A method, device, computer equipment and storage medium for detecting weak signals of moving multiple targets of FMCW array radar under strong clutter

Abstract

A method and device for detecting weak signals of multiple moving targets of FMCW array radar under strong clutter, the method comprising: performing fast Fourier transform processing on difference frequency signals of different frequency modulation periods of different array elements to obtain distance spectra of different targets (S101); performing cancellation processing on the distance spectrum to eliminate clutter signals generated by stationary targets (S102); performing a first detection on the data after cancellation processing to obtain a first target, and the first detection adopts ordered statistic constant false alarm detection (S103); performing a second detection on the first target so that the number of second targets obtained after the second detection meets a preset condition, and the number of second targets is less than the number of first targets (S104); performing a third detection on the second target to obtain a third target, and the third detection adopts array information to perform spatial spectrum estimation, and eliminates false targets by using the difference between the peak values ​​of the spatial spectrum estimation of real targets and false targets (S105); and outputting the distance, speed and angle information of the third target (S106). The method can significantly improve the weak signal detection capability of multiple moving targets under strong clutter environment.

Description

Technical Field

[0001] The present invention belongs to the field of radar detection technology, and in particular relates to a method, device, computer equipment and storage medium for detecting weak signals of moving multiple targets of an FMCW array radar under strong clutter. Background Art

[0002] Compared with pulse Doppler radar, FMCW (frequency modulated carrier wave) radar has special advantages. Its close-range blind spot is generally less than 1 meter (or even a few centimeters), which is very meaningful for many application scenarios that require close-range ranging.

[0003] For FMCW radar, detecting weak signals of multiple moving targets under strong clutter background is another difficulty. Strong clutter environment, especially non-uniform clutter environment, brings new challenges to constant false alarm detection of moving multiple weak signals. In many application fields of FMCW radar, the above problems are faced. Taking the currently popular vehicle-mounted radar as an example, the environment it faces is often complex (urban streets, many motor vehicles, non-motor vehicles, and pedestrians), with various clutters (including non-uniform clutter), multiple targets, and the echo signals of many targets are often weak (pedestrians, etc.). Many scholars have proposed some methods to improve the detection performance of FMCW radar, but many of them are based on triangular wave methods. Triangular waves need to pair the distance and speed of the target, which in itself will restrict the detection ability of weak targets and easily introduce false targets. Therefore, the problem of detecting moving weak signals under strong clutter background has not been well solved. In engineering practice, it is impossible to take into account both the low false alarm rate and the low missed alarm rate of weak signal detection, resulting in low detection and recognition efficiency.

[0004] Technical issues

[0005] The purpose of the present invention is to propose a method and device for detecting weak signals of moving multiple targets of FMCW array radar under strong clutter. The method effectively improves the detection performance of weak signal targets, takes into account the low false alarm rate and low missed alarm rate of weak signal detection, and has high detection and recognition efficiency.

[0006] Technical Solutions

[0007] In order to achieve the above-mentioned object, a method for detecting weak signals of moving multiple targets by FMCW array radar under strong clutter in a first aspect of the present invention is provided, wherein the FMCW array radar adopts sawtooth wave signal modulation, and the method comprises:

[0008] The difference frequency signals of different array elements with different frequency modulation periods are processed by fast Fourier transform to obtain the range spectra of different targets.

[0009] performing cancellation processing on the range spectrum to eliminate clutter signals generated by stationary targets;

[0010] Performing a first detection on the data after the cancellation processing to obtain a first target; the first detection adopts ordered statistical constant false alarm detection;

[0011] Performing a second detection on the first target so that the number of second targets obtained after the second detection meets a preset condition; the number of the second targets is less than the number of the first targets;

[0012] Performing a third detection on the second target to obtain a third target, wherein the third detection uses array information to perform spatial spectrum estimation, and uses the difference between the peak values ​​of the spatial spectrum estimation of the real target and the false target to eliminate the false target;

[0013] The distance, speed and angle information of the third target are output.

[0014] In one embodiment of the present invention, the first detection specifically includes:

[0015] Determine a multiplication factor;

[0016] For each distance unit, a preset number of protection units and calculation units are set on both sides of the distance unit, and then the calculation units are sorted in descending order;

[0017] Selecting a calculation unit with a preset serial number and multiplying the multiplication factor as a threshold value for the first detection;

[0018] A comparator is used to compare the measured distance unit with the threshold value detected for the first time, and when the measured distance unit is greater than the threshold value, it is determined as the first target.

[0019] In one embodiment of the present invention, the second detection specifically includes:

[0020] The initial threshold value for the second detection is obtained by multiplying the mean value of the first target obtained by the first detection by a preset coefficient;

[0021] Using a comparator to compare the measured distance unit with the initial threshold value of the second detection, when the measured distance unit is greater than the initial threshold value of the second detection, it is determined as the second target;

[0022] When the number of second targets is higher than the preset condition, the initial threshold value is increased by a preset search step value as the comparison threshold value for the next detection search; when the number of second targets is lower than the preset condition, the initial threshold value is reduced by a preset search step value as the comparison threshold value for the next detection search; repeat the above comparison and judgment until the number of second targets meets the preset condition.

[0023] In one embodiment of the present invention, the third detection specifically includes:

[0024] calculating the distance and speed of the second target;

[0025] Calculate the covariance matrix of the corresponding distance and speed;

[0026] Perform eigendecomposition on the covariance matrix of different targets respectively;

[0027] Achieve angle estimation and calculate MUSIC spectrum with minimal optimization search;

[0028] The MUSIC spectrum is judged as a true or false target. When the MUSIC spectrum peak is greater than a preset judgment threshold, it is judged as the third target; otherwise, it is judged as a non-target.

[0029] In one embodiment of the present invention, the preset decision threshold is 5-10 times the average value of all spectrum peaks of MUSIC.

