Airspace multi-interference parameter sensing method combined with Capon algorithm

Abstract

The invention provides an airspace multi-interference parameter sensing method combined with a Capon algorithm. The method comprises the following steps: carrying out covariance matrix estimation on a received signal; scanning all angles in a target search range to obtain a discrete angle vector, and further constructing a steering vector; based on a Capon algorithm, performing spatial spectrum coarse estimation on a target search range by using a minimum variance undistorted criterion; obtaining 2p power spectrum densities with the highest peak value in the spatial spectrum coarse estimation result and incident angles corresponding to the 2p power spectrum densities; determining a characteristic value and a characteristic vector of the covariance matrix, and obtaining a plurality of signal estimation incident angles according to the characteristic value and the characteristic vector; according to a plurality of signal estimation incidence angles, screening real signal incidence angles from incidence angles corresponding to the power spectral density, determining the number of the real signal incidence angles and the power spectral density of each real signal incidence angle, and removing false peaks and false peaks through a cross spatial spectrum under the condition that the number of information sources is unknown, so as to obtain a signal source number; and reliable measurement of the signal angle is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and particularly to a method for perceiving multiple interference parameters in the spatial domain by combining the Capon algorithm. Background Art

[0002] Array signal processing is an important part of the signal processing process. By processing the spatial signals received by an antenna array arranged in space, the information carried by the spatial signals is extracted. The research content of array signal processing mainly includes two parts: digital beamforming technology and direction-of-arrival (DOA) estimation. DOA estimation measures the angle at which a target signal arrives at the array sensors, and estimates the DOA of the signal by processing the received signals. Digital beamforming technology uses digital signal processing technology to weight and synthesize the signals from the array antennas (or sensors) to achieve focusing and enhancement in a specific direction, while suppressing interference signals in other directions.

[0003] Scholars at home and abroad have proposed a series of methods for target direction finding, such as the Multiple Signal Classification (MUSIC) method, the Minimum Variance (Capon) method, etc. By estimating the covariance matrix of the mixed signals, searching for angles in the spatial range to construct an angle scanning vector, calculating the steering vector, constructing a spatial spectrum, and using the corresponding peak value of the spatial spectrum to estimate the signal angle. Then, adaptive beamforming is performed to suppress interference. The core of adaptive beamforming is to ensure the effective reception of the useful signal by adjusting the beamforming weights. The effective reception of the signal includes two aspects: one is to enhance the desired signal and form a main lobe beam, and the other is to suppress the interference signal and form a deep null in the interference direction. The adaptive weight coefficients are calculated by the beamforming algorithm, and the adaptive algorithm can essentially be transformed into a multi-parameter optimization problem under a certain criterion. The existing criteria include the maximum signal-to-interference-plus-noise ratio criterion, the minimum mean square error criterion, the minimum noise variance criterion, etc. First, estimate the covariance matrix of the mixed signals, solve the weight coefficients using the formula restricted by the criterion, obtain the weight coefficients of the algorithm, and then perform beamforming.

[0004] However, the MUSIC algorithm requires prior information such as the source angle, and the Capon algorithm is vulnerable to environmental influence and has low direction finding accuracy. Especially when estimating the spatial spectrum, there will be many incorrect information such as "false peaks" and "spurious peaks". The existing anti-interference measures in the spatial domain mainly obtain the direction of arrival of the interference by DOA estimation and use traditional methods to suppress it. They lack interactivity with the environment and it is difficult to perceive the power magnitude of the interference in a complex environment, and then make a dynamic decision on the suppression measures according to the environment.

[0005] 1. Traditional single-direction finding methods need to first estimate the number of signal sources and then perform direction finding. Moreover, direction finding is easily affected by the number of sampling snapshots and noise, resulting in inaccurate direction finding accuracy. When the number of snapshots is small and the signal-to-noise ratio is low, the angle measurement accuracy requirements cannot be met. Moreover, when direction finding multiple targets without knowing the number of signal sources, false peaks are likely to appear, with amplitudes higher than those of real signals, masking the real signals and causing misjudgment, resulting in incorrect estimation of the signal source angles and the number of signal sources, and affecting subsequent decisions on interference suppression.

