An adaptive intelligent optimization null-steering anti-jamming method based on interference detection

Abstract

An adaptive intelligent optimization zeroing anti-interference method based on interference detection is proposed. This method suppresses various types of interference and minimizes FPGA resource usage, making it easy to implement in engineering. The adaptive zeroing anti-interference method and device based on interference detection have advantages such as strong interference filtering capability, large number of satellites received, strong anti-interference capability, low computational resource usage, and good engineering feasibility. It also has good interference suppression capability against various types of interference.

Description

Technical Field

[0001] This invention relates to an adaptive intelligent optimization zeroing anti-interference method based on interference detection, belonging to the field of satellite navigation design. Background Technology

[0002] Satellite navigation anti-jamming technology aims to improve the performance of satellite navigation receivers in complex electromagnetic environments, ensuring the normal operation of weapon systems in combat environments. This technology involves adding anti-jamming antennas to the front end of ordinary receivers to form spatial filters. These filters remove interference signals while retaining useful satellite signals, ultimately guaranteeing the performance of satellite navigation terminals in complex electromagnetic environments. It is an essential tool for military satellite navigation technology. Satellite navigation anti-jamming technology is divided into adaptive nulling technology and digital multi-beam technology.

[0003] Adaptive nulling technology does not require knowledge of the satellite's arrival direction. Its adaptive spatial filter characteristics are: forming nulls in the interference area and a gain of "1" in the non-interference area. Digital multi-beam anti-interference technology requires the satellite's arrival direction as prior information, as shown in the figure below. Its spatial filter characteristics are: forming multiple beams at the satellite's arrival direction and forming nulls at the interference location. The satellite's arrival direction is acquired in two scenarios: one is when no interference is applied, acquiring the arrival direction of the visible satellite through a single antenna element; the other is when interference is applied, acquiring the satellite's arrival direction through adaptive nulling anti-interference technology.

[0004] In summary, adaptive zeroing technology can exist as an independent product form. At the same time, it also exists as a key satellite-to-priority information acquisition process in digital multi-beam anti-jamming technology. Therefore, adaptive zeroing anti-jamming technology is crucial in all satellite navigation anti-jamming equipment, and improving the performance of adaptive zeroing satellite navigation anti-jamming equipment helps to improve the anti-jamming performance of satellite navigation anti-jamming equipment.

[0005] In recent years, particle swarm optimization (PSO) has garnered widespread attention from scholars both domestically and internationally due to its unique advantages in solving complex problems involving large spatial domains, nonlinearity, combinatorial optimization, and global optimization. Simulated annealing has also been widely applied in array signal processing to implement non-uniform array beam design and solve non-convex optimization problems in beam design.

[0006] Current adaptive zeroing anti-interference technology uses a power inversion algorithm for interference suppression. However, this algorithm has a dip in the non-interference area under interference conditions, resulting in a low number of satellites received. In harsh and complex electromagnetic environments with strong or multiple interferences, the anti-interference performance cannot meet the requirements. Summary of the Invention

[0007] The technical problem solved by this invention is: in the current technology, under severe and complex electromagnetic environments such as strong interference and multiple interferences, the number of satellites received is small and the anti-interference performance does not meet the requirements. Therefore, an adaptive intelligent optimization zeroing anti-interference method based on interference detection is proposed.

[0008] The present invention solves the above-mentioned technical problem through the following technical solution:

[0009] An adaptive intelligent optimization zeroing anti-interference method based on interference detection includes:

[0010] Acquire the received data from each antenna element;

[0011] Narrowband processing is performed on the received data;

[0012] Narrowband interference filtering is performed on the frequency domain data after narrowband processing.

[0013] Interference detection is performed on frequency point 1 in the data of each frequency point after interference filtering to obtain interference information;

[0014] Generate the final anti-interference filter based on the interference information;

[0015] Interference suppression is achieved by using a final anti-interference filter to filter the received data of each antenna element.

[0016] The specific method for obtaining the received data of each antenna element is as follows:

[0017] The received signals of each antenna element are acquired, down-converted to obtain intermediate frequency analog signals, and after AD sampling and low-pass filtering, the received data of each antenna element is generated.

[0018] The specific method for narrowband processing of received data is as follows:

[0019] N-point windowing is applied to the received data of each antenna element to reduce spectrum leakage;

[0020] Perform an N-point FFT operation on the windowed data to obtain the narrowband processed received data.

