a radar sensor.

17. The system of claim 16, wherein the computing unit is configured to execute an electromechanical system identification Artificial Intelligence model trained to process images from the camera and determine the type of the target electromechanical system.

18. The system of claim 17, wherein the computing unit is configured to access a database storing known electromechanical systems and associated resonant frequencies to determine the resonant frequency of the target electromechanical system.

19. The system of any one of claims 12 to 18, wherein a subset of the one or more ultrasonic transducers are configured to detect acoustic signals from the target electromechanical system in an active listening mode, and wherein the computing unit is configured to execute an acoustic listening Artificial Intelligence model trained to determine the resonant frequency of the target electromechanical system and / or the distance of the target electromechanical system from the device from the detected acoustic signals.

20. The system of any one of claims 10 to 19, wherein the computing unit is communicatively coupled to a central command server configured to provide auxiliary data to the computing for classifying the target electromechanical system.

21. The system of any one of claims 1 to 20, wherein the generated acoustic waves are beamformed.

22. The system of any one of claims 1 to 21, wherein the generated acoustic waves comprise a single-frequency tone, a frequency sweep, a burst signal, and / or a random signal pattern.

23. The system of any one of claims 1 to 22, wherein the ultrasonic transducers comprise piezoelectric transducers.

24. The system of any one of claims 1 to 23, wherein the ultrasonic transducers comprise a hexagonal arrangement of transducers.

25. The system of any one of claims 1 to 24, wherein the ultrasonic transducers comprise a phased array.

26. The system of any one of claims 1 to 25, wherein the ultrasonic transducers are arranged on a planar or curved surface.

27. The system of any one of claims 1 to 26, wherein the acoustic waves are generated in an ultrasonic frequency range or near-ultrasonic frequency range.

28. The system of any one of claims 1 to 27, further comprising a power module configured to provide power to the device.

29. The system of any one of claims 1 to 28, wherein the target electromechanical system is onboard an unmanned aerial system.

30. A method of disrupting electromechanical systems using acoustic energy, comprising:classifying a target electromechanical system to determine targeting parameters for targeting the electromechanical system; generating acoustic waves in an acoustic frequency band including a target resonant mode for disrupting the target electromechanical system based on the targeting parameters; andemitting the acoustic waves toward the target electromechanical system.

31. The method of claim 30, wherein classifying the target electromechanical system comprises determining a distance to the electromechanical system.

32. The method of claim 30 or claim 31, wherein classifying the target electromechanical system comprises determining a type of the electromechanical system.

33. The method of claim 32, further comprising:searching a database storing a plurality of targeting parameters corresponding to a plurality of types of electromechanical systems; and determining the targeting parameters for the type of the electromechanical system.

34. The method of any one of claims 30 to 32, wherein classifying the target electromechanical system comprises:performing a frequency sweep by generating a plurality of acoustic waves in a range of frequencies;capturing audio data corresponding to feedback to the frequency sweep from the electromechanical system; andanalyzing the audio data to determine one or more frequency peaks for the electromechanical system.

35. The method of any one of claims 30 to 34, wherein generating the acoustic waves comprises determining a target phase, a target frequency, and / or a target amplitude based on the targeting parameters, and driving one or more ultrasonic transducers to generate the acoustic waves based on the target phase, the target frequency, and / or the target amplitude.

36. The method of claim 35, wherein the generating of the acoustic waves comprises:generating, with a waveform synthesis module, signals in the acoustic frequency band corresponding to the targeting parameters; andgenerating, with the one or more ultrasonic transducers, acoustic waves from the signals.

37. The method of claim 36, wherein the generating of the acoustic waves further comprises:amplifying, with an amplification module, the signals to generate amplified waveforms;wherein the acoustic waves are generated from the amplified waveforms.

38. The method of any one of claims 30 to 37, wherein the electromechanical system is onboard an unmanned aerial system.

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