Unmanned aerial vehicle countering method and system based on sound wave nonlinear MEMS resonance

Abstract

The invention discloses an unmanned aerial vehicle countering method and system based on sound wave nonlinear MEMS resonance, and belongs to the technical field of unmanned aerial vehicle countering. Comprising the following steps: performing frequency sweep test on the resonant frequency fa of the MEMS device of the unmanned aerial vehicle; calculating a 1 / k frequency value of the resonance frequency, carrying out a nonlinear resonance test on the unmanned aerial vehicle, determining a k value enabling the output response of an MEMS device to be maximum according to frequency response data of the MEMS device in the unmanned aerial vehicle, recording the k value as k0, and taking the corresponding sound wave frequency # imgabs0 # # imgabs1 # at the moment as the optimal nonlinear resonance excitation frequency; and countering the unmanned aerial vehicle based on the optimal nonlinear resonance excitation frequency. According to the invention, nonlinear resonance of the MEMS sensor is excited by using 1 / k frequency multiplication sound waves, so that the required emission frequency is effectively reduced, the problem that the operating distance is limited due to serious high-frequency attenuation in the traditional ultrasonic countering technology is avoided, the countering range is remarkably expanded, and the method is more suitable for countering the long-distance black-flight unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention belongs to the technical field of drone countermeasures, and in particular relates to a drone countermeasure method and system based on acoustic wave nonlinear MEMS resonance. Background Art

[0002] In recent years, the phenomenon of illegal drone flights has become increasingly common, posing a serious threat to public safety. Unauthorized drones entering airport clear zones, near critical infrastructure, or in densely populated areas can cause serious accidents such as aircraft collisions, communication interference, and privacy breaches. Consequently, non-contact countermeasures based on the principle of acoustic resonance have attracted attention due to their controllability and safety, but their practical application still faces technical bottlenecks.

[0003] Existing acoustic countermeasures primarily rely on experimentally determining the acoustic resonance frequencies of key drone components (such as MEMS sensors), typically focusing on ultrasonic frequencies above 20 kHz. However, ultrasonic waves have significant drawbacks: First, high-frequency sound waves are severely attenuated in air, with an effective range typically less than 50 meters. Attenuation becomes increasingly severe beyond 15 meters, making them ineffective against long-range targets. Second, ultrasonic waves are highly directional and susceptible to obstruction by obstacles or airflow disturbances. Third, the equipment requires high-precision frequency tracking, which results in high hardware costs. Theoretically, if the countermeasure frequency could be lowered to the audible range (below 20 kHz), leveraging the longer propagation distance and greater diffraction properties of low-frequency sound waves, the countermeasure coverage could be significantly increased. However, experiments have shown that simply lowering the frequency significantly weakens the resonance effect, or even completely eliminates it. This discrepancy stems from the complex low-frequency vibration modes of key drone components, resulting in low energy transfer efficiency, and the fact that low-frequency sound waves easily overlap with ambient noise (e.g., traffic and industrial equipment). Therefore, how to find an effective frequency close to the resonance frequency below the ultrasonic frequency band so that it can be used to resist drones in the far field has become the key to breaking through the existing technological bottleneck.

[0004] Current methods for obtaining resonance frequencies based on laboratory conditions suffer from a disconnect with real-world scenarios. In experiments, resonance frequencies are typically measured using precision instruments within a 10cm distance. However, in real-world countermeasure scenarios, the distance between drones and equipment often exceeds 15 meters. At this point, acoustic wave propagation loss, changes in ambient temperature and humidity, and multipath effects can significantly alter the frequency response characteristics, rendering laboratory data invalid. Furthermore, directly scanning the audible frequency band (e.g., 1kHz–15kHz) requires a long scanning process, which not only fails to match the resonance frequency but can also damage the human auditory system.