[0021] The specific method for narrowband interference filtering is as follows:

[0022] Interference detection is performed on the frequency domain data after narrowband processing, and spectral components above the spectral horizontal plane are marked as narrowband interference.

[0023] Narrowband interference is filtered out by setting the spectral values ​​of all frequency domain data corresponding to narrowband interference to 0.

[0024] Each frequency point of all frequency domain data is cached separately.

[0025] The specific method for detecting interference and obtaining interference information at frequency point 1 is as follows:

[0026] Generate a covariance matrix based on the cached data of frequency point 1;

[0027] Perform eigenvalue decomposition on the covariance matrix;

[0028] DOA estimation is performed on the eigenvalue decomposition results;

[0029] Interference information is determined based on the DOA estimation results.

[0030] The specific steps for generating an anti-interference filter based on interference information are as follows:

[0031] Based on the interference information and antenna element feature information of frequency point 1, the mask information is calculated, and the primary anti-interference filter is defined using the mask information;

[0032] The particle swarm optimization algorithm is used to optimize and iterate the primary anti-interference filter to determine the final anti-interference filter, and the optimal filter value at each frequency point is calculated and obtained.

[0033] Interference suppression is performed on the frequency domain data of each frequency point based on the optimal filter value of each frequency point;

[0034] Obtain M-channel FFT data corresponding to each frequency point:

[0035] X n =[x(1),x(2),...,x(M)]',n=1,....,N

[0036] Using the optimal filter for X n Perform interference filtering:

[0037] Y n =w opt '·X n n=1,...,N

[0038] For Y n Perform IFFT operation to obtain the time-domain signal y(k) after interference suppression, and complete the interference suppression of the buffered data.

[0039] The primary anti-interference filter is specifically:

[0040]

[0041] In the formula, w opt : Optimize weights; k1, k2, k3: Weighting factors for each condition, the specific values ​​of which are related to the number of anti-interference antenna elements and the interference scenario; Indicates that the array passes through w opt Weighted in position Gain at point N; N: represents the number of interference nulls in the filter; Indicates the nth interference, i.e., the null position; Ω1: indicates the range of the non-null region; A: indicates the gain of the non-null region, which is a constant, usually set to 1; B n n=1...N: represents the nth disturbance, i.e., the null depth; C: represents the set design requirement ripple value; rip: represents the ripple value of the array after passing through w opt After weighting, the ripple coefficients of the spatial filter are obtained; where: k1+k2+k3=1.

[0042] The specific steps for optimizing and iterating the primary anti-interference filter using the particle swarm optimization algorithm are as follows:

[0043] Initialize the particle swarm, including the swarm size N and the position w of each particle. opt-i With velocity v i ;

[0044] Calculate the fitness value fit[i] for each particle;

[0045] Compare the fitness value fit[i] of each particle with its individual extreme value p. best (i), if fit[i] < p best (i), then fit[i] replaces p best (i);

[0046] Compare the fitness value fit[i] of each particle with the global optimum g. best If fit[i] < g best Then replace g with fit[i]. best ;

[0047] Update the position w of each particle opt-i With velocity v i And perform boundary processing;

[0048] Determine whether the updated data meets the termination condition for iterative update. If so, end the algorithm and output the optimization result; otherwise, recalculate the fitness value of each particle and perform iteration.

[0049] The specific termination condition for the iterative update is: the iterative update terminates when the number of iterations reaches the initially set maximum value.

[0050] Window selection for window addition: Chebyshev windows are adopted;

[0051] The interference information includes the interference azimuth angle, elevation angle, number of interferences, interference intensity, and the angle range of the non-zero depression zone.

[0052] The specific spectral horizontal plane is:

[0053] Under interference-free conditions, the average value of the spectral components at each frequency point after the received data of a single antenna element is processed by N-point windowing and FFT operation, which is the noise floor.

[0054] The advantages of this invention compared to the prior art are:

[0055] (1) The present invention provides an adaptive intelligent optimization zeroing anti-interference method based on interference detection, which innovatively uses objective function modeling and particle swarm algorithm to optimize the complex weights of multi-channel signals, realizes optimal digital beamforming, receives useful signals over the maximum range, and improves anti-interference capability. At the same time, before the optimal weights are calculated at each frequency point, a narrowband interference filtering function is added to ensure that the data entering the optimal weight statistics are all broadband interference, thus ensuring the consistency of interference information at each frequency point.

