Unmanned aerial vehicle detection method and system based on microphone cross array

Abstract

The embodiment of the invention provides an unmanned aerial vehicle detection method and system based on a microphone cross array, and the method comprises the steps: setting a microphone array which is a cross array, and carrying out the periodic scanning of the cross array; the method comprises the following steps: receiving an original sound source signal of an unmanned aerial vehicle, performing signal filtering on the original sound source signal, performing FFT conversion on a time domain filtering signal, and outputting a frequency domain signal; calculating a covariance matrix and a guide matrix by taking the frequency domain signal as input and combining a position vector of a microphone, generating corresponding adaptive weights, and further calculating beam output and a power spectrum in a scanning direction; trajectory fusion is carried out based on the time sequence of the power spectrum, the trajectory of the unmanned aerial vehicle is determined, real-time coordinates of the unmanned aerial vehicle are calculated based on the azimuth angle of the cross microphone array, and trajectory prediction is carried out on the unmanned aerial vehicle in combination with trajectory information.

Description

Technical Field

[0001] This invention relates to the field of drone positioning technology, and in particular to a drone detection method and system based on a microphone cross array. Background Technology

[0002] Traditional acoustic drones generate unique, broadband noise with harmonic characteristics by cutting through the air with their rotors (propellers) during flight. After receiving these noise signals, the acoustic detection system uses a microphone array to scan a two-dimensional area in the air and uses beamforming algorithms to focus and locate the drone's position.

[0003] However, traditional microphone arrays may have the following problems when detecting: low-frequency noise interference in the environment, especially at longer detection distances, where the interference is particularly severe; relatively short detection distance, generally around 300m, which can only detect low-flying drones; slow response speed; and small scanning area, as traditional beamforming can only scan a certain angular range, generally between 120° and 150°. The larger the range, the more severe the interference, and the sidelobes have a greater impact on performance. Summary of the Invention

[0004] To address the problems existing in the prior art, embodiments of the present invention provide a method and system for detecting unmanned aerial vehicles (UAVs) based on a microphone cross array.

[0005] This invention provides a method for detecting unmanned aerial vehicles (UAVs) based on a microphone cross array, the method comprising:

[0006] A microphone array is set up, which is a cross array, and the cross array is scanned periodically;

[0007] Receive the raw sound source signal from the UAV, perform signal filtering on the raw sound source signal, perform FFT transformation on the time-domain filtered signal, and output the frequency-domain signal;

[0008] Using the frequency domain signal as input, the covariance matrix and steering matrix are calculated in conjunction with the microphone's position vector, and corresponding adaptive weights are generated to calculate the beam output and power spectrum in the scanning direction.

[0009] Trajectory fusion is performed based on the time series of the power spectrum to determine the trajectory of the UAV, and the real-time coordinates of the UAV are calculated based on the azimuth angle of the cross microphone array. Combined with the trajectory, the trajectory of the UAV is predicted.

[0010] In one embodiment, the method further includes:

[0011] A channel complex matrix is ​​constructed using the frequency domain signal of the microphone array, and the average covariance of the microphone array is calculated by combining the time-series data of the channel complex matrix.

[0012] Based on the scanning angle of the two arms in the cross array, the direction angle unit vector is calculated, and then the relative time delay in the cross array is determined, thereby constructing the corresponding guide vector. The guide vector simulates the ideal phase relationship of the sound source arriving at each microphone in the cross array from different directions.

[0013] Determine the regularized covariance matrix corresponding to the covariance matrix, and generate the corresponding adaptive weights by combining the regularized covariance matrix and the guidance matrix.

[0014] In one embodiment, the formula for calculating the adaptive weights includes:

[0015]

[0016] Where w is the adaptive weight, The regularized covariance matrix, H is the guiding vector, and H is the conjugate transpose.

[0017] In one embodiment, the method further includes:

[0018] Using adaptive weights and the complex matrix of the channels of the current frame, the beam output of the current frame in the scanning direction is calculated, wherein the beam output includes the beam output corresponding to each microphone frequency point;

[0019] The sound pressure energy received by the cross array is calculated based on the beam output, thereby determining the power spectrum, which includes the power spectrum in the east-west scanning direction and the power spectrum in the north-south scanning direction.