[0005] Currently, determining the resonance frequency of acoustic waves relies heavily on ultrasonic frequency sweep experiments conducted under laboratory conditions. In a controlled environment, researchers have used precision equipment to sweep frequencies above 20 kHz, accurately identifying the resonance frequencies of key components such as drone MEMS devices. Given the need to find effective frequencies close to the resonance frequency below the ultrasonic frequency band, directly sweeping frequencies below the ultrasonic frequency band is impractical. First, the physical conditions in the laboratory differ significantly from those in the real world: laboratory test results at a distance of 10 cm cannot reflect the sound wave propagation characteristics at distances exceeding 15 meters, rendering the resonance frequency determined in the laboratory invalid in the far field. Second, sweeping frequencies in the low-frequency band faces technical bottlenecks: Directly attempting to sweep frequencies in the low-frequency audible sound band in a real-world setting would not only require extremely high sound pressure levels to overcome ambient noise interference, but would also result in significant energy diffusion due to the long wavelength. Furthermore, long-term exposure to high-intensity, low-frequency sound waves can cause auditory fatigue or psychological discomfort. Summary of the Invention

[0006] In view of the dual dilemma of existing acoustic wave countermeasure technology, which is severe high-frequency attenuation and poor low-frequency effect, the present invention proposes a drone countermeasure method and system based on acoustic wave nonlinear MEMS resonance.

[0007] The technical solution adopted in the present invention is as follows:

[0008] In a first aspect, the present invention provides a method for countering drones based on acoustic nonlinear MEMS resonance, characterized in that it includes the following steps:

[0009] (1) Frequency sweep test of the resonance frequency f of the UAV MEMS device a ;

[0010] (2) Calculate the 1 / k multiple frequency value of the resonant frequency and perform nonlinear resonance test on the drone. According to the frequency response data of the MEMS device in the drone, determine the k value that makes the MEMS device output the maximum response, which is recorded as k0. The corresponding sound wave frequency at this time is That is the optimal nonlinear resonance excitation frequency;

[0011] (3) Countering UAVs based on the optimal nonlinear resonance excitation frequency.

[0012] Furthermore, a database of different types of drones is established to record the resonance frequency f of each type of drone. a and its optimal nonlinear resonance excitation frequency.

[0013] Furthermore, in step (3), the target UAV model is determined based on image recognition technology, and the optimal nonlinear resonance excitation frequency is obtained based on a table lookup method.

[0014] Furthermore, in step (1), the sweep frequency test range is 20kHz to 40kHz, and the distance between the drone MEMS device and the acoustic wave signal generating device is 5-20cm.

[0015] Furthermore, in step (1), the resonant frequency f a It refers to the sound wave frequency corresponding to the maximum output response amplitude of the drone MEMS device.

[0016] Furthermore, step (2) includes:

[0017] (2.1) Based on the measured resonant frequency f a , calculate its 1 / k multiplication frequency value, where k = 2 to 5, that is, f k =

[0018] f a / k;

[0019] (2.2) Use f in sequence k Inject acoustic wave signals of different frequencies into the MEMS device, measure its frequency response, and record the output signal amplitude at each frequency;

[0020] (2.3) Analyze the frequency response data and determine the k value that maximizes the output response of the MEMS device, denoted as k0. The corresponding acoustic wave frequency at this time This is the optimal nonlinear resonance excitation frequency.

[0021] Furthermore, in step (3), a sound wave transmitting device is used to counter the UAV based on the optimal nonlinear resonance excitation frequency, and the sound wave transmitting device includes:

[0022] Power supply module, used to provide stable power for the entire acoustic wave transmitting device;

[0023] A signal generating module, used for generating a frequency-adjustable sound wave signal;

[0024] The audio amplifier module is used to amplify the sound wave signal output by the signal generating module to increase the intensity of the sound wave;

[0025] The speaker array module is used to convert the electrical signal output by the audio amplifier module into high-intensity sound waves and transmit them directionally toward the target drone.

[0026] Furthermore, the UAV to be countered is more than 10m away from the sonic wave emitting device.

