A method and system for detecting a drone based on a microphone cross array

Abstract

The embodiment of the application provides a kind of unmanned aerial vehicle detection method and system based on microphone cross array, the method comprises: setting microphone array, microphone array is cross array, cross array is periodically scanned;Receive the original sound source signal of unmanned aerial vehicle, signal filtering is carried out to original sound source signal, time domain filter signal is carried out FFT transformation, and frequency domain signal is output;With the frequency domain signal as input, covariance matrix and guide matrix are calculated in combination with the position vector of microphone, and corresponding adaptive weight is generated, and then the beam output in scanning direction and power spectrum are calculated;Trajectory fusion is carried out based on the time series of power spectrum, the trajectory of unmanned aerial vehicle is determined, and the real-time coordinate of unmanned aerial vehicle is calculated based on the azimuth angle of cross microphone array, and trajectory prediction is carried out to unmanned aerial vehicle in combination with trajectory information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle positioning, and in particular to an unmanned aerial vehicle detection method and system based on a microphone cross array. BACKGROUND

[0002] A conventional acoustic unmanned aerial vehicle, when flying, has its rotor (propeller) cutting air to generate unique, wideband, harmonic noise. An acoustic detection system receives these noise signals, scans a two-dimensional area using a microphone array, and focuses on the location of the unmanned aerial vehicle in the air through a beamforming algorithm.

[0003] However, the conventional microphone array may have the following problems during detection: low-frequency noise interference in the environment, especially when the detection distance is far, the interference is particularly serious; when the detection distance is close, generally about 300 m, only low-altitude flying unmanned aerial vehicles can be detected; the response speed is slow; the scanning area is small, and the conventional beamforming can only scan a certain angle range, generally between (120°~150°), the larger the range, the more serious the interference, and the side lobe has a greater impact on performance. SUMMARY

[0004] To solve the problems in the prior art, the present application provides an unmanned aerial vehicle detection method and system based on a microphone cross array.

[0005] The present application provides an unmanned aerial vehicle detection method based on a microphone cross array, which comprises:

[0006] A microphone array is provided, which is a cross array, and the cross array is periodically scanned.

[0007] The original sound source signal of the unmanned aerial vehicle 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.

[0008] The frequency domain signal is taken as input, the covariance matrix and the steering matrix are calculated in combination with the position vector of the microphone, the corresponding adaptive weight is generated, and then the beam output in the scanning direction and the power spectrum are calculated.

[0009] The trajectory fusion is performed based on the time sequence of the power spectrum, the trajectory of the unmanned aerial vehicle is determined, the real-time coordinates of the unmanned aerial vehicle are calculated based on the azimuth angle of the cross microphone array, and the trajectory of the unmanned aerial vehicle is predicted in combination with the trajectory.

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

[0011] Constructing a channel complex matrix with frequency domain signals of the microphone array, and calculating the average covariance of the microphone array combined with time sequence data of the channel complex matrix;

[0012] Based on the scanning angle of the double arms in the cross array, a direction angle unit vector is calculated to determine the relative time delay in the cross array, thereby constructing a corresponding steering vector, which simulates the ideal phase relationship of sound sources reaching each microphone in the cross array from different directions;

[0013] Determining a regularized covariance matrix corresponding to the covariance matrix, and generating a corresponding adaptive weight combined with the regularized covariance matrix and the steering matrix.

[0014] In one embodiment, the calculation formula of the adaptive weight includes:

[0015]

[0016] Wherein, w is the adaptive weight, is the regularized covariance matrix, is the steering vector, and H is the conjugate transpose.

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

[0018] With the adaptive weight and the channel complex matrix 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] Based on the beam output, the sound pressure energy received by the cross array is calculated to determine 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] Wherein, represents the beamforming result of the θ direction to the frequency f k , m is the microphone number, w m is the adaptive weight of the mth microphone, X m is the complex spectrum value of the mth microphone, and * represents the complex conjugate.

[0023] The calculation formula of the power spectrum includes:

[0024]

[0025] Wherein, Power spectrum of the direction of θ, k is frequency index, f k is the center frequency of the kth frequency point.