[0006] 2. When selecting an algorithm to suppress interference after identifying the interference direction, there are problems of high complexity and large computational amount. When the external interference environment is uncertain and changing, it is difficult to determine which suppression method can be effective and can reasonably utilize computing power, cost, and time. Considering computing power, cost, and time, it is necessary to first make a judgment on the current environment to obtain the characteristic information of the interference power strength in the current environment, and dynamically decide to select an effective method that saves cost, computing power, and time to suppress interference. Summary of the Invention

[0007] To solve the above problems existing in the prior art, the present invention provides a method for perceiving multiple interference parameters in the spatial domain by combining the Capon algorithm, specifically including:

[0008] In a first aspect, the present invention provides a method for perceiving multiple interference parameters in the spatial domain by combining the Capon algorithm, including:

[0009] Estimate the covariance matrix of the received signal;

[0010] Scan all angles within the target search range according to a preset step size to obtain a discrete angle vector, and construct a steering vector according to the discrete angle vector. The discrete angle vector includes an elevation angle vector and an azimuth angle vector;

[0011] Based on the Capon algorithm, use the minimum variance distortionless criterion to perform a rough spatial spectrum estimation of the target search range;

[0012] Obtain the power spectral density with the highest peak among the rough spatial spectrum estimation results, and the incident angles corresponding to the power spectral density with the highest peak, where represents the number of array elements, and the incident angles include elevation angles and azimuth angles;

[0013] Determine the eigenvalues and eigenvectors of the estimated covariance matrix, and obtain multiple signal estimated incident angles according to the eigenvalues and eigenvectors;

[0014] According to the multiple signal estimated incident angles, screen out the real signal incident angles from the incident angles corresponding to the power spectral density, and determine the number of real signal incident angles and the power spectral density of each real signal incident angle.

[0015] In a second aspect, the present invention further provides a combined method for airspace multi-interference parameter perception device, including:

[0016] A receiving module, configured to estimate the covariance matrix of the received signal;

[0017] A processing module, configured to scan all angles within the target search range according to a preset step size to obtain a discrete angle vector matrix, construct a steering vector based on the discrete angle vector matrix; based on the Capon algorithm, use the minimum variance distortionless criterion to perform a rough spatial spectrum estimation on the target search range; obtain the 2p power spectral densities with the highest peaks and the incident angles corresponding to the 2p power spectral densities with the highest peaks in the rough spatial spectrum estimation result, where p represents the number of array elements; determine the eigenvalues and eigenvectors of the estimated covariance matrix, and obtain multiple signal estimated incident angles according to the eigenvalues and eigenvectors; screen out the true signal incident angles from the incident angles corresponding to the power spectral densities according to the multiple signal estimated incident angles, and determine the number of true signal incident angles and the power spectral densities of each true signal incident angle.

[0018] In a third aspect, the present invention further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete mutual communication through the communication bus;

[0019] The memory is used to store a computer program;

[0020] The processor is configured to implement any method provided in the first aspect when executing the program stored in the memory.

[0021] Advantages of the present invention:

[0022] The method for perceiving multi-interference parameters in the airspace by combining the Capon algorithm provided by the present invention estimates the covariance matrix by receiving signals; scans all angles within the target search range according to a preset step size to obtain a discrete angle vector, constructs a steering vector based on the discrete angle vector, and the discrete angle vector includes a pitch angle vector and an azimuth angle vector; based on the Capon algorithm, uses the minimum variance distortionless criterion to roughly estimate the spatial spectrum of the target search range; obtains the 2p power spectral densities with the highest peaks and the incident angles corresponding to the 2p power spectral densities with the highest peaks in the rough spatial spectrum estimation result; determines the eigenvalues and eigenvectors of the estimated covariance matrix, and obtains multiple signal estimated incident angles according to the eigenvalues and eigenvectors; screens out the true signal incident angles from the incident angles corresponding to the power spectral densities according to the multiple signal estimated incident angles, determines the number of true signal incident angles and the power spectral densities of each true signal incident angle, and can eliminate false peaks and spurious peaks through the cross spatial spectrum without knowing the number of signal sources, realizing reliable measurement of the signal angle. Based on the minimum mean square error criterion method, this method constructs a spatial spectrum by adding the eigenvectors of the covariance matrix decomposition, and screens out the false peaks and spurious peaks in the minimum mean square error criterion spectrum, improving the robustness and accuracy of the signal incident angle measurement.

