Compression-deception mixed interference detection and suppression method based on co-prime array

Abstract

The invention discloses a co-prime array-based suppression-deception mixed interference detection and suppression method, which comprises the following steps of: firstly, calculating a data covariance matrix after noise weakening based on the periodic stationary characteristic of a navigation signal, then, carrying out vectorization and redundancy elimination processing on the covariance matrix, and further constructing a virtual array equivalent receiving signal model; the method comprises the following steps of: selecting a continuous part of the matrix to carry out spatial solution smoothing operation to recover the rank of the matrix, and then taking the discrete condition of a characteristic value of the matrix as a deception jamming detection quantity, and finishing the detection and suppression of the suppressing jamming and the deception jamming in cooperation with a multiple signal classification (MUSIC) algorithm and a signal subspace projection algorithm. The method can give consideration to the detection and suppression of suppressing interference and deception interference in a suppressing-deception mixed interference scene, and has the advantages of high interference detection rate and high-precision DOA estimation.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of navigation, and particularly relates to a method for detecting and suppressing jamming based on a mixed jamming of suppression and deception. BACKGROUND

[0002] Global navigation satellite systems can provide accurate timing, positioning and navigation services, and have been widely used in various fields of civil and military. However, the satellite signal power reaching the ground is very weak and is easily affected by various interferences, especially suppression interference and deception interference. With the diversification of interference means, suppression interference and deception interference may coexist in complex environments, and the security of satellite navigation systems has been greatly challenged. Antenna arrays are widely used in the detection and suppression of interference, but in the mixed jamming scene of suppression and deception, the current detection and suppression methods for a single type of interference are difficult to accurately detect and distinguish the real signal, suppression interference and deception interference, and have the problems of limited array degrees of freedom, low interference detection accuracy and poor anti-interference performance.

[0003] The existing array-based interference detection and suppression method is as follows: In practical applications, the power inversion (PI) algorithm is widely used, which can effectively suppress the suppression interference by forming a deep null in the direction of the interference signal. However, this method cannot detect and suppress deception interference. When the power of the deception signal is lower than that of the real signal, this algorithm may even form a null in the direction of the real signal to suppress the real signal, resulting in low interference detection accuracy and poor anti-interference performance. In addition, the anti-interference performance of this method is directly related to the number of array elements, and the array degrees of freedom are limited in the mixed jamming of suppression and deception. Therefore, this method cannot be directly applied to the detection and suppression of interference in the mixed jamming of suppression and deception. SUMMARY

[0004] In order to overcome the shortcomings of the prior art, the present application provides a method for detecting and suppressing mixed jamming of suppression and deception based on a coprime array. First, the data covariance matrix after weakening the noise is calculated based on the periodic stationary characteristics of the navigation signal, then the covariance matrix is vectorized and de-redundant, and then a virtual array equivalent received signal model is constructed, the rank of the matrix is recovered by selecting the continuous part for spatial de-smoothing operation, and then the eigenvalue dispersion is used as a deception interference detection quantity, and the multiple signal classification (MUSIC) and signal subspace projection algorithm are used to complete the detection and suppression of suppression interference and deception interference. The present application can detect and suppress suppression interference and deception interference in the mixed jamming scene of suppression and deception, and has the advantages of high interference detection rate and high precision DOA estimation.

[0005] The technical scheme adopted by the present application to solve its technical problems is as follows: Step 1: Constructing a signal receiving model based on a coprime array; Step 2: Establishing an equivalent received signal of a virtual array element; Step 3: Using the equivalent signal receiving model corresponding to the continuous virtual array element to detect and suppress the suppressing interference; Step 4: Detecting and suppressing the suppressing interference; Step 5: Detecting and suppressing the deceptive interference.

[0006] Preferably, the step 1 is specifically as follows: The coprime array is composed of two uniform linear arrays with different element numbers and spacings, which are named as subarray 1 and subarray 2; wherein the element spacing of the subarray 1 is Nd , the number is 2 M , the element spacing of the subarray 2 is Md , and the number is N ; the two subarrays only overlap the first element, the combined array has 2 M+N -1 elements, and there are 2 MN +2 M- 1 continuous virtual array elements, d the value of which is half the wavelength of the satellite signal; it is assumed that there are L real satellite signals, Q deceptive interferences and K suppressing interferences in the space domain; the array receiving expression is as follows: (1) In the formula, , and respectively represent the sampling data of the envelope of the nT th real satellite signal, the l th deceptive interference signal and the q th suppressing interference signal at the k th moment; , and respectively represent the steering vector of the l th real satellite signal with the direction of , the steering vector of the q th deceptive signal with the direction of , and the steering vector of the k th suppressing interference with the direction of ; represents the spatial white noise subject to Gaussian distribution; , and respectively represent (2) (3) (4) wherein, denotes the transpose, denotes the signal wavelength; denotes the distance of the i th antenna physical element relative to the reference element, .