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

[0027] calculating the power spectrum combined with the adaptive weight, locating the peak value of the weighted power spectrum, determining the target direction of the UAV per frame based on the peak value;

[0028] and performing biaxial correlation of the peak value in a cross array to determine an effective angle pair, the angle pair including an east-west angle and a north-south angle;

[0029] performing Kalman filtering on each frame of power spectrum to output a smoothed angle pair trajectory point.

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

[0031] In the cross array, the distance between the microphones and the midpoint of the cross array is dynamically adjusted based on the distance between the microphones and the midpoint of the cross array;

[0032] Or, the sensitivity of the edge microphone is dynamically adjusted based on the distance between the microphone and the midpoint of the cross array.

[0033] Embodiments of the present application provide a UAV detection system based on a microphone cross array, the system comprising:

[0034] a setting module for setting a microphone array, the microphone array being a cross array, the cross array performing periodic scanning;

[0035] a receiving module for receiving an original sound source signal of a UAV, performing signal filtering on the original sound source signal, performing FFT transformation on the time domain filtered signal, and outputting a frequency domain signal;

[0036] a weight module for taking the frequency domain signal as input, calculating a covariance matrix and a steering matrix in combination with the position vector of the microphone, and generating corresponding adaptive weights, and then calculating the beam output in the scanning direction and the power spectrum;

[0037] a trajectory module for performing trajectory fusion based on the time series of the power spectrum to determine the trajectory of the UAV, calculating the real-time coordinates of the UAV based on the azimuth angle of the cross microphone array, and combining the trajectory to predict the trajectory of the UAV.

[0038] Embodiments of the present application provide an electronic device comprising a processor and a memory;

[0039] The processor is connected to the memory;

[0040] The memory is configured to store executable program code.

[0041] The processor executes a program corresponding to the executable program code stored in the memory to perform the method of one or more embodiments.

[0042] The embodiment of the present application provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the unmanned aerial vehicle detection method based on a microphone cross array.

[0043] In view of the above, in one or more embodiments of the present specification, a microphone array is provided, the microphone array is a cross array, the cross array performs periodic scanning; a raw sound source signal of an unmanned aerial vehicle is received, signal filtering is performed on the raw sound source signal, the time domain filtered signal is subjected to FFT transformation, and a frequency domain signal is output; the frequency domain signal is taken as input, a covariance matrix and a steering matrix are calculated in combination with the position vector of the microphone, corresponding adaptive weights are generated, and then the beam output in the scanning direction and the power spectrum are calculated; trajectory fusion is performed based on the time sequence of the power spectrum, the trajectory of the unmanned aerial vehicle is determined, the real-time coordinates of the unmanned aerial vehicle are calculated based on the azimuth angle of the cross microphone array, and the trajectory of the unmanned aerial vehicle is predicted in combination with the trajectory information. In this way, the signal-to-noise ratio of the received sound source of the unmanned aerial vehicle can be improved, the resistance to low-frequency interference is improved, the maximum detection distance and the positioning accuracy are improved, the response speed is increased due to the independent scanning of the two arms of the cross array, and the sound source receiving range is also improved due to the scanning range of the cross array. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0045] Figure 1 is a flowchart of an unmanned aerial vehicle detection method based on a microphone cross array provided by an embodiment of the present specification.

[0046] Figure 2 is a structural schematic diagram of an unmanned aerial vehicle detection system based on a microphone cross array provided by an embodiment of the present specification.

[0047] Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0048] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that these implementations are discussed solely for the purpose of illustrating aspects of the subject matter described herein and are not a limitation of the scope, applicability, or examples set forth in the claims. Changes in the function and arrangement of elements discussed can be made without departing from the scope of the subject matter described herein. Various examples can omit, substitute, or add various procedures or components as appropriate. For instance, the methods described can be performed in an order different than that described, and / or various steps can be added, omitted, or combined. Also, features described with respect to some examples can be combined in other examples.