[0056] (2) This invention detects interference at frequency point 1 and obtains interference information. Each frequency point is optimized by using the interference information to optimize the optimal weight. This avoids complex mathematical calculations such as feature decomposition at each frequency point, reduces FPGA resources, and ensures the engineering feasibility of the solution. Attached Figure Description

[0057] Figure 1 A schematic diagram of the adaptive intelligent optimization zeroing anti-interference method based on interference detection provided for the invention;

[0058] Figure 2 Flowchart of the zero-adjustment anti-interference method provided for the invention;

[0059] Figure 3 A schematic diagram of the particle swarm optimization algorithm iteration process provided for the invention;

[0060] Figure 4 A schematic diagram of the adaptive zeroing anti-interference filter mask provided for the invention;

[0061] Figure 5 The adaptive zeroing anti-interference method provided for the invention has the following effect. Figure 2 Vito;

[0062] Figure 6 A three-dimensional rendering of the spatial filter generated by the original adaptive zeroing algorithm provided for the invention;

[0063] Figure 7 A three-dimensional rendering of the adaptive zeroing anti-interference method based on interference detection provided for the invention; Detailed Implementation

[0064] An adaptive intelligent optimization zeroing anti-interference method based on interference detection is proposed. This method suppresses various types of interference and minimizes FPGA resource usage, facilitating engineering implementation. The method and device based on interference detection offer advantages such as strong interference filtering capability, high satellite reception capacity, strong anti-interference ability, low computational resource requirements, and good engineering feasibility. Furthermore, it exhibits good interference suppression capabilities against various types of interference. The specific steps are as follows:

[0065] Acquire the received data from each antenna element;

[0066] Narrowband processing is performed on the received data;

[0067] Narrowband interference filtering is performed on the frequency domain data after narrowband processing.

[0068] Interference detection is performed on frequency point 1 in the data of each frequency point after interference filtering to obtain interference information;

[0069] Generate the final anti-interference filter based on the interference information;

[0070] Interference suppression is achieved by using a final anti-interference filter to filter the received data of each antenna element.

[0071] The specific method for obtaining the received data of each antenna element is as follows:

[0072] The received signals of each antenna element are acquired, down-converted to obtain intermediate frequency analog signals, and after AD sampling and low-pass filtering, the received data of each antenna element is generated.

[0073] The specific method for narrowband processing of received data is as follows:

[0074] N-point windowing is applied to the received data of each antenna element to reduce spectrum leakage;

[0075] Perform an N-point FFT operation on the windowed data to obtain the narrowband processed received data.

[0076] The specific method for narrowband interference filtering is as follows:

[0077] Interference detection is performed on the frequency domain data after narrowband processing, and spectral components above the spectral horizontal plane are marked as narrowband interference.

[0078] Narrowband interference is filtered out by setting the spectral values ​​of all frequency domain data corresponding to narrowband interference to 0.

[0079] Each frequency point of all frequency domain data is cached separately;

[0080] The specific method for detecting interference and obtaining interference information at frequency point 1 is as follows:

[0081] Generate a covariance matrix based on the cached data of frequency point 1;

[0082] Perform eigenvalue decomposition on the covariance matrix;

[0083] DOA estimation is performed on the eigenvalue decomposition results;

[0084] Based on the DOA estimation results, determine the interference information;

[0085] The specific steps for generating an anti-interference filter based on interference information are as follows:

[0086] Based on the interference information and antenna element feature information of frequency point 1, the mask information is calculated, and the primary anti-interference filter is defined using the mask information;

[0087] The particle swarm optimization algorithm is used to optimize and iterate the primary anti-interference filter to determine the final anti-interference filter, and the optimal filter value at each frequency point is calculated and obtained respectively.

[0088] Interference suppression is performed on the frequency domain data of each frequency point based on the optimal filter value of each frequency point;

[0089] Obtain M-channel FFT data corresponding to each frequency point:

[0090] X n =[x(1),x(2),...,x(M)]',n=1,....,N

[0091] Using the optimal filter for X n Perform interference filtering:

[0092] Y n =w opt '·X n n=1,...,N

[0093] For Y n Perform IFFT operation to obtain the time-domain signal y(k) after interference suppression, and complete the interference suppression of the buffered data.