[0030] In one embodiment of the present invention, the distance and speed of the second target are calculated using the following expression, wherein the distance is calculated using the expression Where c is the speed of light, T c Represents a single frequency modulation cycle, F s Indicates the sampling rate, M bin Indicates the FFT unit where the target distance is located, B indicates the signal bandwidth, N FFT Indicates the number of distance FFT points; the calculation speed uses the expression

[0031] Among them, V bin Indicates the FFT unit where the target speed is located, f indicates the frequency of the transmitted signal, K indicates the number of frequency modulation cycles in the speed measurement cycle, T f Indicates the speed measurement period.

[0032] In one embodiment of the present invention, the covariance matrix The characteristic decomposition is Among them, U S is the signal subspace spanned by the eigenvectors corresponding to the large eigenvalues, and U N It is a noise subspace spanned by eigenvectors corresponding to small eigenvalues; the eigenvalues ​​after eigendecomposition are used to estimate the number of information sources.

[0033] In one embodiment of the present invention, the angle estimation is performed by the minimum optimization search using the expression The MUSIC spectrum is calculated using the expression

[0034] In one embodiment of the present invention, the frequency modulation working bandwidth of the FMCW array radar satisfies the expression Among them, d resrepresents the distance resolution, c represents the speed of light, and B represents the frequency modulation working bandwidth; a single frequency modulation cycle of the FMCW array radar satisfies the expression Among them, V max Indicates the maximum measurable speed, T c represents a single frequency modulation period, λ represents the wavelength of the transmitted signal; the speed measurement period of the FMCW array radar satisfies the expression Among them, V res Indicates the velocity resolution, T f represents the speed measurement period, λ represents the wavelength of the transmitted signal; the sampling rate of the FMCW array radar satisfies the expression Among them, F s represents the sampling rate, d max Indicates the maximum detectable distance, T c represents a single FM cycle, c represents the speed of light, and B represents the FM working bandwidth.

[0035] In order to achieve the above-mentioned object, the FMCW array radar moving multi-target weak signal detection device under strong clutter in the second aspect of the embodiment of the present invention, the FMCW array radar adopts sawtooth wave signal modulation, and the device comprises:

[0036] The Fourier transform module is used to perform fast Fourier transform processing on the difference frequency signals of different array elements with different frequency modulation periods to obtain the range spectra of different targets;

[0037] A cancellation processing module, used for performing cancellation processing on the range spectrum to eliminate clutter signals generated by stationary targets;

[0038] A first detection module is used to perform a first detection on the data after the cancellation process to obtain a first target; the first detection adopts an ordered statistic constant false alarm detection;

[0039] A second detection module, used for performing a second detection on the first target so that the number of second targets obtained after the second detection meets a preset condition; the number of the second targets is less than the number of the first targets;

[0040] A third detection module is used to perform a third detection on the second target to obtain a third target, wherein the third detection uses array information to perform spatial spectrum estimation, and uses the difference between the peak values ​​of the spatial spectrum estimation of the real target and the false target to eliminate the false target;

[0041] The output module is used to output the distance, speed and angle information of the third target.

[0042] In one embodiment of the present invention, the first detection module is specifically used for:

[0043] Determine a multiplication factor;

[0044] For each distance unit, a preset number of protection units and calculation units are set on both sides of the distance unit, and then the calculation units are sorted in descending order;

[0045] Selecting a calculation unit with a preset serial number and multiplying the multiplication factor as a threshold value for the first detection;

[0046] A comparator is used to compare the measured distance unit with the threshold value detected for the first time, and when the measured distance unit is greater than the threshold value, it is determined as the first target.

[0047] In one embodiment of the present invention, the second detection module is specifically used for:

[0048] The initial threshold value for the second detection is obtained by multiplying the mean value of the first target obtained by the first detection by a preset coefficient;

[0049] Using a comparator to compare the measured distance unit with the initial threshold value of the second detection, when the measured distance unit is greater than the initial threshold value of the second detection, it is determined as the second target;

[0050] When the number of second targets is higher than the preset condition, the initial threshold value is increased by a preset search step value as the comparison threshold value for the next detection search; when the number of second targets is lower than the preset condition, the initial threshold value is reduced by a preset search step value as the comparison threshold value for the next detection search; repeat the above comparison and judgment until the number of second targets meets the preset condition.

[0051] In one embodiment of the present invention, the third detection module is specifically used for:

[0052] calculating the distance and speed of the second target;

[0053] Calculate the covariance matrix of the corresponding distance and speed;

[0054] Perform eigendecomposition on the covariance matrix of different targets respectively;

[0055] Achieve angle estimation and calculate MUSIC spectrum with minimal optimization search;

[0056] The MUSIC spectrum is judged as a true or false target. When the MUSIC spectrum peak is greater than a preset judgment threshold, it is judged as the third target; otherwise, it is judged as a non-target.

[0057] In one embodiment of the present invention, the preset decision threshold is 5-10 times the average value of all spectrum peaks of MUSIC.

[0058] In one embodiment of the present invention, the distance and speed of the second target are calculated using the following expression, wherein the distance is calculated using the expression Where c is the speed of light, T c Represents a single frequency modulation cycle, F s Indicates the sampling rate, M bin Indicates the FFT unit where the target distance is located, B indicates the signal bandwidth, N FFT Indicates the number of distance FFT points; the calculation speed uses the expression

[0059] Among them, V bin Indicates the FFT unit where the target speed is located, f indicates the frequency of the transmitted signal, K indicates the number of frequency modulation cycles in the speed measurement cycle, T f Indicates the speed measurement period.

[0060] In one embodiment of the present invention, the covariance matrix The characteristic decomposition is Among them, U S is the signal subspace spanned by the eigenvectors corresponding to the large eigenvalues, and U N It is a noise subspace spanned by eigenvectors corresponding to small eigenvalues; the eigenvalues ​​after eigendecomposition are used to estimate the number of information sources.