[0049] As used herein, the terms "includes," "including," "has," "having," "contains," "containing," "comprises," "comprising," "is" and "wherein" are open-ended terms. The term "based on" means "based, at least in part, on." The terms "one or more of" and "one or more" means "one, two, three, four, or five or more." The terms "a plurality" and "plurality" mean "two or more." The term "another" means "at least one." The term "or" is inclusive and means "and / or". The phrase "associated with," the clause "associated with," and variations thereof mean "associated with and / or synonymous with" and vice versa. The phrase "determined by" means "determined at least in part by". The term "exemplary" means "an example of and / or "not limiting." The terms "first," "second," "third," "fourth," etc. mean "different and / or successive." The following claims can refer to other drawings. The terms "computer program medium" and "computer program product" mean any tangible computer readable medium excluding propagating signals per se. The terms "program element" and "computer program element" mean any component used to effectuate processes and / or logic, such as software, hardware, and / or various claims. The terms "process" and "logic" mean any set of operations and / or rules that effectuate a function and / or result. The terms "coupled" and "connected" mean directly or indirectly connected. The term "user" means any person and / or entity using the subject matter described herein. The term "non-transitory" means not intended to change with time. The term "memory" means any component that retains information.

[0050] As shown in Figure 1 The embodiments of the present application provide a UAV detection method based on a microphone cross array, which comprises the following steps:

[0051] In step S102, a microphone array is set, which is a cross array, and the cross array is periodically scanned.

[0052] Specifically, a two-dimensional microphone array is set, the microphone array is arranged in a cross shape and can be composed of two arms, and the physical parameters of the microphone array can be set as follows: the lengths of the two arms are at least greater than 1.6 m, and the two arms are an east-west arm and a south-north arm, respectively. At least 4 or more microphones are arranged on each arm, so as to collect more comprehensive UAV sound source data. A time synchronization module is arranged at the center point of the array to ensure that the sampling clocks of all channels are synchronized. Further, the parameters of the microphone, such as frequency response, dynamic range, noise floor, sensitivity, etc., can be dynamically adjusted according to the UAV detection requirements.

[0053] In addition, when the microphone array is arranged as a cross array, due to the array geometry asymmetry, the receiving efficiency is reduced due to the grazing incidence of sound waves, wind noise, etc. Therefore, edge beam distortion, edge microphone attenuation and other edge problems may occur at the edge of the cross array. For the above problems, the arrangement density of the end microphone can be set to be higher than that of the central area, for example, the microphone spacing near the center is 0.5m, and the microphone spacing near the edge can be 0.4m. Or the sensitivity of the edge microphone can be adjusted, such as increasing the microphone sensitivity by 3dB, so as to compensate for the loss of 6dB of signal-to-noise ratio. The edge compensation can compensate for the grazing signal attenuation, thereby reducing the low-frequency interference.

[0054] In step S104, the original sound source signal of the unmanned aerial vehicle 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.

[0055] Specifically, when the microphone array receives the original sound source signal of the unmanned aerial vehicle, the original sound source signal is subjected to band-pass filtering processing. The original sound source signal can be a microphone quantity channel original signal, and the filtered data subjected to band-pass filtering should consider the characteristic frequency of the unmanned aerial vehicle rotor, such as being set to 100-2000Hz, so as to suppress high-frequency interference greater than 2000Hz, including wind sound / circuit noise, and low-frequency interference less than 100Hz, such as vehicle / mechanical vibration, so as to improve the signal-to-noise ratio of the sound source signal. After the signal filtering, the time domain filtered signal is subjected to FFT transformation. Before the FFT transformation, the time domain filtered signal is preprocessed, including framing, time window overlap, and windowing processing. Then, the FFT calculation is performed to convert the time domain filtered signal into a complex spectrum, and the frequency domain complex signal is output.

[0056] In step S106, the frequency domain signal is input, the covariance matrix and the steering matrix are calculated in combination with the position vector of the microphone, the corresponding adaptive weight is generated, and then the beam output in the scanning direction and the power spectrum are calculated.

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

[0058]

[0059] Wherein, the element in the matrix represents the complex spectrum value of the mth microphone at the frequency f k .

[0060] Further, the covariance matrix is estimated. The method can be, for example, to take corresponding time sequence data, such as data including current frame and previous 49 frame data (a total of 50 frames), to calculate 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] wherein, represents the beamforming result in the direction of θ, and X is the frequency domain complex signal of the microphone channel input. In addition, in the actual calculation formula, an independent calculation can be performed for each microphone frequency point, that is:

[0079]

[0080] wherein, w m is the adaptive weight of the mth microphone, * represents the complex conjugate, and a complex vector y is output, .