[0023] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic flowchart of a method for perceiving multi-interference parameters in the airspace by combining the Capon algorithm provided by the present invention;

[0025] Figure 2 It is a schematic structural diagram of a device for perceiving multi-interference parameters in the airspace by combining the method provided by the present invention;

[0026] Figure 3 It is a schematic diagram of a simulation result provided by the present invention;

[0027] Figure 4 It is another schematic diagram of a simulation result provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The present invention will be further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0029] The object of the present invention is to provide a method for perceiving multiple interference parameters in the airspace by combining the Capon algorithm. By using the minimum variance method and the eigenvectors decomposed from the covariance matrix, spatial spectra are constructed respectively, and the overlapping values (within the set threshold range) are jointly compared to filter out the false peaks and pseudo-peaks in the spectra, cross-verify the angles of the true signal sources, and obtain the number of signal sources, so as to improve the robustness and accuracy of signal angle measurement. And by using the measured angles, the interference power information can be obtained in the constructed spectrum, which can make a rough judgment on the current environment, conduct analysis, and is beneficial to making a dynamic decision in the next step to select a suitable interference suppression method, achieving the effects of being effective and saving cost, computing power and time.

[0030] Figure 1 A method for perceiving multiple interference parameters in the airspace by combining the Capon algorithm provided by the present invention, as Figure 1 shown, the method includes:

[0031] S101. Estimate the covariance matrix of the received signal.

[0032] Optionally, estimating the covariance matrix of the received signal is expressed as:

[0033]

[0034] where K represents the number of sampling snapshots, x(k) represents the received signal of the k-th snapshot, and the superscript H represents conjugate transpose.

[0035] S102. Scan all angles within the target search range according to a preset step size to obtain a discrete angle vector, and construct a steering vector according to the discrete angle vector.

[0036] where the discrete angle vector includes an elevation angle vector and an azimuth angle vector.

[0037] In a possible implementation manner, scanning all angles within the target search range according to a preset step size to obtain a discrete angle vector matrix, and constructing a steering vector according to the discrete angle vector matrix includes the following steps A1 - A3:

[0038] A1. Discretize the target search range in the elevation angle direction and the azimuth angle direction respectively to obtain a plurality of elevation angles and a plurality of azimuth angles.

[0039] A2. Scan all elevation angles and azimuth angles according to a preset step size to obtain an elevation angle vector and an azimuth angle vector, where the elevation angle vector is expressed as:

[0040] θ = [θ 0 ... θ i ... θ end ,

[0041] The azimuth angle vector is expressed as:

[0042]

[0043] θ represents the pitch angle vector, and θ i represents the i-th pitch angle, represents the azimuth angle vector, and represents the j-th azimuth angle.

[0044] A3. According to the pitch angle vector and the azimuth angle vector, multiple incident angles are obtained, and the steering vector of each incident angle is determined, expressed as:

[0045]

[0046] wherein, represents the steering vector of the incident angle , r n = [x n ; y n ; z n represents the position coordinates of the n-th array element, and λ represents the wavelength.

[0047] S103. Based on the Capon algorithm, using the minimum variance distortionless criterion, a spatial spectrum rough estimation is performed on the target search range.

[0048] Optionally, based on the Capon algorithm, using the minimum variance distortionless criterion, a spatial spectrum rough estimation is performed on the target search range, expressed as:

[0049]

[0050] wherein, represents the power spectral density of the incident angle obtained from the spatial spectrum rough estimation , and R -1 represents the inverse matrix of the covariance matrix R.

[0051] S104. Obtain the 2p power spectral densities with the highest peaks in the spatial spectrum rough estimation result, and the incident angles corresponding to the 2p power spectral densities with the highest peaks.

[0052] wherein, p represents the number of array elements.

[0053] The incident angles include pitch angles and azimuth angles.

[0054] S105. Determine the eigenvalues and eigenvectors of the estimated covariance matrix, and based on the eigenvalues and eigenvectors, obtain multiple signal estimated incident angles.

[0055] In a possible implementation, the eigenvalues and eigenvectors of the estimated covariance matrix are determined, and based on the eigenvalues and eigenvectors, multiple signal estimated incident angles are obtained, including the following steps B1 - B3:

[0056] B1. Determine the eigenvalues and eigenvectors of the estimated covariance matrix, and in the order of eigenvalues from large to small, obtain the eigenvectors corresponding to the first p - 1 eigenvalues, denoted as:

[0057] v = [v 1 , v 2 , …, v l , … v p-1 ,

[0058] where v l represents the l-th eigenvector.