[0020] In one embodiment, the beam output corresponding to each microphone frequency point includes:

[0021]

[0022] in, Indicates the direction of θ with respect to frequency f k The beamforming result, where m is the microphone number, w m X is the adaptive weight for the m-th microphone. m Let be the complex spectral value of the m-th microphone, where * denotes complex conjugate;

[0023] The formula for calculating the power spectrum includes:

[0024]

[0025] in, The power spectrum in the θ direction is represented by k, where k is the frequency index and f is the frequency index. k Let be the center frequency of the k-th frequency point.

[0026] In one embodiment, the method further includes:

[0027] Calculate the power spectrum after incorporating the adaptive weights, locate the peak value of the weighted power spectrum, and determine the target direction of the UAV for each frame based on the peak value;

[0028] The peak values ​​are then correlated in a cross array along two axes to determine effective angle pairs, which include east-west angles and north-south angles.

[0029] Kalman filtering is applied to the power spectrum of each frame, and the smoothed angle-to-track points are output.

[0030] In one embodiment, the method further includes:

[0031] In the cross array, the microphone spacing is dynamically adjusted based on the distance of the microphone from the center point of the cross array;

[0032] Alternatively, the sensitivity of the edge microphones can be dynamically adjusted based on the distance of the microphone from the center point of the cross array.

[0033] This invention provides a drone detection system based on a microphone cross array, the system comprising:

[0034] The setting module is used to set the microphone array, which is a cross array and performs periodic scanning.

[0035] The receiving module is used to receive the raw sound source signal from the UAV, filter the raw sound source signal, perform FFT transformation on the time-domain filtered signal, and output the frequency-domain signal.

[0036] The weighting module is used to calculate the covariance matrix and steering matrix by taking the frequency domain signal as input and combining it with the microphone's position vector, and to generate corresponding adaptive weights, thereby calculating the beam output and power spectrum in the scanning direction.

[0037] The trajectory module is used to perform trajectory fusion based on the time series of the power spectrum to determine the trajectory of the UAV, calculate the real-time coordinates of the UAV based on the azimuth angle of the cross microphone array, and perform trajectory prediction of the UAV in combination with the trajectory.

[0038] This invention provides an electronic device, including a processor and a memory;

[0039] The processor is connected to the memory;

[0040] The memory is used to store executable program code;

[0041] The processor runs a program corresponding to the executable program code stored in the memory to perform the methods described in one or more embodiments.

[0042] This invention provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described UAV detection method based on a microphone cross array.

[0043] In view of the above, in one or more embodiments of this specification, a microphone array is set up, which is a cross array and performs periodic scanning; the original sound source signal of the UAV is received, the original sound source signal is filtered, the time-domain filtered signal is subjected to FFT transformation, and the frequency domain signal is output; the frequency domain signal is used as input, and the covariance matrix and steering matrix are calculated in combination with the position vector of the microphone, and corresponding adaptive weights are generated, and then the beam output and power spectrum in the scanning direction are calculated; trajectory fusion is performed based on the time series of the power spectrum to determine the trajectory of the UAV, and the real-time coordinates of the UAV are calculated based on the azimuth angle of the cross microphone array, and the trajectory information is combined to predict the trajectory of the UAV. This can improve the signal-to-noise ratio of the UAV sound source, improve the resistance to low-frequency interference, improve the maximum detection distance and positioning accuracy; the independent scanning of the two arms of the cross array increases the response speed; and the scanning range of the cross array also improves the sound source reception range. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of a drone detection method based on a microphone cross array, provided in one embodiment of this specification.

[0046] Figure 2 This is a schematic diagram of a drone detection system based on a microphone cross array, provided in one embodiment of this specification.

[0047] Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this specification. Detailed Implementation

[0048] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed merely to enable those skilled in the art to better understand and implement the subject matter described herein, and are not intended to limit the scope, applicability, or examples set forth in the claims. The function and arrangement of the elements discussed may be changed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the various examples. For example, the described methods may be performed in a different order than described, and steps may be added, omitted, or combined. Furthermore, features described in some examples may be combined in other examples.