[0081] Further, based on the above features, the azimuth-power spectrum of the microphone array is calculated, so as to determine the sound pressure energy of each angle received by the microphone array. The specific calculation formula is:

[0082]

[0083] wherein, represents the power spectrum in the direction of θ, k is the frequency index, and f k is the center frequency of the kth frequency point.

[0084] wherein, further, in combination with the cross microphone array, the power spectrum can be divided into the azimuth-power spectrum scanned in the east-west direction and the azimuth-power spectrum .

[0085] Step S108, based on the time sequence of the power spectrum, trajectory fusion is performed to determine the trajectory of the unmanned aerial vehicle, and based on the azimuth angle of the cross microphone array, the real-time coordinates of the unmanned aerial vehicle are determined, and in combination with the trajectory, the trajectory of the unmanned aerial vehicle is predicted.

[0086] Specifically, after determining the power spectrum, first, the trajectory data of the unmanned aerial vehicle is determined, including:

[0087] The power spectrum data after the power spectrum is combined with the time sequence, the east-west direction and the north-south direction , and in combination with the corresponding weight W, the weighted power is calculated:

[0088]

[0089] and locate the peak value of the weighted power spectrum, so as to extract the most significant target direction in each frame and suppress the edge false detection.

[0090] After determining the peak value of each frame, a two-axis correlation of the cross array is performed according to the thing detected according to the peak value and the original angle pair in the north-south direction, abnormal points with abnormal direction angles are filtered out, and the physical situation of the target motion is determined to be reasonable, so as to ensure that the flight angle of the unmanned aerial vehicle is an effective angle pair. The filtering of the abnormal points can be, for example, compared with the last frame of trajectory points, if the angle change is greater than 15 degrees / 0.1 seconds, it is considered to be an abnormal point, and the data is excluded.

[0091] Further, for the data smoothing between frames, Kalman filtering can be performed on the effective angle pair to output a stable trajectory point sequence, thereby outputting the smoothed angle trajectory point. The angle jitter of the trajectory point data can be suppressed by Kalman filtering. Thus, the discrete angle points output by beamforming are associated into continuous trajectories to provide stable input for positioning.

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

[0093] The target is positioned by combining the east-west direction and the north-south direction trajectory. The slant range of the target is determined by sound intensity ranging, double array estimation, etc. The three-axis coordinates of the coordinates can be determined by combining the east-west direction and the north-south direction angle with the target slant range. Thus, the two angle trajectories of the cross array are converted into three-dimensional coordinates, solving the height ambiguity problem. Taking sound intensity ranging as an example:

[0094] First, calculate the sound pressure level:

[0095]

[0096] Among them, is the pure signal after step S106 processing, and then the edge compensation is determined:

[0097]

[0098] Among them, θ is the direction angle of compensation, is the compensated data, such as 2.5 dB.

[0099] The ranging formula is: Thus, the target slant range d is determined, thereby improving the positioning accuracy according to the sound intensity positioning.

[0100] Further, the trajectory of the unmanned aerial vehicle can also be predicted, including:

[0101] Based on the trajectory point, the three-dimensional velocity of the unmanned aerial vehicle is determined, the motion model of the unmanned aerial vehicle is determined by combining the three-dimensional coordinates, and the dynamics model of the unmanned aerial vehicle is determined. In the dynamics model of the unmanned aerial vehicle, the current state of the unmanned aerial vehicle is input, the position of the unmanned aerial vehicle at the next frame or subsequent time point can be predicted, and the future coordinates of the unmanned aerial vehicle are determined.

[0102] The embodiment of the present application provides a kind of unmanned aerial vehicle detection method based on microphone cross array, microphone array is set, microphone array is cross array, cross array is periodically scanned;Receive the original sound source signal of unmanned aerial vehicle, the original sound source signal is filtered, the time domain filter signal is carried out FFT transformation, and frequency domain signal is output;With the frequency domain signal as input, covariance matrix and steering matrix are calculated in combination with the position vector of microphone, and corresponding adaptive weight is generated, and then the beam output in scanning direction and power spectrum are calculated;Trajectory fusion is carried out based on the time series of power spectrum, the trajectory of unmanned aerial vehicle is determined, and the real-time coordinates of unmanned aerial vehicle are calculated based on the azimuth angle of cross microphone array, and trajectory prediction is carried out on unmanned aerial vehicle in combination with trajectory information.This can improve the receiving signal-to-noise ratio of unmanned aerial vehicle sound source, improve the resistance to low-frequency interference;Maximum detection distance and positioning accuracy are improved;Cross array double-arm independent scanning increases response speed;The scanning range of cross array also improves the sound source receiving range.