[0059] B2. Determine the spatial spectrum of each eigenvector according to the steering vector and the eigenvectors corresponding to the p - 1 eigenvalues.

[0060] B3. Determine the incident angle corresponding to the spectral peak of the spatial spectrum of each eigenvector as the signal estimated angle.

[0061] Further, optionally, determine the spatial spectrum of each eigenvector according to the steering vector and the eigenvectors corresponding to the p - 1 eigenvalues, denoted as:

[0062]

[0063] where represents the spatial spectrum value determined by the l-th eigenvector and the incident angle , and l represents the index of the eigenvector.

[0064] By decomposing the estimated covariance matrix, the decomposed eigenvectors can form a signal subspace and a noise subspace. Among them, the first p - 1 eigenvectors corresponding to large eigenvalues form the signal subspace, and different signals are unevenly distributed in the spaces formed by different eigenvectors, so that different signals can be distinguished. Exemplarily, if there are M signals, they must be distributed in the signal subspace constructed by the eigenvectors corresponding to M large eigenvalues. The spatial spectra formed by the latter p - 1 - M eigenvalues have spectral peaks close to 0 because there are no signals. Search for projections within the spatial range. Only the eigenvectors corresponding to each signal direction will project onto this space to form spectral peaks, and the orthogonal projection values of other irrelevant signals or noises to this signal subspace are almost zero.

[0065] S106. According to the multiple signal estimated incident angles, screen out the true signal incident angles from the incident angles corresponding to the power spectral density, determine the number of true signal incident angles and the power spectral density of each true signal incident angle.

[0066] Optionally, estimate the incident angle based on multiple signals, and screen out the incident angles of real signals from the incident angles corresponding to the power spectral density, including the following steps C1 and C2:

[0067] C1. Determine the deviation between any angle in the estimated incident angles of multiple signals and any angle in the incident angles corresponding to the power spectral density.

[0068] C2. Determine the incident angle corresponding to the power spectral density with the deviation within the preset error range as the incident angle of the real signal.

[0069] Exemplarily, the preset error range is 1 / 2 of the 3dB beam width.

[0070] This method constructs a spatial spectrum using the minimum mean square error criterion, takes angle estimation values more than the number of signal sources and records the power values of the spectral peaks. At the same time, it uses the eigenvectors containing the characteristics of the signal subspace information decomposed from the covariance matrix to search and project in the spatial range to form a spatial spectrum; only the eigenvectors corresponding to the signal directions will project onto this space to form spectral peaks, and the orthogonal projection values of other irrelevant signals or noises to this space are almost zero. Then, based on this, the signal estimation angle values matching the eigenvectors are obtained; the two estimated angle values are matched, and the successfully matched ones are determined as the real signal angles, which is equivalent to screening the estimation results of the minimum mean square error criterion once, discarding the estimated false peaks and pseudo-peak values, and cross-verifying the correctness of the estimated angle values. Without first estimating the number of signal sources, it can simultaneously estimate the accurate incident angles and their numbers of signals, as well as the signal power values of the real angles, and can effectively reduce the computational complexity.

[0071] Furthermore, this method further includes:

[0072] Estimate the angles and power spectral densities of other interfering signals based on the known incident angles of the target signals and the incident angles of each real signal; determine the interference-to-signal ratio of other interfering signals to the incident angles of each target signal according to the power spectral densities of each other interfering signal and the target signal, and determine the anti-interference method according to the interference-to-signal ratio.

[0073] This method uses the interference power information obtained during direction finding as prior information to make a judgment on the current environment and decide on a suitable anti-interference method, which can achieve a reasonable allocation of algorithm complexity and time cost consumption, and then achieve an accurate estimate of the relative magnitudes of interference powers.