[0049] As used herein, the term "comprising" and its variations are open terms meaning "including but not limited to". The term "based on" means "at least partially based on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other definitions, whether explicit or implicit, may be included below. Unless explicitly indicated by the context, the definition of a term shall remain consistent throughout the specification.

[0050] like Figure 1 As shown, this embodiment of the invention provides a drone detection method based on a microphone cross array, including:

[0051] Step S102: Set up a microphone array, which is a cross array, and perform periodic scanning.

[0052] Specifically, a two-dimensional microphone array is set up, arranged in a cross shape, which can be composed of two intersecting arms. The physical parameters of the microphone array can be set as follows: the length of each arm is at least 1.6m, with one arm running east-west and the other north-south. At least four microphones are deployed on each arm to collect more comprehensive UAV sound source data. A time synchronization module is set at the center point of the array to ensure that the sampling clocks of all channels are synchronized. Furthermore, microphone parameters, such as frequency response, dynamic range, noise floor, and sensitivity, can be dynamically adjusted according to the UAV's detection requirements.

[0053] Furthermore, when arranging the microphone array in a cross pattern, due to array geometric asymmetry, reduced reception efficiency caused by grazing incidence of sound waves, and wind noise, edge beam distortion and microphone attenuation may occur at the edges of the cross array. To address these issues, the density of the microphones at the edges can be set higher than in the center area; for example, the spacing between microphones near the center could be 0.5m, while the spacing between microphones near the edges could be 0.4m. Alternatively, the sensitivity of the edge microphones can be adjusted, for example, by increasing the microphone sensitivity by 3dB to compensate for a 6dB signal-to-noise ratio loss. Edge compensation can compensate for grazing incidence signal attenuation, thereby reducing low-frequency interference.

[0054] Step S104: Receive the original sound source signal from the UAV, perform signal filtering on the original sound source signal, perform FFT transformation on the time-domain filtered signal, and output the frequency-domain signal.

[0055] Specifically, when the microphone array receives the raw sound source signal from the drone, it performs bandpass filtering. The raw sound source signal can be the original signal from any of the microphone channels. The bandpass filtering data should consider the characteristic frequency of the drone's rotor, for example, set to 100-2000Hz, to suppress high-frequency interference above 2000Hz, including wind noise / circuit noise, and low-frequency interference below 100Hz, such as vehicle / mechanical vibration, thereby improving the signal-to-noise ratio of the sound source signal. After filtering the signal to obtain the time-domain filtered signal, an FFT transform is performed. Before the FFT transform, the time-domain filtered signal undergoes preprocessing, including frame segmentation, overlapping time windows, and windowing. Then, the FFT calculation is performed to convert the time-domain filtered signal into a complex spectrum, outputting a frequency-domain complex signal.

[0056] Step S106: Using the frequency domain signal as input, calculate the covariance matrix and steering matrix in conjunction with the microphone's position vector, and generate corresponding adaptive weights, thereby calculating the beam output and power spectrum in the scanning direction.

[0057] Specifically, the frequency domain complex signal is used as the input data, where the input data is taken as a 7-channel (the number of microphones in the microphone array) complex matrix as an example:

[0058]

[0059] Among them, the elements in the matrix This indicates that the m-th microphone is at frequency f k The complex spectrum value at that location.

[0060] Furthermore, the covariance matrix is ​​estimated. This can be done by, for example, taking the corresponding time-series data, such as data including the current frame and the previous 49 frames (a total of 50 frames), and calculating the average covariance:

[0061]

[0062] Where R represents the covariance, X i Let H represent the frequency domain data of the i-th frame, and H denote the conjugate transpose. Taking a 7-channel example, the output is a 7*7 complex matrix, with the diagonal lines representing the microphone power and the off-diagonal lines representing the cross-correlation values.

[0063] A 170° range can be scanned using a linear beamforming algorithm. The two arms of the array scan east-west and north-south directions respectively. The angle scan cycle can be from -85° to 85°, in 1° increments.