[0061] In one embodiment of the present invention, the angle estimation is performed by the minimum optimization search using the expression The MUSIC spectrum is calculated using the expression

[0062] In one embodiment of the present invention, the frequency modulation working bandwidth of the FMCW array radar satisfies the expression Among them, d res represents the distance resolution, c represents the speed of light, and B represents the frequency modulation working bandwidth; a single frequency modulation cycle of the FMCW array radar satisfies the expression Among them, V max Indicates the maximum measurable speed, T c represents a single frequency modulation period, λ represents the wavelength of the transmitted signal; the speed measurement period of the FMCW array radar satisfies the expression Among them, V res Indicates the velocity resolution, T f represents the speed measurement period, λ represents the wavelength of the transmitted signal; the sampling rate of the FMCW array radar satisfies the expression Among them, F s represents the sampling rate, d max Indicates the maximum detectable distance, T c represents a single FM cycle, c represents the speed of light, and B represents the FM working bandwidth.

[0063] In order to achieve the above-mentioned purpose, the computer device of the embodiment of the third aspect of the present invention includes: one or more processors, a memory and one or more computer programs, wherein the processor and the memory are connected via a bus, the one or more computer programs are stored in the memory and are configured to be executed by the one or more processors, and the processor implements the above-mentioned FMCW array radar moving multi-target weak signal detection method under strong clutter when executing the computer program.

[0064] In order to achieve the above-mentioned purpose, the storage medium of the fourth aspect embodiment of the present invention is used to store a computer program. When the computer program is run on a computer device, the computer device executes the above-mentioned FMCW array radar moving multi-target weak signal detection method under strong clutter.

[0065] Beneficial Effects

[0066] This method eliminates the clutter generated by stationary targets through cancellation processing, and then detects the data without clutter. At this time, the detection is carried out three times. The first detection ensures that the weak signal can pass the threshold to ensure a low false alarm rate; the second detection further narrows the range to eliminate false targets to reduce the amount of calculation for the third detection; the third detection uses array information to estimate the spatial spectrum, and uses the difference in the peak value of the spatial spectrum estimation of the real target and the false target to distinguish, so as to eliminate the remaining false targets. After three detections, the detection performance of weak signal targets can be significantly improved, taking into account the low false alarm rate and low false alarm rate of weak signal detection, and the detection and recognition efficiency is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic diagram of a flow chart of a method for detecting weak signals of multiple moving targets of an FMCW array radar under strong clutter provided by an embodiment of the present invention;

[0068] Figure 2 This is the structural diagram of the first detection;

[0069] Figure 3 This is a schematic diagram of the process of the third test;

[0070] Figure 4 A schematic diagram of a module of a FMCW array radar moving multi-target weak signal detection device under strong clutter provided by an embodiment of the present invention;

[0071] Figure 5 A structural block diagram of a computer device provided in an embodiment of the present invention.

[0072] Embodiments of the present invention

[0073] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0074] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In addition, in the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0075] Any process or method description in the flowchart or otherwise described herein may be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes other implementations, in which the functions may not be performed in the order shown or discussed, including performing the functions in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art of the present invention. With reference to the following description and drawings, these and other aspects of the embodiments of the present invention will be clear. In these descriptions and drawings, some specific embodiments of the embodiments of the present invention are specifically disclosed to represent some ways of implementing the principles of the embodiments of the present invention, but it should be understood that the scope of the embodiments of the present invention is not limited thereto. On the contrary, the embodiments of the present invention include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0076] The embodiment of the present invention provides a method for detecting weak signals of moving multiple targets by FMCW array radar under strong clutter. The FMCW array radar adopts sawtooth wave signal modulation, such as Figure 1 As shown, the method includes:

[0077] S101, performing fast Fourier transform processing on difference frequency signals of different array elements and different frequency modulation periods to obtain range spectra of different targets.

[0078] The input array difference frequency signal data includes K frequency modulation periods of N array elements, and each frequency modulation period has M sampling points. The K frequency modulation periods of N array elements are processed by fast Fourier transform (FFT) to obtain the distance spectrum of different targets. The distance spectrum is 3D stereo data.

[0079] S102: performing cancellation processing on the range spectrum to eliminate clutter signals generated by stationary targets.

[0080] The purpose of the cancellation process is to eliminate the clutter generated by stationary targets and leave only moving targets. The clutter cancellation process can eliminate the clutter signals generated by stationary targets, reduce the number of echoes involved in subsequent calculations, and thus simplify the subsequent separation of multiple moving targets.

[0081] S103, performing a first detection on the data after the cancellation processing to obtain a first target; the first detection adopts ordered statistic constant false alarm detection.

[0082] The ordered statistic constant false alarm detection is actually an adaptive threshold detection, which uses the reference window samples to estimate the parameters of the environmental noise to achieve the characteristics of constant false alarm rate. Environmental noise includes clutter and interference. When the reference environment unit is a non-uniformly distributed multi-target environment, the ordered statistic constant false alarm detection has better anti-interference ability than other types of constant false alarm detection.

[0083] like Figure 2 The figure shows the structural block diagram of the first detection. The first detection specifically includes:

[0084] Determine a multiplication factor T; for each distance unit D, set a preset number of protection units (Pn, Pn+1) and calculation units (X 1 -X N ), and then sort the calculation units in descending order; the preset sequence number can be selected to represent the frequency modulation cycle number K of the speed measurement cycle. The calculation unit Z with the preset sequence number K is selected and multiplied by the multiplication factor T as the threshold value S of the first detection. In the figure, D is the target unit to be measured. Because the power of the target may leak to the adjacent units, the several units adjacent to the target are not used as the background clutter estimation, but as the protection unit P.

[0085] A comparator is used to compare the measured distance unit D with the threshold value S detected for the first time. When the measured distance unit D is greater than the threshold value S, D is determined as the first target.

[0086] For example: after performing 1024-point FFT on the echo difference frequency signal of a set of FM signals, 1024 output data are obtained. Data 1-512 are taken for detection (because the output of FFT is symmetrical). Each data is detected one by one, and one data represents a frequency point (that is, a certain distance). If the number of protection units is 4 and the number of calculation units is 8, then for the 20th unit, the protection units are 18, 19, 21, 22; the calculation units are 14, 15, 16, 17, 23, 24, 25, 26.