[0074] The method for perceiving spatial multi-interference parameters combining the Capon algorithm provided by the present invention estimates the covariance matrix by receiving signals; scans all angles within the target search range according to a preset step size to obtain a discrete angle vector matrix, constructs a steering vector based on the discrete angle vector matrix, and the discrete angle vector matrix includes an elevation angle vector and an azimuth angle vector; based on the Capon algorithm, uses the minimum variance distortionless criterion to roughly estimate the spatial spectrum of the target search range; obtains the 2p power spectral densities with the highest peaks and the incident angles corresponding to the 2p power spectral densities with the highest peaks in the rough spatial spectrum estimation result, where p represents the number of array elements, and the incident angles include elevation angles and azimuth angles; determines the eigenvalues and eigenvectors of the estimated covariance matrix, and obtains multiple signal estimated incident angles according to the eigenvalues and eigenvectors; screens out the true signal incident angles from the incident angles corresponding to the power spectral densities according to the multiple signal estimated incident angles, and determines the number of true signal incident angles and the power spectral density of each true signal incident angle. It can eliminate false peaks and spurious peaks through the cross spatial spectrum without knowing the number of signal sources, and realize reliable measurement of the signal angle. Based on the minimum mean square error criterion method, this method constructs a spatial spectrum by adding the eigenvectors of the covariance matrix decomposition, and screens out the false peaks and spurious peaks in the minimum mean square error criterion spectrum, improving the robustness and accuracy of the signal incident angle measurement.

[0075] Figure 2 FIG. is a structural schematic diagram of a device for perceiving spatial multi-interference parameters by a combined method provided by the present invention, as Figure 2 shown, the device includes:

[0076] A receiving module 21 for estimating the covariance matrix of the received signal;

[0077] A processing module 22 for scanning all angles within the target search range according to a preset step size to obtain a discrete angle vector matrix, constructing a steering vector based on the discrete angle vector matrix; based on the Capon algorithm, using the minimum variance distortionless criterion to roughly estimate the spatial spectrum of the target search range; obtaining the 2p power spectral densities with the highest peaks and the incident angles corresponding to the 2p power spectral densities with the highest peaks in the rough spatial spectrum estimation result, where p represents the number of array elements; determining the eigenvalues and eigenvectors of the estimated covariance matrix, and obtaining multiple signal estimated incident angles according to the eigenvalues and eigenvectors; screening out the true signal incident angles from the incident angles corresponding to the power spectral densities according to the multiple signal estimated incident angles, and determining the number of true signal incident angles and the power spectral density of each true signal incident angle.

[0078] To further prove the beneficial effects of the present invention, the present invention also provides a set of simulation data, Figure 3 which is the spatial spectrum estimation result obtained based on the traditional Capon method,Figure 4 The spatial spectrum estimation result obtained by the method provided by the present invention. By comparison, it can be seen that the spatial spectrum obtained by the traditional method contains many false peaks and spurious peaks, and it is impossible to distinguish which are real signals, and it is impossible to obtain the number and angle of real signals clearly.

[0079] The present invention also provides a structure of an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus.

[0080] The memory is used to store computer programs.

[0081] The processor is used to implement the steps provided in the above method embodiments when executing the programs stored on the memory.

[0082] The communication interface is used for communication between the above electronic device and other devices.

[0083] The method provided by the embodiments of the present invention can be applied to electronic devices. Specifically, the electronic device can be: a desktop computer, a portable computer, a smart mobile terminal, a server, etc. This is not limited here, and any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0084] For the device / electronic device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the specific content, beneficial effects, etc., please refer to the partial description of the method embodiments.

[0085] The terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0086] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A spatial domain multi-interference parameter perception method combined with Capon algorithm, characterized in that: include: Estimating the covariance matrix of the received signal; Scan all angles within the target search range according to a preset step length to obtain a discrete angle vector, and construct a steering vector according to the discrete angle vector, wherein the discrete angle vector includes a pitch angle vector and an azimuth angle vector; Based on the Capon algorithm, the minimum variance distortion-free criterion is used to roughly estimate the spatial spectrum of the target search range; Acquire 2p power spectrum densities with the highest peak values ​​in the rough estimation result of the spatial spectrum, and the incident angles corresponding to the 2p power spectrum densities with the highest peak values, where p represents the number of array elements, and the incident angles include a pitch angle and an azimuth angle; Determining eigenvalues ​​and eigenvectors of the estimated covariance matrix, and obtaining a plurality of signal estimated incident angles according to the eigenvalues ​​and the eigenvectors; The incident angles are estimated according to the multiple signals, the real signal incident angles are screened out from the incident angles corresponding to the power spectrum density, and the number of the real signal incident angles and the power spectrum density of each of the real signal incident angles are determined.