[0064] During the scanning loop, the current angle θ, frequency f, and sound speed c corresponding to the microphone are determined (sound speed is calibrated using real-time temperature), and then the unit vector of the direction angle is calculated. :

[0065]

[0066] Then, the relative time delay between each microphone in the microphone array is calculated:

[0067]

[0068] in, This represents the position vector of the m-th microphone.

[0069] Construct the guide vector:

[0070] This simulates the ideal phase relationship of sound waves arriving at each microphone in the microphone array from the θ direction.

[0071] Furthermore, using the covariance matrix R and the steering vector... Calculate the regularized covariance matrix:

[0072]

[0073] Then calculate the corresponding adaptive weights:

[0074]

[0075] In the adaptive weight calculation, the response in the interference direction is minimized while maintaining a gain of 1 in the θ direction. The introduction of weights can create a null of a certain depth in the interference direction, thereby improving the signal-to-noise ratio.

[0076] Furthermore, the beam output of the linear beam is calculated.

[0077]

[0078] in, This represents the beamforming result in the θ direction, where X is the frequency domain complex signal input to the microphone channel. Furthermore, in practical calculations, each microphone frequency can be calculated independently, i.e.:

[0079]

[0080] Among them, w m Let * represent the adaptive weights for the m-th microphone, * denote complex conjugate, and output a complex vector. .

[0081] Furthermore, based on the above characteristics, the azimuth-power spectrum of the microphone array is calculated, thereby determining the sound pressure energy received by the microphone array at each angle. The specific calculation formula is as follows:

[0082]

[0083] in, The power spectrum in the θ direction is represented by k, where k is the frequency index and f is the frequency index. k Let be the center frequency of the k-th frequency point.

[0084] Furthermore, by combining a cross microphone array, the power spectrum can be divided into an azimuth-power spectrum for east-west scanning. And the azimuth-power spectrum of the north-south scan. .

[0085] Step S108: Based on the time series of the power spectrum, perform trajectory fusion to determine the trajectory of the UAV, and based on the azimuth angle of the cross microphone array, determine the real-time coordinates of the UAV, and combine the trajectory to predict the trajectory of the UAV.

[0086] Specifically, after determining the power spectrum, the first step is to determine the UAV's trajectory data, including:

[0087] Determine the power spectrum data after combining the power spectrum with the time series, in the east-west direction. and north-south And, combined with the corresponding weight W, calculate the weighted power:

[0088]

[0089] It also locates the peak value of the weighted power spectrum, thereby extracting the most significant target direction in each frame and suppressing false edge detections.

[0090] After determining the independent peak value for each frame, the original east-west and north-south angle pairs detected based on the peak value within the frame are correlated in a cross array along two axes to filter out outlier points with abnormal directional angles. This ensures that the physical condition of the target's motion is reasonable and that the drone's flight angles are valid angle pairs. For example, when filtering outlier points, if the angle change is greater than 15 degrees / 0.1 seconds compared to the trajectory points in the previous frame, it is considered an outlier and the data is removed.

[0091] Furthermore, for inter-frame data smoothing, Kalman filtering can be applied to the effective angle pairs to output a stable sequence of trajectory points, thus producing smoothed angle trajectory points. Angle jitter in the trajectory point data can be suppressed through Kalman filtering. This associates the discrete angle points output by beamforming into a continuous trajectory, providing stable input for positioning.

[0092] Secondly, the relative position of the target (drone) can also be determined, including:

[0093] Target location is achieved by combining east-west and north-south trajectory maps. The target's slant range is determined using methods such as sound intensity ranging and dual-array estimation. Combining the target's slant range with the east-west and north-south angles allows for the determination of the three-axis coordinates. This transforms the two-angle trajectory of the crosshair array into three-dimensional coordinates, resolving the height ambiguity problem. Taking sound intensity ranging as an example:

[0094] First, calculate the sound pressure level:

[0095]

[0096] in, The clean signal after processing in step S106 is then determined through edge compensation:

[0097]

[0098] Where θ is the compensation direction angle. The data is for compensation, for example, 2.5dB.

[0099] The distance measurement formula is: This allows us to determine the target slant distance d, and thus improve positioning accuracy based on sound intensity.