[0087] In order to detect the target from the weak signal, the threshold of the first detection needs to be lowered to avoid missing the real target. The first detection is to detect the suspected target from the non-uniform clutter.

[0088] S104. Perform a second detection on the first targets so that the number of second targets obtained after the second detection meets a preset condition; the number of the second targets is less than the number of the first targets.

[0089] The second detection is used to eliminate some false targets. Since the requirements of the first ordered statistic constant false alarm detection are relatively low, some false targets may be mistaken for targets. The second detection further narrows the range and eliminates false targets.

[0090] The second test specifically includes:

[0091] The initial threshold value of the second detection is obtained by multiplying the mean value of the first target obtained by the first detection by a preset coefficient. The initial threshold value of the second detection search is obtained by calculating the mean value of the targets that have passed the threshold in the first detection, and the initial threshold value of the second detection search is obtained by multiplying the mean value by a preset coefficient. In order to reduce the probability of false alarms and take into account both strong and weak signal detection, the initial fixed threshold value of the second detection search can be set smaller, that is, the preset system can be set smaller.

[0092] A comparator is used to compare the measured distance unit with the initial threshold value of the second detection. When the measured distance unit is greater than the initial threshold value of the second detection, it is determined as the second target.

[0093] When the number of second targets is higher than the preset condition, the initial threshold value is increased by a preset search step value as the comparison threshold value for the next detection search; when the number of second targets is lower than the preset condition, the initial threshold value is reduced by a preset search step value as the comparison threshold value for the next detection search; repeat the above comparison and judgment until the number of second targets meets the preset condition.

[0094] Set a preset search step value (for example, 10%). If the second detection is lower than the preset condition, the second initial threshold will be lowered by one search step value and the search will continue until the target is found. If the number of targets found in the second search is higher than the preset condition, it means that there are too many false targets, then the second initial threshold will be increased by one search step value and the search will be repeated.

[0095] The preset conditions for the number of targets in the second detection can generally be set as needed (depending on the hardware and requirements). For example, the vehicle-mounted corner radar and the front radar are obviously different. The corner radar has a short range, so the number of targets can be set to be smaller (for example, 10-20). The front radar has a long range and the hardware performance has enough margin, so the number of targets can be set to be larger (for example, 30-40).

[0096] The number of targets obtained in the m-1th detection search is higher than the preset condition. At this time, the decision threshold needs to be increased. When the decision threshold is increased by a preset search step value and the mth detection search is performed, it is found that the number of targets obtained in the detection search is lower than the preset condition. At this time, the decision threshold needs to be lowered. The amplitude of reducing the judgment threshold cannot remain unchanged according to the original adjustment amplitude, otherwise the data will not converge, and the number of targets obtained in the second detection will never meet the preset conditions. At this time, the adjustment amplitude can be halved. That is, the preset search step value is increased or decreased by half. This method helps the number of second targets obtained in the second detection to converge quickly to meet the preset conditions.

[0097] For example: the preset search step value is 10%. The initial target threshold is 100. When the detection threshold is 100, the number of second targets obtained by the second detection is higher than the preset condition, so the detection threshold needs to be increased. When the detection threshold is increased by a preset search step value to 100+10=110, the number of second targets obtained by the second detection is lower than the preset condition. At this time, the detection threshold needs to be lowered. At this time, if the preset search step value 110-10=100 is lowered, the number of second targets obtained by the second detection will always fail to meet the preset condition. Therefore, the preset search step value needs to be halved. The reduction is halved, that is, the detection threshold is set to 110-5=105, and so on.

[0098] S105, performing a third detection on the second target to obtain a third target, wherein the third detection uses array information to perform spatial spectrum estimation, and uses the difference between the peak values ​​of the spatial spectrum estimation of the real target and the false target to eliminate the false target.

[0099] like Figure 3 As shown, the third test specifically includes the following steps:

[0100] S1051. Calculate the distance and speed of the second target.

[0101] The distance and speed of the second target are calculated using the following expressions, where the distance is calculated using the expression

[0102] Where c is the speed of light, T c Represents a single frequency modulation cycle, F s Indicates the sampling rate, M bin Indicates the FFT unit where the target distance is located, B indicates the signal bandwidth, N FFT Indicates the number of distance FFT points.

[0103] The speed is calculated using the expression

[0104] Among them, V binIndicates the FFT unit where the target speed is located, f indicates the frequency of the transmitted signal, K indicates the number of frequency modulation cycles in the speed measurement cycle (also the number of points in the speed FFT), T f Indicates the speed measurement cycle (equal to KT c ). The above formula involves the definition of speed in which the target approaches the radar and the speed is positive.

[0105] S1052: Calculate the covariance matrix of the corresponding distance and speed.

[0106] Two situations need to be distinguished here: one is that there is no coherent signal (that is, two or more targets have the same distance and speed). In this case, the array covariance matrix can be obtained by the original data S (three-dimensional data: one frame of data has N array elements, each array element has K frequency modulation periods, and each frequency modulation period has a certain number of sampling point data) after two FFT transformations (the first is to perform FFT on the sampling point data of a single frequency modulation period to obtain the target distance information; the second is to perform FFT on the K frequency modulation period data of the corresponding target distance based on the first FFT to obtain the target speed information) FFT (N×K×N FFT Dimension) is obtained, that is, the data of the corresponding target distance unit and speed unit of each array element are arranged into a vector X=[X 1 X 2 … X N ] T ,in

[0107] X n =S FFT (n,V bin ,M bin ),n=1,2,···N;V bin =1,2,···K,M bin =1,2,···N FFT . At this time, the covariance matrix of the corresponding target is:

[0108]

[0109] The superscript 'H' indicates the conjugate transpose.

[0110] Another case is when there is a coherent signal. In this case, the covariance matrix needs to add up the unit data without speed target corresponding to the range unit with target. In this case, X n Become a vector (X n =S FFT (n,1:K,M bin ),n=1,2,LN), X becomes an N×K matrix. At this time, the covariance matrix of the corresponding target is:

[0111]

[0112] S1053. Perform eigendecomposition on the covariance matrices of different targets respectively.