[0094] The following description, in conjunction with the accompanying drawings and preferred embodiments, provides further details:

[0095] In the current embodiment, this application provides a technical flowchart of an adaptive intelligent optimization zeroing anti-interference method and device based on interference detection, as shown below. Figure 1 As shown, the method includes:

[0096] Step 101: Obtain the received data from each antenna channel;

[0097] Step 102: Perform narrowband processing on the signals of each channel;

[0098] Step 103: Perform narrowband interference filtering on the signals of each channel;

[0099] Step 104: Based on the data of each frequency point, perform interference detection on frequency point 1 to obtain interference information;

[0100] Step 105: Generate an anti-interference filter based on the interference information;

[0101] Step 106: Perform interference filtering on the received data to complete interference suppression.

[0102] In some embodiments, acquiring the received data from each antenna channel includes:

[0103] Acquire signals received by multiple antenna array elements;

[0104] Based on the signals received by the multi-antenna array elements, the signals are down-converted to intermediate frequency analog signals by a multi-channel RF module, and then sampled by AD, digitally down-converted, low-pass filtered, and tripled down-depressed to generate the received data for each antenna array element.

[0105] In some embodiments, the narrowband processing of each channel signal specifically includes:

[0106] Perform N-point windowing on the data from each channel to reduce spectrum leakage;

[0107] Perform an N-point FFT on the windowed data;

[0108] The N-point is typically between 256 and 1024.

[0109] In some embodiments, the narrowband interference filtering of each channel signal specifically involves:

[0110] Interference detection is performed on the N-point frequency domain data after each channel undergoes N-point FFT, and frequency points above the spectrum horizontal plane are marked as narrowband interference;

[0111] Narrowband interference is filtered out by setting all frequency values ​​to 0;

[0112] Buffer the data from N frequency points across multiple channels respectively;

[0113] In some embodiments, interference detection is performed on frequency point 1 based on the cached data of frequency point 1 to obtain interference information, specifically as follows:

[0114] The covariance matrix is ​​formed for the cached data at frequency point 1;

[0115] Perform eigenvalue decomposition on the resulting covariance matrix;

[0116] DOA estimation is performed using the results of eigenvalue decomposition.

[0117] Interference information is obtained through the results of eigenvalue decomposition and DOA estimation.

[0118] Covariance matrix formation: x represents snapshot data, which is an M×1 column vector, where M is the number of array elements and N is the number of array elements. snapshot Indicates the number of snapshots;

[0119] Eigenvalue decomposition: for the covariance matrix R xx Perform eigenvalue decomposition, R xx =VDV H D is a diagonal matrix composed of eigenvalues ​​arranged in descending order; V = [E j E n ] is the corresponding feature vector, E j E n These are the interference subspace and the noise subspace, respectively, composed of eigenvectors corresponding to the N larger eigenvalues ​​and the MN smaller eigenvalues. Here, N is the number of interferences, N is the number of eigenvalues ​​greater than the noise floor on the diagonal of matrix D, and M is the number of matrix elements. Based on the interference subspace E... j The number of interferences N is obtained njam With interference intensity J njam .

[0120] DOA estimation: The MUSIC algorithm is used for DOA estimation. Spatial spectral function:

[0121]

[0122] right Perform a spectral peak search to obtain the direction of interference.

[0123] Interference information obtained: Number of interferences N njam With interference intensity J njam Interference direction

[0124] In some embodiments, an anti-interference filter is generated based on the interference information, specifically as follows:

[0125] Based on the interference information at frequency point 1 and antenna characteristics, determine the adaptive nulling filter mask, such as... Figure 4 As shown, the gain in the non-zero trap region of the mask is 0dB, the transition region is not constrained, and the lead depth of the zero trap position is 10*log10(J / S)dB, where J / S is the interference-to-signal ratio.

[0126] The aforementioned adaptive zeroing fitness value is the core of this invention. Based on the mask information, the filter fitness value is defined as follows:

[0127]

[0128] in,

[0129] w opt : Optimize weights;

[0130] k1, k2, k3: weighting factors for each condition. The specific values ​​are related to the number of anti-interference antenna elements and the interference scenario.

[0131] Indicates that the array passes through w opt Weighted in position Gain at the location;

[0132] N: Represents the number of interferences (i.e. filter nulls);

[0133] This indicates the location of the nth disturbance (i.e., null).

[0134] Ω1: Represents the range of the non-zero trap region;

[0135] A: Represents the gain in the non-zero trap region, which is a constant and is generally set to 1;

[0136] B n n = 1...N: represents the depth of the nth disturbance (i.e., null trap);

[0137] C: Indicates the ripple value required by the design specifications;

[0138] rip: indicates that the array is routed via w opt The ripple coefficients of the spatial filter are obtained after weighting.