[0100] Furthermore, trajectory prediction for drones can also be performed, including:

[0101] The three-dimensional velocity of the UAV is determined based on trajectory points, and the UAV's motion is modeled using three-dimensional coordinates to establish its dynamic model. In this dynamic model, the UAV's current state is used as input to predict its position in the next frame or subsequent time points, thereby determining its future coordinates.

[0102] This invention provides a method for detecting unmanned aerial vehicles (UAVs) based on a microphone cross array. The method involves setting up a microphone array, specifically a cross array, which performs periodic scanning. The method receives the raw sound source signal from the UAV, filters the original sound source signal, performs an FFT transformation on the time-domain filtered signal, and outputs a frequency-domain signal. Using the frequency-domain signal as input, the method calculates the covariance matrix and steering matrix based on the microphone position vector, generates corresponding adaptive weights, and then calculates the beam output and power spectrum along the scanning direction. Based on the time series of the power spectrum, the method performs trajectory fusion to determine the UAV's trajectory. Furthermore, based on the azimuth angle of the cross microphone array, the method calculates the UAV's real-time coordinates and, combined with the trajectory information, predicts the UAV's trajectory. This method improves the received signal-to-noise ratio of the UAV's sound source, enhances resistance to low-frequency interference, increases the maximum detection distance and positioning accuracy, increases the response speed due to the independent scanning of the dual arms of the cross array, and expands the sound source reception range due to the scanning range of the cross array.

[0103] Please see Figure 2 , Figure 2 This is a schematic diagram of a drone detection system based on a microphone cross array, provided in an embodiment of this application. Figure 2 As shown, the system includes:

[0104] Setting module S202 is used to set a microphone array, wherein the microphone array is a cross array and the cross array is periodically scanned;

[0105] The receiving module S204 is used to receive the original sound source signal from the UAV, perform signal filtering on the original sound source signal, perform FFT transformation on the time-domain filtered signal, and output the frequency-domain signal.

[0106] The weighting module S206 is used to take the frequency domain signal as input, combine it with the microphone position vector to calculate the covariance matrix and steering matrix, and generate corresponding adaptive weights, thereby calculating the beam output and power spectrum in the scanning direction.

[0107] The trajectory module S208 is used to perform trajectory fusion based on the time series of the power spectrum to determine the trajectory of the UAV, calculate the real-time coordinates of the UAV based on the azimuth angle of the cross microphone array, and perform trajectory prediction of the UAV in combination with the trajectory.

[0108] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0109] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0110] See Figure 3 It shows a schematic diagram of the structure of an electronic device according to an embodiment of this application, which can be used to implement... Figure 1 The method in the illustrated embodiment. (As shown) Figure 3 As shown, the electronic device 300 may include: at least one processor 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0111] The communication bus 302 is used to enable communication between these components.

[0112] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0113] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0114] The processor 301 may include one or more processing cores. The processor 301 connects to various parts within the electronic device 300 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0115] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0116] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call the image-based interactive application stored in the memory 305 and specifically perform the following operations: setting up a microphone array, the microphone array being a cross array, the cross array performing periodic scanning; receiving the original sound source signal from the UAV, filtering the original sound source signal, performing FFT transformation on the time-domain filtered signal, and outputting a frequency-domain signal; using the frequency-domain signal as input, calculating the covariance matrix and steering matrix in combination with the microphone position vector, and generating corresponding adaptive weights, thereby calculating the beam output and power spectrum in the scanning direction; performing trajectory fusion based on the time series of the power spectrum to determine the trajectory of the UAV, and calculating the real-time coordinates of the UAV based on the azimuth angle of the cross microphone array, and predicting the trajectory of the UAV in combination with the trajectory information.

[0117] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0118] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0119] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0121] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0123] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0124] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0125] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

Claims

1. A method for detecting unmanned aerial vehicles (UAVs) based on a microphone cross array, characterized in that, include: A microphone array is set up, which is a cross array, and the cross array is periodically scanned; Receive the raw sound source signal from the UAV, perform signal filtering on the raw sound source signal, perform FFT transformation on the time-domain filtered signal, and output the frequency-domain signal; Using the frequency domain signal as input, the covariance matrix and steering matrix are calculated in conjunction with the microphone's position vector, and corresponding adaptive weights are generated to calculate the beam output and power spectrum in the scanning direction. Trajectory fusion is performed based on the time series of the power spectrum to determine the trajectory of the UAV, and the real-time coordinates of the UAV are calculated based on the azimuth angle of the cross microphone array. Combined with the trajectory, the trajectory of the UAV is predicted.