[0113] Covariance matrix The characteristic decomposition is

[0114] Among them, U S is the subspace spanned by the eigenvectors corresponding to the large eigenvalues, i.e., the signal subspace, and U N The subspace spanned by the eigenvectors corresponding to the small eigenvalues ​​is also the noise subspace. The eigenvalues ​​after eigendecomposition can be used to estimate the number of information sources. The correct estimation of the number of information sources is the premise of spatial spectrum estimation. Under ideal conditions, the signal subspace and the noise subspace in the data space are orthogonal to each other, that is, the steering vector in the signal subspace is also orthogonal to the noise subspace: a H (θ)U N =0.

[0115] S1054: Implement angle estimation and calculate the MUSIC spectrum using a minimum optimization search.

[0116] The angle estimation here is achieved by minimum optimization search, that is,

[0117] This gives the spatial spectrum estimation formula of the classic MUSIC algorithm:

[0118]

[0119] S1055: Perform true or false target judgment on the MUSIC spectrum, and judge it as the third target when the MUSIC spectrum peak is greater than a preset judgment threshold, otherwise it is judged as a non-target.

[0120] The calculated MUSIC spectrum is used to judge true and false targets: if the MUSIC spectrum peak is greater than the preset judgment threshold, it is judged as the third target, otherwise it is judged as a non-target. The preset judgment threshold is determined by a large amount of data simulation and combined with measured data. The preset judgment threshold can be set to 5-10 times the average value of all MUSIC spectrum peaks.

[0121] S106: Output the distance, speed and angle information of the third target.

[0122] After three tests, false targets can be eliminated and the distance, speed and angle of the real target can be obtained.

[0123] In this embodiment, the specific steps of the first detection, the second detection and the third detection are all optimal solutions. In other embodiments, the specific steps of the first detection, the second detection and the third detection can also be implemented in other forms to achieve the corresponding effect of each detection.

[0124] Of course, in actual engineering implementation, many problems need to be solved, such as correction of array errors, mutual coupling of array elements and amplitude and phase inconsistencies of channels, characteristic decomposition of the matrix, correct estimation of the number of signal sources, signal decorrelation processing, etc., which will not be elaborated here.

[0125] Compared with triangle wave FMCW signal modulation, sawtooth wave FMCW signal modulation has obvious advantages. It does not require distance and speed pairing and can effectively improve the detection performance of weak signal targets. This application uses sawtooth wave FMCW signal system. The signal waveform and hardware parameters of the FMCW array radar with sawtooth wave signal modulation need to meet the following conditions:

[0126] (1) Select the appropriate frequency modulation working bandwidth according to the distance measurement accuracy requirements. The relationship between distance resolution and working bandwidth is:

[0127]

[0128] Among them, d res represents the range resolution, c represents the speed of light, and B represents the frequency modulation working bandwidth of the FMCW array radar. If the range resolution needs to be within 1 meter, the frequency modulation working bandwidth should be greater than 150MHz.

[0129] (2) Select appropriate frequency modulation period and speed measurement period according to the speed measurement requirements (including speed accuracy requirements and maximum target speed requirements) and signal wavelength. For sawtooth FMCW signal modulation, the maximum measurable speed and speed resolution need to satisfy the following relationships:

[0130]

[0131] Among them, V max Indicates the maximum measurable speed, V res Indicates the velocity resolution, T c Represents a single frequency modulation cycle, T f Indicates the speed measurement cycle, which generally contains K frequency modulation cycles. If the wavelength of the transmitted signal is 0.004 meters, the maximum measurable speed (relative radial speed with the radar) is not less than 50 meters per second, and the speed resolution is not greater than 1 meter per second, then T c Should not be greater than 20 microseconds, T f Should be no less than 2 milliseconds.

[0132] (3) Select an appropriate sampling rate based on the maximum measurement distance and FM working bandwidth. The sampling rate of the signal should satisfy the following relationship:

[0133]

[0134] Among them, F s represents the sampling rate, d max Indicates the maximum detectable distance, T c represents a single FM cycle, c represents the speed of light, and B represents the FM working bandwidth. If B is 200MHz, d max is 200 meters, T c is 20 microseconds, then F s Should not be less than 40 / 3MHz.

[0135] Select the appropriate number of array elements according to the angle measurement requirements. For conventional angle measurement methods, the angle resolution satisfies the following relationship (Rayleigh limit):

[0136]

[0137] Among them, θ res represents the angular resolution, N represents the number of array elements, d represents the array element spacing, and θ represents the angle between the target and the array normal (if the target is located on the array normal, θ=0). If the array element spacing is half a wavelength, the target is located on the array normal, and the number of array elements is 8, then the angular resolution is approximately 14.324°. For single-target angle measurement, the single-pulse angle measurement accuracy can ideally reach 2% of the angle resolution. Of course, there are many factors in actual engineering that restrict the angle resolution, which can generally reach 10%. For some application scenarios, it can meet the requirements, but for vehicle-mounted application scenarios, this angle resolution is not enough. High-resolution spatial spectrum estimation provides a solution for high-resolution angle measurement of small arrays, which can break through the Rayleigh limit and obtain angle measurement accuracy far superior to traditional angle measurement methods.

[0138] This method is used to detect weak signals of multiple moving targets of FMCW array radar under strong clutter background. After simulation verification, the number of array elements is 8, the target signal-to-noise ratio is 0dB, and the clutter-to-noise ratio is 15dB. Under the comprehensive error (mutual coupling (between adjacent array elements) 0.1+0.1i, array element error (within plus or minus 10%), amplitude and phase inconsistency (amplitude within plus or minus 1dB, phase within plus or minus 10°)), the detection success probability of 100 Monte Carlo simulations reaches 100%, and the mean deviation and variance are no more than 0.01° and 0.02 respectively. This method can not only effectively detect targets (extremely low false alarm probability and missed alarm probability when the signal-to-noise ratio is 0dB), but also has high angle measurement accuracy.