[0139] Where: k1+k2+k3=1.

[0140] The particle swarm optimization algorithm is used to iteratively optimize the fitness value, and the optimal filter value for each frequency point is calculated. Figure 3 As shown.

[0141] In some embodiments, the interference filtering of the received data to achieve interference suppression includes:

[0142] Using the obtained anti-interference filter w opt Filter the received data. Obtain the filtered Y n After IFFT, the time-domain signal y(t) with interference suppression is obtained.

[0143] In some embodiments, the spectral horizontal plane specifically refers to:

[0144] Under interference-free conditions, the average value of the spectral components at each frequency point after the received data from a single antenna element undergoes N-point windowing and FFT operation, i.e., the noise floor. For example... Figure 2 The diagram shown is a flowchart of an adaptive intelligent optimization zeroing anti-interference method and apparatus based on interference detection, provided in an exemplary embodiment.

[0145] Step 201: After windowing the data of each channel, perform an N-point FFT operation to form N-point FFT data for multiple channels, x' m (k), m=1...M;

[0146] Step 202: For each channel data x' m (k),m=1...M are subjected to narrowband filtering to obtain x m (k), m=1...M;

[0147] Step 203: Perform statistical analysis on the frequency domain data of each frequency point, and form a covariance matrix R for each frequency point relative to the received data. xx (k), k=1,....,N.

[0148] Step 204: Covariance matrix R of frequency point 1 xx (k), k=1 are subjected to eigenvalue decomposition to obtain the eigenvector matrix V and the corresponding eigenvalue matrix D, and the resulting subspace E n ;

[0149] Step 204: Obtain the resulting subspace E through eigenvalue decomposition. n The spectral function is obtained using the MUSIC algorithm. The interference angle was obtained through spectral peak search. Based on the eigenvalue magnitude and DOA estimation results, the number of nulls, null depth, and null location of the anti-interference filter are determined, and the parameters of the anti-interference filter are finally determined.

[0150] Step 205: After confirming the filter parameters, the particle swarm optimization algorithm is used to optimize the fitness value fit[i] to minimize fit[i] and obtain the optimal w. opt .

[0151] Step 206: Use the optimized w opt The received data x(k) is filtered to obtain the interference-filtered signal.

[0152] like Figure 3 The diagram shown is a flowchart of a particle swarm optimization algorithm provided in an embodiment of this application.

[0153] Step 301: Initialize the particle swarm, including the swarm size N and the position x of each particle. i With velocity v i ;

[0154] Step 302: Calculate the fitness value fit[i] for each particle;

[0155] Step 303: For each particle, use its fitness value fit[i] and individual extreme value p best (i) Comparison. If fit[i] < p best (i), then fit[i] replaces p best (i).

[0156] Step 304: For each particle, use its fitness value fit[i] and global extremum g. best Compare. If fit[i] < g best Then replace g with fit[i]. best .

[0157] Step 305: Iteratively update the particle velocity v i and position x i .

[0158] Step 306: Perform boundary processing.

[0159] Step 307: Determine the algorithm termination condition: If yes, end the algorithm and output the optimization result; otherwise, return to step 302.

[0160] like Figure 4 , Figure 5 The figure shown is a simulation diagram of the improved anti-interference algorithm of the present invention compared with the power inversion algorithm. Number of anti-interference antennas: 7, interference scenario: number of interferences: single interference, interference direction: elevation angle 10° azimuth angle 15°, interference intensity: 80dB. Figure 5 This is a two-dimensional spatial filter diagram at a 10° pitch angle. In the diagram, red represents the spatial filter generated by this invention, and blue represents the spatial filter generated by the power inversion algorithm; as shown... Figure 6 The image shows a 3D spatial filter diagram generated by the original power inversion algorithm. The lighter-colored areas indicate noticeable filter "dips" even in non-interference regions, and these "dips" contribute to the attenuation of useful satellite signals. Figure 7 The image shown is a three-dimensional spatial filter diagram generated by the algorithm of this invention. This filter is relative to... Figure 6 The power inversion algorithm filter, except for the null generated in the interference region, has no obvious "dimples" in other non-interference regions, compared to... Figure 6 There has been a significant improvement.