[0027] In a second aspect, the present invention proposes a drone countermeasure system based on acoustic nonlinear MEMS resonance, which is used to implement the above-mentioned drone countermeasure method based on acoustic nonlinear MEMS resonance.

[0028] The beneficial effects of the present invention are:

[0029] This invention overcomes the technical limitations of traditional linear acoustic frameworks through a two-stage implementation. In the first, high-frequency ultrasonic sweeping (20kHz–100kHz) is used in a laboratory environment to accurately determine the inherent resonant frequencies of key MEMS sensor components in the target drone. In the second, based on nonlinear acoustic theory, the subharmonic frequencies (e.g., 1 / kth harmonics) of these resonant frequencies are precisely calculated to identify the nonlinear resonant excitation frequencies with optimal energy transfer efficiency. This technique stimulates the nonlinear vibration modes of the target MEMS device, inducing phase disturbances and harmonic distortion in its output signal.

[0030] The present invention utilizes 1 / k-fold frequency sound waves to stimulate the nonlinear resonance of the MEMS sensor, effectively reducing the required transmission frequency and avoiding the problem of limited effective range caused by severe high-frequency attenuation of traditional ultrasonic countermeasure technology, thereby significantly improving the countermeasure range and being more suitable for long-range countermeasures against illegal drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of the UAV countermeasure method based on acoustic nonlinear MEMS resonance;

[0032] Figure 2 1. It is a structural diagram of a sound wave transmitting device;

[0033] Figure 3 This is a flow chart for testing the optimal nonlinear resonance excitation frequency of UAV MEMS devices;

[0034] Figure 4 This is the Z-axis output result of the gyroscope when testing the ultrasonic frequency resonance frequency of a certain type of drone in this embodiment;

[0035] Figure 5 yes Figure 4 A partial enlarged view of

[0036] Figure 6 is the Z-axis output result of the gyroscope before and after the gyroscope is manually moved in this embodiment;

[0037] Figure 7 This is the attenuation simulation result when the sound wave frequency is 8.575kHz;

[0038] Figure 8 This is the attenuation simulation result when the sound wave frequency is 25.725kHz. DETAILED DESCRIPTION

[0039] The present invention will be further described and illustrated below in conjunction with specific embodiments. The embodiments are merely illustrative of the present disclosure and do not limit its scope. The technical features of the various embodiments of the present invention may be combined accordingly, provided that there is no conflict between them.

[0040] In response to the threat to public safety posed by the irregular flight of low-flying, slow, small drones, this invention proposes a drone countermeasure method based on acoustic nonlinear MEMS resonance. This method induces nonlinear resonance of the target drone's MEMS device through acoustic wave excitation, causing its output signal to be severely distorted, thereby causing the flight control system to make incorrect decisions, ultimately causing the drone to lose control, make an emergency landing, or deviate from its route, effectively responding to malicious flight behavior of single or clustered drones.

[0041] like Figure 1 As shown, the implementation process of the present invention mainly includes the following steps:

[0042] S1, frequency sweep test of the resonance frequency f of the drone MEMS device a ;

[0043] S2, calculate the 1 / k multiple frequency value of the resonant frequency, and perform nonlinear resonance test on the drone. According to the frequency response data of the MEMS device in the drone, determine the k value that makes the MEMS device output the maximum response, recorded as k0, and the corresponding sound wave frequency at this time That is the optimal nonlinear resonance excitation frequency;

[0044] S3, countering drones based on optimal nonlinear resonance excitation frequency.

[0045] In a specific embodiment of the present invention, the resonance frequency f of the MEMS device of the UAV is tested by sweeping the frequency. a , an optional implementation process is as follows:

[0046] S1.1: Use a signal generator to generate an acoustic wave signal with a frequency range of 20 kHz to 40 kHz, and transmit the swept-frequency acoustic wave signal to the MEMS devices (such as gyroscopes and accelerometers) of the target drone through a speaker.