[0103] Please see Figure 2 , Figure 2 It is a kind of structure schematic diagram of unmanned aerial vehicle detection system based on microphone cross array provided by the embodiment of the present application.As shown in Figure 2 , the system comprises:

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

[0105] Receiving module S204 is used to receive the original sound source signal of unmanned aerial vehicle, and the original sound source signal is filtered, the time domain filter signal is carried out FFT transformation, and frequency domain signal is output;

[0106] Weight module S206 is used to input the frequency domain signal, calculate covariance matrix and steering matrix in combination with the position vector of microphone, and generate corresponding adaptive weight, and then calculate the beam output in scanning direction and power spectrum;

[0107] Trajectory module S208 is used to carry out trajectory fusion based on the time series of power spectrum, determine the trajectory of the unmanned aerial vehicle, and calculate the real-time coordinates of the unmanned aerial vehicle based on the azimuth angle of cross microphone array, and carry out trajectory prediction on unmanned aerial vehicle in combination with the trajectory.

[0108] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented by means of software and / or hardware. The "unit" and "module" in the specification refer to software and / or hardware capable of independently completing or cooperating with other components to complete a specific function, wherein the hardware may, for example, be a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), and the like.

[0109] The various processing units and / or modules of the embodiments of the present application can be implemented by means of analog circuits that implement the functions described in the embodiments of the present application, or can be implemented by means of software that executes the functions described in the embodiments of the present application.

[0110] Referring to Figure 3 , a structural schematic diagram of an electronic device related to the embodiments of the present application is shown, which can be used to implement the method in the embodiments shown in Figure 1 . As shown in Figure 3 , the electronic device 300 can include at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0111] The communication bus 302 is used to realize the connection and communication between the components.

[0112] The user interface 303 can include a display screen (Display) and a camera (Camera), and the optional user interface 303 can further include a standard wired interface and a wireless interface.

[0113] The network interface 304 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0114] The processor 301 can include one or more processing cores. The processor 301 connects various parts within the entire electronic device 300 by various interfaces and lines, and performs various functions of the electronic device 300 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Alternatively, the processor 301 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), a programmable logic array (PLA). The processor 301 can be integrated with a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes an operating system, a user interface, and an application program; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

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

[0116] In Figure 3 ​The electronic device 300 shown, the user interface 303 is mainly used for providing an interface for user input, obtaining user input data; and the processor 301 can be used to call the interactive application program based on image generation stored in the memory 305, and specifically perform the following operations: setting a microphone array, the microphone array is a cross array, the cross array is periodically scanned; receiving a raw sound source signal of a drone, performing signal filtering on the raw sound source signal, performing FFT transformation on the time domain filtered signal, and outputting a frequency domain signal; taking the frequency domain signal as input, combining the position vector of the microphone to calculate the covariance matrix and the steering matrix, and generating the corresponding adaptive weight, and then calculating the beam output in the scanning direction and the power spectrum; based on the time sequence of the power spectrum, trajectory fusion is performed, the trajectory of the drone is determined, and based on the azimuth angle of the cross microphone array, the real-time coordinates of the drone are calculated, and the trajectory of the drone is predicted in combination with the trajectory information.

[0117] The application also provides a computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of the above method. The computer readable storage medium can include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0118] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the application is not limited by the action sequence described, because according to the application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the application.

[0119] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0120] In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments described above is merely illustrative, and the division of the units can be changed according to actual needs. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0121] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0122] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0123] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable memory. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned memory includes: a U disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0124] A person of ordinary skill in the art can understand that all or part of the steps of the various methods of the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer readable memory, which can include a flash disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc.

[0125] The above described embodiments of the present description have been described. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing can be advantageous.

Claims

1. A UAV detection method 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, The regularized covariance matrix, H is the guiding 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.

Images