2. The UAV detection method based on a microphone cross array according to claim 1, characterized in that, The process of calculating the covariance matrix and steering matrix by combining the microphone's position vector and generating corresponding adaptive weights includes: A channel complex matrix is ​​constructed using the frequency domain signal of the microphone array, and the average covariance of the microphone array is calculated by combining the time-series data of the channel complex matrix. Based on the scanning angle of the two arms in the cross array, the direction angle unit vector is calculated, and then the relative time delay in the cross array is determined, thereby constructing the corresponding guide vector. The guide vector simulates the ideal phase relationship of the sound source arriving at each microphone in the cross array from different directions. Determine the regularized covariance matrix corresponding to the covariance matrix, and generate the corresponding adaptive weights by combining the regularized covariance matrix and the guidance matrix.

3. The UAV detection method based on a microphone cross array according to claim 2, characterized in that, The formula for calculating the adaptive weights includes: ， where \(w\) is the adaptive weight, is the regularization covariance matrix, is the steering vector, and \(H\) is the conjugate transpose.

4. The UAV detection method based on a microphone cross array according to claim 2, characterized in that, The calculation of beam output and power spectrum in the scanning direction includes: Using adaptive weights and the complex matrix of the channels of the current frame, the beam output of the current frame in the scanning direction is calculated, wherein the beam output includes the beam output corresponding to each microphone frequency point; The sound pressure energy received by the cross array is calculated based on the beam output, thereby determining the power spectrum, which includes the power spectrum in the east-west scanning direction and the power spectrum in the north-south scanning direction.

5. The UAV detection method based on a microphone cross array according to claim 4, characterized in that, The beam output corresponding to each microphone frequency point includes: ， in, Indicates the direction of θ with respect to frequency f k The beamforming result, where m is the microphone number, w m X is the adaptive weight for the m-th microphone. m Let be the complex spectral value of the m-th microphone, where * denotes complex conjugate; The formula for calculating the power spectrum includes: ， in, The power spectrum in the θ direction is represented by k, where k is the frequency index and f is the frequency index. k Let be the center frequency of the k-th frequency point.

6. The UAV detection method based on a microphone cross array according to claim 4, characterized in that, The trajectory fusion based on the power spectrum time series to determine the trajectory of the UAV includes: Calculate the power spectrum after incorporating the adaptive weights, locate the peak value of the weighted power spectrum, and determine the target direction of the UAV for each frame based on the peak value; The peak values ​​are then correlated in a cross array along two axes to determine effective angle pairs, which include east-west angles and north-south angles. Kalman filtering is applied to the power spectrum of each frame, and the smoothed angle-to-track points are output.

7. The UAV detection method based on a microphone cross array according to claim 6, characterized in that, The method further includes: In the cross array, the microphone spacing is dynamically adjusted based on the distance of the microphone from the center point of the cross array; Alternatively, the sensitivity of the edge microphones can be dynamically adjusted based on the distance of the microphone from the center point of the cross array.

8. A drone detection system based on a microphone cross array, characterized in that, The system includes: The setting module is used to set the microphone array, which is a cross array and performs periodic scanning. The receiving module is used to receive the raw sound source signal from the UAV, filter the raw sound source signal, perform FFT transformation on the time-domain filtered signal, and output the frequency-domain signal. The weighting module is used to calculate the covariance matrix and steering matrix by taking the frequency domain signal as input and combining it with the microphone's position vector, and to generate corresponding adaptive weights, thereby calculating the beam output and power spectrum in the scanning direction. The trajectory module is used to perform trajectory fusion based on the time series of the power spectrum to determine the trajectory of the UAV, calculate the real-time coordinates of the UAV based on the azimuth angle of the cross microphone array, and perform trajectory prediction of the UAV in combination with the trajectory.

9. An electronic device, comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code stored in the memory to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of claims 1-7.

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