[0047] S1.2: Detect the output response of the drone's MEMS device, record its output signal amplitude at different frequencies, and determine the frequency corresponding to its maximum output amplitude as the resonant frequency f a , that is, the sound wave frequency corresponding to the maximum output response.

[0048] In a specific implementation of the present invention, an optional implementation process for determining the optimal nonlinear resonance excitation frequency is as follows: Figure 3 As shown, specifically:

[0049] S2.1, based on the measured resonant frequency f a , calculate its 1 / k multiplication frequency value, where k = 2 to 5, that is, f k =

[0050] f a / k;

[0051] S2.2, use f in turn k Inject acoustic wave signals of different frequencies into the MEMS device, measure its frequency response, and record the output signal amplitude at each frequency;

[0052] S2.3, analyze the frequency response data and determine the k value that maximizes the output response of the MEMS device, denoted as k0. The corresponding sound wave frequency at this time This is the optimal nonlinear resonance excitation frequency.

[0053] In a specific implementation of the present invention, a sound wave transmitting device is used to counter the drone based on the optimal nonlinear resonance excitation frequency, such as Figure 2 As shown, the sound wave emitting device includes:

[0054] Power supply module, used to provide stable power for the entire acoustic wave transmitting device;

[0055] A signal generating module, used for generating a frequency-adjustable sound wave signal;

[0056] The audio amplifier module is used to amplify the sound wave signal output by the signal generating module to increase the intensity of the sound wave;

[0057] The speaker array module is used to convert the electrical signal output by the audio amplifier module into high-intensity sound waves and transmit them directionally toward the target drone.

[0058] Those skilled in the art can record the resonance frequency f of each type of drone by building a database of different types of drones. a When countering a target drone, the target drone model is determined using image recognition technology, and the optimal nonlinear resonance excitation frequency is obtained using a table lookup method. For drone models not found in the table, the preset typical MEMS resonance frequency (e.g., above 20kHz) is used directly, and the drone database is expanded in a timely manner.

[0059] Aim the acoustic wave transmitter at the drone to be countered, ensuring that the sound waves effectively cover its MEMS sensor area. Adjust the signal generation module to emit the optimal nonlinear resonance excitation frequency for the target drone model. Activate the audio amplifier module to increase the intensity of the sound waves. Directed transmission of the sound waves toward the target drone is performed through the speaker array module, causing the MEMS device to enter a nonlinear resonance state, resulting in severe distortion of its output signal. This, in turn, interferes with the drone's stable control, achieving a countermeasure effect.

[0060] The principle of countering drones based on the optimal nonlinear resonance excitation frequency is:

[0061] Acoustic signals are a type of mechanical wave that propagate through the vibration of a medium (such as air, water, or solids) and can be described by a wave equation. When the frequency of the external acoustic signal is the same as the natural frequency of the system, the object will resonate, a phenomenon called the acoustic resonance effect. The acoustic resonance vulnerability of MEMS devices refers to the fact that sensors and actuators with MEMS structures are susceptible to mechanical resonance interference caused by acoustic waves due to their micromechanical structure, which in turn causes abnormal perception or execution. MEMS devices usually contain tiny mechanical structures such as vibrating beams or pendulums, which are very sensitive to the vibration of acoustic waves. When the frequency of the acoustic wave is close to the natural frequency of these structures, it will cause excessive vibration of the mechanical structure inside the MEMS device, thereby affecting the measurement results of the sensor or the execution results of the actuator.

[0062] When the acoustic wave resonance effect occurs, the MEMS device is introduced into an unexpected response. When the unexpected acoustic signal is strong enough, the resonance of the MEMS device will become nonlinear, and the signal it receives will be:

[0063]

[0064] That is, the received signal contains a signal with a fundamental frequency of f and a harmonic signal with a frequency of kf. Therefore, when the optimal nonlinear resonance excitation frequency is transmitted to the drone, it is used as the fundamental frequency. Based on the nonlinear acoustic theory, the nonlinear resonance of the MEMS sensor is excited, thereby generating a frequency of The harmonic signal can theoretically achieve an effect close to the resonant frequency.