[0161] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

[0162] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. An adaptive intelligent optimization zeroing anti-interference method based on interference detection, characterized in that... include: Acquire the received data from each antenna element; Narrowband processing is performed on the received data; Narrowband interference filtering is performed on the frequency domain data after narrowband processing. Interference detection is performed on frequency point 1 in the data of each frequency point after interference filtering to obtain interference information; Generate the final anti-interference filter based on the interference information; The final anti-interference filter is used to perform interference filtering on the received data of each antenna element to achieve interference suppression. The specific method for obtaining the received data of each antenna element is as follows: The received signals of each antenna element are acquired, down-converted to obtain intermediate frequency analog signals, and after AD sampling and low-pass filtering, the received data of each antenna element is generated. The specific method for narrowband processing of received data is as follows: N-point windowing is applied to the received data of each antenna element to reduce spectrum leakage; Perform an N-point FFT operation on the windowed data to obtain the narrowband processed received data. The specific method for narrowband interference filtering is as follows: Interference detection is performed on the frequency domain data after narrowband processing, and spectral components above the spectral horizontal plane are marked as narrowband interference. Narrowband interference is filtered out by setting the spectral values ​​of all frequency domain data corresponding to narrowband interference to 0. Each frequency point of all frequency domain data is cached separately; The specific method for detecting interference and obtaining interference information at frequency point 1 is as follows: Generate a covariance matrix based on the cached data of frequency point 1; Perform eigenvalue decomposition on the covariance matrix; DOA estimation is performed on the eigenvalue decomposition results; Based on the DOA estimation results, determine the interference information; The specific steps for generating the final anti-interference filter based on the interference information are as follows: Based on the interference information and antenna element feature information of frequency point 1, the mask information is calculated, and the primary anti-interference filter is defined using the mask information; The particle swarm optimization algorithm is used to optimize and iterate the primary anti-interference filter to determine the final anti-interference filter, and the optimal filter value at each frequency point is calculated and obtained respectively. The primary anti-interference filter is specifically as follows: In the formula, : Optimize weights; : The weighting factor for each condition; the specific value setting is related to the number of anti-interference antenna array elements and the interference scenario. : Indicates that the array has passed through Weighted in position Gain at the location; : Indicates the number of interference nulls in the filter; : Indicates the nth disturbance, i.e., the null position; : Indicates the range of the non-zero trap region; : Represents the gain in the non-zero trap region, which is a constant and is generally set to 1; : Indicates the nth interference, i.e., the null depth; C: Indicates the set design requirement ripple value; rip: Indicates the array ripple depth. The ripple coefficients of the spatial filter obtained after weighting; where: .

2. The adaptive intelligent optimization zeroing anti-interference method based on interference detection according to claim 1, characterized in that: Interference suppression is performed on the frequency domain data of each frequency point based on the optimal filter value of each frequency point; Obtain the M-channel FFT data corresponding to each frequency point: Using the optimal filter pair Perform interference filtering: right Perform IFFT operations to obtain the time-domain signal after interference suppression. This completes the suppression of interference with cached data.

3. The adaptive intelligent optimization zeroing anti-interference method based on interference detection according to claim 2, characterized in that: The specific steps for optimizing and iterating the primary anti-interference filter using the particle swarm optimization algorithm are as follows: Initialize the particle swarm, including the swarm size. The position of each particle With speed ; Calculate the fitness value for each particle. ; Compare the fitness values ​​of each particle. and individual extreme values ,like ,but Replace ; Compare the fitness values ​​of each particle. and global extrema ,like Then use replace ; Update the position of each particle With speed And perform boundary processing; Determine whether the updated data meets the termination condition for iterative update. If so, end the algorithm and output the optimization result; otherwise, recalculate the fitness value of each particle and perform iteration.

4. The adaptive intelligent optimization zeroing anti-interference method based on interference detection according to claim 3, characterized in that: The specific termination condition for the iterative update is: the iterative update terminates when the number of iterations reaches the initially set maximum value. Window selection for window addition: Chebyshev windows are adopted; The interference information includes the interference azimuth angle, elevation angle, number of interferences, interference intensity, and the angle range of the non-zero depression zone.

5. The adaptive intelligent optimization zeroing anti-interference method based on interference detection according to claim 4, characterized in that: The aforementioned spectral horizontal plane is specifically: Under interference-free conditions, the average value of the spectral components at each frequency point after the received data of a single antenna element is processed by N-point windowing and FFT operation, which is the noise floor.

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