[0139] The embodiment of the present invention also provides a FMCW array radar moving multi-target weak signal detection device under strong clutter, wherein the FMCW array radar adopts sawtooth wave signal modulation, such as Figure 4 As shown, the device comprises:

[0140] The Fourier transform module is used to perform fast Fourier transform processing on the difference frequency signals of different array elements with different frequency modulation periods to obtain the range spectra of different targets.

[0141] The input array difference frequency signal data includes K frequency modulation periods of N array elements, and each frequency modulation period has M sampling points. The K frequency modulation periods of N array elements are processed by fast Fourier transform (FFT) to obtain the distance spectrum of different targets. The distance spectrum is 3D stereo data.

[0142] The cancellation processing module is used to perform cancellation processing on the range spectrum to eliminate clutter signals generated by stationary targets.

[0143] The purpose of the cancellation process is to eliminate the clutter generated by stationary targets and leave only moving targets. The clutter cancellation process can eliminate the clutter signals generated by stationary targets, reducing the number of echoes involved in subsequent calculations, thereby simplifying the subsequent separation of multiple moving targets.

[0144] The first detection module is used to perform a first detection on the data after the cancellation processing to obtain a first target; the first detection adopts ordered statistical constant false alarm detection.

[0145] The first detection module is specifically used for:

[0146] Determine a multiplication factor;

[0147] For each distance unit, a preset number of protection units and calculation units are set on both sides of the distance unit, and then the calculation units are sorted in descending order;

[0148] Selecting a calculation unit with a preset serial number and multiplying the multiplication factor as a threshold value for the first detection;

[0149] A comparator is used to compare the measured distance unit with the threshold value detected for the first time, and when the measured distance unit is greater than the threshold value, it is determined as the first target.

[0150] The second detection module is used to perform a second detection on the first target so that the number of second targets obtained after the second detection meets a preset condition; the number of the second targets is less than the number of the first targets.

[0151] The second detection is used to eliminate some false targets. Since the requirements of the first ordered statistic constant false alarm detection are relatively low, some false targets may be mistaken for targets. The second detection further narrows the range and eliminates false targets.

[0152] The second detection module is specifically used for:

[0153] The initial threshold value for the second detection is obtained by multiplying the mean value of the first target obtained by the first detection by a preset coefficient;

[0154] Using a comparator to compare the measured distance unit with the initial threshold value of the second detection, when the measured distance unit is greater than the initial threshold value of the second detection, it is determined as the second target;

[0155] When the number of second targets is higher than the preset condition, the initial threshold value is increased by a preset search step value as the comparison threshold value for the next detection search; when the number of second targets is lower than the preset condition, the initial threshold value is reduced by a preset search step value as the comparison threshold value for the next detection search; repeat the above comparison and judgment until the number of second targets meets the preset condition.

[0156] When the data does not converge, that is, the number of targets obtained by the second detection cannot meet the preset conditions, the preset search step value needs to be increased or decreased by half. This method helps the number of second targets obtained by the second detection to quickly meet the preset conditions.

[0157] The third detection module is used to perform a third detection on the second target to obtain a third target, wherein the third detection uses array information to perform spatial spectrum estimation, and uses the difference between the peak values ​​of the spatial spectrum estimation of the real target and the false target to eliminate the false target.

[0158] The third detection module is specifically used for:

[0159] The distance and speed of the second target are calculated.

[0160] The distance and speed of the second target are calculated using the following expressions, where the distance is calculated using the expression Where c is the speed of light, T c Represents a single frequency modulation cycle, F s Indicates the sampling rate, M bin Indicates the FFT unit where the target distance is located, B indicates the signal bandwidth, N FFT Indicates the number of distance FFT points.

[0161] The speed is calculated using the expression

[0162] Among them, V binIndicates the FFT unit where the target speed is located, f indicates the frequency of the transmitted signal, K indicates the number of frequency modulation cycles in the speed measurement cycle (also the number of points in the speed FFT), T f Indicates the speed measurement cycle (equal to KT c ). The above formula involves the definition of speed in which the target approaches the radar and the speed is positive.

[0163] Compute the covariance matrix of the corresponding distances and velocities.

[0164] Two situations need to be distinguished here: one is that there is no coherent signal (that is, two or more targets have the same distance and speed). In this case, the array covariance matrix can be obtained by the original data S (three-dimensional data: one frame of data has N array elements, each array element has K frequency modulation periods, and each frequency modulation period has a certain number of sampling point data) after two FFT transformations (the first is to perform FFT on the sampling point data of a single frequency modulation period to obtain the target distance information; the second is to perform FFT on the K frequency modulation period data of the corresponding target distance based on the first FFT to obtain the target speed information) FFT (N×K×N FFT Dimension) is obtained, that is, the data of the corresponding target distance unit and speed unit of each array element are arranged into a vector X=[X 1 X 2 … X N ] T ,in

[0165] X n =S FFT (n,V bin ,M bin ),n=1,2,···N;V bin =1,2,···K,M bin =1,2,···N FFT .this

[0166] When , the covariance matrix of the corresponding target is:

[0167]

[0168] The superscript 'H' indicates the conjugate transpose.

[0169] Another case is when there is a coherent signal. In this case, the covariance matrix needs to add up the unit data without speed target corresponding to the range unit with target. In this case, X n Become a vector (X n =S FFT (n,1:K,M bin ),n=1,2,LN), X becomes an N×K matrix. At this time, the covariance matrix of the corresponding target is:

[0170]

[0171] The covariance matrices of different targets are subjected to eigendecomposition respectively.

[0172] Covariance matrix The characteristic decomposition is

[0173] Among them, U S is the subspace spanned by the eigenvectors corresponding to the large eigenvalues, i.e., the signal subspace, and U N The subspace spanned by the eigenvectors corresponding to the small eigenvalues ​​is also the noise subspace. The eigenvalues ​​after eigendecomposition can be used to estimate the number of information sources. The correct estimation of the number of information sources is the premise of spatial spectrum estimation. Under ideal conditions, the signal subspace and the noise subspace in the data space are orthogonal to each other, that is, the steering vector in the signal subspace is also orthogonal to the noise subspace: a H (θ)U N =0.