2. The method according to claim 1, characterized in that The covariance matrix of the received signal is estimated as follows: Where K represents the number of sampling snapshots, x(k) represents the received signal of the k-th snapshot, and the superscript H represents the conjugate transpose.

3. The method according to claim 2, characterized in that The step of scanning all angles within the target search range according to a preset step length to obtain a discrete angle vector matrix, and constructing a steering vector according to the discrete angle vector matrix includes: Discretizing the target search range in the elevation angle direction and the azimuth angle direction respectively to obtain a plurality of elevation angles and a plurality of azimuth angles; According to the preset step length, all pitch angles and azimuth angles are scanned to obtain a pitch angle vector and an azimuth angle vector, wherein the pitch angle vector is expressed as: θ=[θ0...θ i ...i end ], The azimuth angle vector is expressed as: θ represents the pitch angle vector, θ i represents the i-th pitch angle, represents the azimuth angle vector, represents the jth azimuth; According to the pitch angle vector and the azimuth angle vector, multiple incident angles are obtained, and the steering vector of each incident angle is determined, which is expressed as: in, Indicates the angle of incidence The guiding vector, r n =[x n ;y n ;z n ] represents the position coordinate of the nth array element, and λ represents the wavelength.

4. The method according to claim 3, characterized in that The minimum variance distortion-free criterion is used to roughly estimate the spatial spectrum of the target search range, which is expressed as: in, Represents the incident angle obtained by rough estimation of the spatial spectrum The power spectral density, R -1 Represents the inverse matrix of the covariance matrix R.

5. The method according to claim 4, characterized in that The step of determining the eigenvalues ​​and eigenvectors of the estimated covariance matrix, and obtaining a plurality of signal estimated incident angles according to the eigenvalues ​​and the eigenvectors, comprises: Determine the eigenvalues ​​and eigenvectors of the estimated covariance matrix, and obtain the eigenvectors corresponding to the first p-1 eigenvalues ​​in descending order of eigenvalues, expressed as: v=[v1,v2...v p-1 ]; Determine a spatial spectrum of each of the eigenvectors according to the steering vector and the eigenvectors corresponding to the p-1 eigenvalues; The incident angle corresponding to the peak of the spatial spectrum of each of the eigenvectors is determined as the signal estimation angle.

6. The method according to claim 5, characterized in that The spatial spectrum of each eigenvector is determined according to the eigenvectors corresponding to the steering vector and the p-1 eigenvalues, which is expressed as: in, The table consists of the lth eigenvector and the incident angle Determine the spatial spectrum value, l represents the index of the eigenvector.

7. The method according to claim 6, characterized in that The estimating the incident angle according to the multiple signals and selecting the real signal incident angle from the incident angles corresponding to the power spectrum density includes: Determining a deviation between any angle of the plurality of signal estimated incident angles and any angle of the incident angle corresponding to the power spectral density; The incident angle corresponding to the power spectrum density whose deviation is within a preset error range is determined as the real signal incident angle.

8. The method according to claim 1, characterized in that Also includes: According to the known incident angle of the target signal and the incident angle of each of the real signals, the angle and power spectral density of other interference signals are estimated; According to the power spectral density of each of the other interference signals and the power spectral density of the target signal, an interference-to-signal ratio of the incident angle of the other interference signal to each of the target signals is determined, and an anti-interference method is determined according to the interference-to-signal ratio.

9. A joint method spatial domain multi-interference parameter perception device, characterized in that: include: A receiving module, used for estimating the covariance matrix of the received signal; A processing module is used to scan all angles within a target search range according to a preset step size to obtain a discrete angle vector matrix, and construct a steering vector according to the discrete angle vector matrix; based on the Capon algorithm, a minimum variance distortion-free criterion is used to perform a rough spatial spectrum estimation of the target search range; obtain the 2p power spectrum densities with the highest peak values ​​in the rough spatial spectrum estimation results, and the incident angles corresponding to the 2p power spectrum densities with the highest peak values, where p represents the number of array elements; determine the eigenvalues ​​and eigenvectors of the estimated covariance matrix, and obtain multiple signal estimated incident angles according to the eigenvalues ​​and the eigenvectors; according to the multiple signal estimated incident angles, screen out the real signal incident angles from the incident angles corresponding to the power spectrum densities, and determine the number of the real signal incident angles and the power spectrum density of each of the real signal incident angles.

10. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing any of the methods described in claims 1-8 when executing a program stored in a memory.

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