[0065] In order to verify the effect of the present invention, a certain type of drone was tested. Its MEMS device was a gyroscope. The sweep frequency test showed that the resonance frequency of the ultrasonic frequency band was 25.725kHz. Figure 4 The following figure shows the Z-axis output of the gyroscope. The details after magnification are as follows: Figure 5 As shown, the horizontal axis is the time axis. The output comparison results before and after manually turning the gyroscope are shown in Figure 6 As shown in the figure, it can be seen that the gyroscope's output signal fluctuation at 25.725kHz is not affected by human touch, but by the resonance of the sound wave. The 1 / k multiplication frequency value is further calculated and the nonlinear resonance test of the drone is performed to obtain the optimal nonlinear resonance excitation frequency. It is 8.575kHz.

[0066] Under different media and conditions, the relationship between the sound wave attenuation coefficient α and the frequency f is α∝f nThe exponent n in the equation will vary. In air, sound attenuation is mainly determined by classical absorption (viscosity and heat conduction) and molecular relaxation absorption (excitation of vibrational energy levels of oxygen and nitrogen). Using simulation software, it can be seen that at 50% humidity, 20 degrees Celsius, and 1 standard atmospheric pressure, when the sound wave frequency is 8.575kHz, its attenuation is 11.9dB / 100m. Figure 7 When the sound wave frequency is 25.725kHz, its attenuation is 76.2dB / 100m, as shown in Figure 8 shown.

[0067] From the above test results, we can see that under the same transmission power, 8.575kHz can cause the gyroscope output amplitude to be about 1, and 25.725kHz can cause the gyroscope output amplitude to be about 3. Therefore, the energy of the 8.575kHz sound wave needs to be increased by 9 times to make its output amplitude equivalent to that of 25.725kHz.

[0068] It can be calculated that when the propagation distance is greater than 14.84m, the attenuation of 25.725kHz sound waves will be 9 times greater than that of 8.575kHz. Therefore, in the scenario of long-distance countermeasures, the use of 8.575kHz sound waves can achieve a more effective countermeasure effect.

[0069] Based on the same inventive concept, this embodiment also provides a drone countermeasure system based on acoustic wave nonlinear MEMS resonance, the system comprising:

[0070] Resonance frequency test module, which is used to sweep the resonance frequency f of the drone MEMS device a ;

[0071] The optimal nonlinear resonance excitation frequency test module is used to calculate the 1 / k multiple frequency value of the resonance frequency and perform nonlinear resonance testing on the drone. According to the frequency response data of the MEMS device in the drone, the k value that maximizes the output response of the MEMS device is determined, which is recorded as k0. The corresponding sound wave frequency at this time is That is the optimal nonlinear resonance excitation frequency; the preferred value of k is 2 to 5;

[0072] The acoustic wave emission module is used to counter drones based on the optimal nonlinear resonance excitation frequency.

[0073] As for the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment, and the implementation methods of the remaining modules will not be repeated here. The system embodiment described above is only illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present invention. Ordinary technicians in this field can understand and implement it without paying any creative work.

[0074] Embodiments of the system of the present invention can be applied to any device with data processing capabilities, such as a computer or other device. System embodiments can be implemented through software, hardware, or a combination of software and hardware. For example, a software implementation, as a logical device, is implemented by a processor of any device with data processing capabilities, reading corresponding computer program instructions from non-volatile memory into internal memory and executing them.

[0075] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they are not to be construed as limiting the scope of the present invention. Persons skilled in the art will readily appreciate that variations and modifications may be made without departing from the spirit of the present invention, and all such variations and modifications fall within the scope of protection of the present invention.