[0174] Angle estimation is achieved with a minimal optimization search and the MUSIC spectrum is calculated.

[0175] The angle estimation here is achieved by minimum optimization search, that is,

[0176] This gives the spatial spectrum estimation formula of the classic MUSIC algorithm:

[0177]

[0178] The MUSIC spectrum is judged as a true or false target. When the MUSIC spectrum peak is greater than a preset judgment threshold, it is judged as the third target; otherwise, it is judged as a non-target.

[0179] The calculated MUSIC spectrum is used to judge true and false targets: if the MUSIC spectrum peak is greater than the preset judgment threshold, it is judged as the third target, otherwise it is judged as a non-target. The preset judgment threshold is determined by a large amount of data simulation and combined with measured data. The preset judgment threshold can be set to 5-10 times the average value of all MUSIC spectrum peaks.

[0180] The output module is used to output the distance, speed and angle information of the third target.

[0181] After three tests, false targets can be eliminated and the distance, speed and angle of the real target can be obtained.

[0182] The frequency modulation working bandwidth of the FMCW array radar satisfies the expression Among them, d resrepresents the distance resolution, c represents the speed of light, and B represents the frequency modulation working bandwidth; a single frequency modulation cycle of the FMCW array radar satisfies the expression Among them, V max Indicates the maximum measurable speed, T c represents a single frequency modulation period, λ represents the wavelength of the transmitted signal; the speed measurement period of the FMCW array radar satisfies the expression Among them, V res Indicates the velocity resolution, T f represents the speed measurement period, λ represents the wavelength of the transmitted signal; the sampling rate of the FMCW array radar satisfies the expression Among them, F s represents the sampling rate, d max Indicates the maximum detectable distance, T c represents a single FM cycle, c represents the speed of light, and B represents the FM working bandwidth.

[0183] Figure 5 As shown, a specific structural block diagram of a computer device provided in an embodiment of the present application, the computer device 100 includes: one or more processors 101, a memory 102, and one or more computer programs, wherein the processor 101 and the memory 102 are connected via a bus, the one or more computer programs are stored in the memory 102, and are configured to be executed by the one or more processors 101, and the processor 101 implements the steps of the FMCW array radar moving multi-target weak signal detection method under strong clutter provided in an embodiment of the present application when executing the computer program. The computer device includes a server and a terminal, etc. The computer device can be a personal computer, a mobile terminal or a vehicle-mounted device, and the mobile terminal includes at least one of a mobile phone, a tablet computer, a personal digital assistant or a wearable device, etc.

[0184] In order to implement the above embodiment, the present invention also proposes a storage medium for storing a computer program. When the computer program is run on a computer device, the computer device executes the steps of the FMCW array radar moving multi-target weak signal detection method under strong clutter provided in one embodiment of the present application.

[0185] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0186] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0187] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0188] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0189] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0190] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0191] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0192] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting weak signals of moving multiple targets of FMCW array radar under strong clutter, characterized in that: The FMCW array radar adopts sawtooth wave signal modulation, and the method comprises: The difference frequency signals of different array elements with different frequency modulation periods are processed by fast Fourier transform to obtain the range spectra of different targets. performing cancellation processing on the range spectrum to eliminate clutter signals generated by stationary targets; Performing a first detection on the data after the cancellation processing to obtain a first target; the first detection adopts ordered statistical constant false alarm detection; Performing a second detection on the first target so that the number of second targets obtained after the second detection meets a preset condition; the number of the second targets is less than the number of the first targets; Performing a third detection on the second target to obtain a third target, wherein the third detection uses array information to perform spatial spectrum estimation, and uses the difference between the peak values ​​of the spatial spectrum estimation of the real target and the false target to eliminate the false target; The distance, speed and angle information of the third target are output.

2. The method according to claim 1, characterized in that The first detection specifically includes: Determine a multiplication factor; For each distance unit, a preset number of protection units and calculation units are set on both sides of the distance unit, and then the calculation units are sorted in descending order; Selecting a calculation unit with a preset serial number and multiplying the multiplication factor as a threshold value for the first detection; A comparator is used to compare the measured distance unit with the threshold value detected for the first time, and when the measured distance unit is greater than the threshold value, it is determined as the first target.

3. The method according to claim 1, characterized in that The second detection specifically includes: The initial threshold value for the second detection is obtained by multiplying the mean value of the first target obtained by the first detection by a preset coefficient; Using a comparator to compare the measured distance unit with the initial threshold value of the second detection, when the measured distance unit is greater than the initial threshold value of the second detection, it is determined as the second target; When the number of second targets is higher than the preset condition, the initial threshold value is increased by a preset search step value as the comparison threshold value for the next detection search; when the number of second targets is lower than the preset condition, the initial threshold value is reduced by a preset search step value as the comparison threshold value for the next detection search; repeat the above comparison and judgment until the number of second targets meets the preset condition.

4. The method according to claim 1, characterized in that: The third test specifically includes: calculating the distance and speed of the second target; Calculate the covariance matrix of the corresponding distance and speed; Perform eigendecomposition on the covariance matrix of different targets respectively; Achieve angle estimation and calculate MUSIC spectrum with minimal optimization search; The MUSIC spectrum is judged as a true or false target. When the MUSIC spectrum peak is greater than a preset judgment threshold, it is judged as the third target; otherwise, it is judged as a non-target.

5. The method according to claim 4, characterized in that The preset decision threshold is 5-10 times the average value of all spectrum peaks of MUSIC.

6. The method according to claim 4, characterized in that The distance and speed of the second target are calculated using the following expressions, where the distance is calculated using the expression Where c is the speed of light, T c Represents a single frequency modulation cycle, F s Indicates the sampling rate, M bin Indicates the FFT unit where the target distance is located, B indicates the signal bandwidth, N FFT Indicates the number of distance FFT points; the calculation speed uses the expression Among them, V bin Indicates the FFT unit where the target speed is located, f indicates the frequency of the transmitted signal, K indicates the number of frequency modulation cycles in the speed measurement cycle, T f Indicates the speed measurement period.

7. The method according to claim 4, characterized in that Covariance matrix The characteristic decomposition is Among them, U S is the signal subspace spanned by the eigenvectors corresponding to the large eigenvalues, and U N It is a noise subspace spanned by eigenvectors corresponding to small eigenvalues; the eigenvalues ​​after eigendecomposition are used to estimate the number of information sources.

8. The method according to claim 4, characterized in that The angle estimation is achieved by a minimal optimization search using the expression Where a represents the steering vector; the MUSIC spectrum is calculated using the expression 9. The method according to claim 1, characterized in that: The frequency modulation working bandwidth of the FMCW array radar satisfies the expression Among them, d res represents the distance resolution, c represents the speed of light, and B represents the frequency modulation working bandwidth; a single frequency modulation cycle of the FMCW array radar satisfies the expression Among them, V max Indicates the maximum measurable speed, T c represents a single frequency modulation period, λ represents the wavelength of the transmitted signal; the speed measurement period of the FMCW array radar satisfies the expression Among them, V res Indicates the velocity resolution, T f represents the speed measurement period, λ represents the wavelength of the transmitted signal; the sampling rate of the FMCW array radar satisfies the expression Among them, F s represents the sampling rate, d max Indicates the maximum detectable distance, T c represents a single FM cycle, c represents the speed of light, and B represents the FM working bandwidth.

10. A FMCW array radar moving multi-target weak signal detection device under strong clutter, characterized in that: The FMCW array radar adopts sawtooth wave signal modulation, and the device comprises: The Fourier transform module is used to perform fast Fourier transform processing on the difference frequency signals of different array elements with different frequency modulation periods to obtain the range spectra of different targets; A cancellation processing module, used for performing cancellation processing on the range spectrum to eliminate clutter signals generated by stationary targets; A first detection module is used to perform a first detection on the data after the cancellation process to obtain a first target; the first detection adopts an ordered statistic constant false alarm detection; A second detection module, used for performing a second detection on the first target so that the number of second targets obtained after the second detection meets a preset condition; the number of the second targets is less than the number of the first targets; A third detection module is used to perform a third detection on the second target to obtain a third target, wherein the third detection uses array information to perform spatial spectrum estimation, and uses the difference between the peak values ​​of the spatial spectrum estimation of the real target and the false target to eliminate the false target; The output module is used to output the distance, speed and angle information of the third target.

11. The device according to claim 10, characterized in that The first detection module is specifically used for: Determine a multiplication factor; For each distance unit, a preset number of protection units and calculation units are set on both sides of the distance unit, and then the calculation units are sorted in descending order; Selecting a calculation unit with a preset serial number and multiplying the multiplication factor as a threshold value for the first detection; A comparator is used to compare the measured distance unit with the threshold value detected for the first time, and when the measured distance unit is greater than the threshold value, it is determined as the first target.

12. The device according to claim 10, characterized in that The second detection module is specifically used for: The initial threshold value for the second detection is obtained by multiplying the mean value of the first target obtained by the first detection by a preset coefficient; Using a comparator to compare the measured distance unit with the initial threshold value of the second detection, when the measured distance unit is greater than the initial threshold value of the second detection, it is determined as the second target; When the number of second targets is higher than the preset condition, the initial threshold value is increased by a preset search step value as the comparison threshold value for the next detection search; when the number of second targets is lower than the preset condition, the initial threshold value is reduced by a preset search step value as the comparison threshold value for the next detection search; repeat the above comparison and judgment until the number of second targets meets the preset condition.

13. The device according to claim 10, characterized in that The third detection module is specifically used for: calculating the distance and speed of the second target; Calculate the covariance matrix of the corresponding distance and speed; Perform eigendecomposition on the covariance matrix of different targets respectively; Achieve angle estimation and calculate MUSIC spectrum with minimal optimization search; The MUSIC spectrum is judged as a true or false target. When the MUSIC spectrum peak is greater than a preset judgment threshold, it is judged as the third target; otherwise, it is judged as a non-target.

14. The device according to claim 13, characterized in that The preset decision threshold is 5-10 times the average value of all spectrum peaks of MUSIC.

15. The device according to claim 13, characterized in that The distance and speed of the second target are calculated using the following expressions, where the distance is calculated using the expression Where c is the speed of light, T c Represents a single frequency modulation cycle, F s Indicates the sampling rate, M bin Indicates the FFT unit where the target distance is located, B indicates the signal bandwidth, N FFT Indicates the number of distance FFT points; the calculation speed uses the expression Among them, V bin Indicates the FFT unit where the target speed is located, f indicates the frequency of the transmitted signal, K indicates the number of frequency modulation cycles in the speed measurement cycle, T f Indicates the speed measurement period.

16. The device according to claim 13, characterized in that Covariance matrix The characteristic decomposition is Among them, U S is the signal subspace spanned by the eigenvectors corresponding to the large eigenvalues, and U N It is a noise subspace spanned by eigenvectors corresponding to small eigenvalues; the eigenvalues ​​after eigendecomposition are used to estimate the number of information sources.

17. The device according to claim 13, characterized in that The angle estimation is achieved by a minimal optimization search using the expression Where a represents the steering vector; the MUSIC spectrum is calculated using the expression 18. A computer device comprising: One or more processors, a memory, and one or more computer programs, wherein the processor and the memory are connected via a bus, the one or more computer programs are stored in the memory, and are configured to be executed by the one or more processors, characterized in that when the processor executes the computer program, the FMCW array radar moving multi-target weak signal detection method under strong clutter as described in any one of claims 1 to 9 is implemented.

19. A storage medium for storing a computer program, characterized in that: When the computer program is executed on a computer device, the computer device is enabled to execute the method for detecting weak signals of moving multiple targets of an FMCW array radar under strong clutter as claimed in any one of claims 1 to 9.

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