Claims

1. A drone countermeasure method based on acoustic nonlinear MEMS resonance, characterized in that: The following steps are involved: (1) Frequency sweep test of the resonance frequency f of the UAV MEMS device a ; (2) Calculate the 1 / k multiple frequency value of the resonant frequency and perform nonlinear resonance test on the drone. According to the frequency response data of the MEMS device in the drone, determine the k value that maximizes the output response of the MEMS device, which is recorded as k0. The corresponding sound wave frequency f k0 =f a / k0 is the optimal nonlinear resonance excitation frequency; (3) Countering UAVs based on the optimal nonlinear resonance excitation frequency.

2. The drone countermeasure method based on acoustic nonlinear MEMS resonance according to claim 1 is characterized in that: Establish a database of different types of drones and record the resonance frequency f of each type of drone a and its optimal nonlinear resonance excitation frequency.

3. The drone countermeasure method based on acoustic nonlinear MEMS resonance according to claim 2 is characterized in that: In step (3), the target UAV model is determined based on image recognition technology, and the optimal nonlinear resonance excitation frequency is obtained based on a lookup table method.

4. The drone countermeasure method based on acoustic nonlinear MEMS resonance according to claim 1 is characterized in that: In step (1), the sweep frequency test range is 20kHz to 40kHz, and the distance between the drone MEMS device and the acoustic wave signal generating device is 5-20cm.

5. The drone countermeasure method based on acoustic nonlinear MEMS resonance according to claim 1 is characterized in that: In step (1), the resonant frequency f a It refers to the sound wave frequency corresponding to the maximum output response amplitude of the drone MEMS device.

6. The drone countermeasure method based on acoustic nonlinear MEMS resonance according to claim 1 is characterized in that: Step (2) includes: (2.1) Based on the measured resonant frequency f a , calculate its 1 / k multiplication frequency value, where k = 2 to 5, that is, f k = f a / k; (2.2) Use f in sequence k Inject acoustic wave signals of different frequencies into the MEMS device, measure its frequency response, and record the output signal amplitude at each frequency; (2.3) Analyze the frequency response data and determine the k value that maximizes the output response of the MEMS device, denoted as k0. The corresponding acoustic wave frequency f k0 =f a / k0 is the optimal nonlinear resonance excitation frequency.

7. The drone countermeasure method based on acoustic nonlinear MEMS resonance according to claim 1 is characterized in that: In step (3), a sound wave transmitting device is used to counter the UAV based on the optimal nonlinear resonance excitation frequency, and the sound wave transmitting device includes: Power supply module, used to provide stable power for the entire acoustic wave transmitting device; A signal generating module, used for generating a frequency-adjustable sound wave signal; The audio amplifier module is used to amplify the sound wave signal output by the signal generating module to increase the intensity of the sound wave; The speaker array module is used to convert the electrical signal output by the audio amplifier module into high-intensity sound waves and transmit them directionally toward the target drone.

8. The drone countermeasure method based on acoustic nonlinear MEMS resonance according to claim 1 is characterized in that: The UAV to be countered is more than 10m away from the sonic wave transmitter.

9. A drone countermeasure system based on acoustic nonlinear MEMS resonance, used to implement the drone countermeasure method according to claim 1, characterized in that: The system comprises: Resonance frequency test module, which is used to sweep the resonance frequency f of the drone MEMS device a ; The optimal nonlinear resonance excitation frequency test module is used to calculate the 1 / k multiple frequency value of the resonance frequency and perform nonlinear resonance testing on the drone. According to the frequency response data of the MEMS device in the drone, the k value that maximizes the output response of the MEMS device is determined, which is recorded as k0. The corresponding sound wave frequency f k0 =f a / k0 is the optimal nonlinear resonance excitation frequency; The acoustic wave emission module is used to counter drones based on the optimal nonlinear resonance excitation frequency.

10. The UAV countermeasure system based on acoustic nonlinear MEMS resonance according to claim 9, characterized in that: The value of k ranges from 2 to 5.

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