[0007] Preferably, the step 2 is specifically: delaying the received signal by one C / A code period through a delay tap , obtaining as the reference signal: (5) wherein, denotes one C / A code period of the navigation signal; denoising the data covariance matrix denotes: (6) wherein, N s denotes the number of snapshots used, denotes the conjugate transpose; , and denote the cyclic autocorrelation function of the l th real satellite signal, the q th spoofing jamming signal and the k th suppressing jamming signal respectively, and are respectively denoted as: (7) (8) (9) wherein, denotes taking the conjugate; vectorizing the denoised data covariance matrix to obtain the co-prime array virtual domain equivalent signal receiving model : (10) wherein, denotes the vectorization operation, which refers to rearranging a matrix to connect each column of the matrix to form a new column vector; removing the duplicate rows in , at which time the dimension of z becomes corresponding to the selected z-th to z+1-th row of the z-th virtual array element ; and selecting a continuous virtual array element part, i.e. MN - M +1 to MN+M -1, totally 2 MN+ 2 M- 1 virtual array element, corresponding to the selected z-th to z+1-th row of the z-th virtual array element ; and selecting a continuous virtual array element part, i.e. - .

[0008] Preferably, the step 3 is specifically: Let the sliding window length be MN + M , totally MN + M virtual sub-arrays, then the data received by the z-th virtual sub-array is represented as: j (11) The covariance matrix of the z-th virtual sub-array j can be represented as: (12) Summing and averaging all + MN covariance matrices, the rank-recovered data covariance matrix M is obtained: (13).

[0009] Preferably, the step 4 is specifically: Perform eigenvalue decomposition on the rank-recovered data covariance matrix : (14) wherein is the z-th largest eigenvalue, is the eigenvector corresponding to the z-th largest eigenvalue; i is a diagonal matrix with the z largest eigenvalues as the main diagonal, is a signal subspace composed of the eigenvectors corresponding to the z largest eigenvalues; i is a diagonal matrix with the z largest eigenvalues as the main diagonal, is a noise subspace composed of the eigenvectors corresponding to the z largest eigenvalues; and are represented as: ​​​​​​​​​​ (15) (16) Spatial spectrum Represented as: (17) In the formula, It is the guiding vector of the covariance matrix dimension after rank restoration on the virtual matrix elements, expressed as: (18) In the formula, Indicates the first i The position of each virtual array element relative to the reference array element , Spatial spectrum In the angle range Perform a peak search, when there is K When suppressing interference, the spatial spectrum Only exist K A maximum value, K The direction of the maximum value This refers to suppressing the incoming interference. The DOA estimation results of the suppressed interference are used to construct a set of guiding vectors based on physical array elements as a constraint matrix. (19) calculate The orthogonal complement space is used as the weight for resisting suppression interference: (20) The signal after anti-suppression interference processing Represented as: (twenty one).

[0010] Preferably, step 5 specifically comprises: After the anti-suppression interference processing is completed, repeat equations (10)-(16) to obtain the data covariance matrix after rank restoration. And perform feature decomposition: (twenty two) in, for No. i Large eigenvalues For the first i The eigenvectors corresponding to each eigenvalue; based on If the signal subspace is partitioned, then The main diagonal is diagonal matrix, It is an eigenvalue The signal subspace formed by the corresponding feature vectors; is a diagonal matrix with as the main diagonal, is the eigenvalue corresponding eigenvector constitute the noise subspace, and are expressed as: (23) (24) Calculate the error sum of squares of the first four eigenvalues as the deception jamming detection quantity : MN (25) In the formula, denotes the average value of the set of eigenvalues; Set up a deception jamming detection threshold , when the deception jamming measurement SSE is greater than the threshold, the spatial spectrum maximum value corresponds to the direction of arrival of the deception jamming, that is, the deception jamming detection strategy is: (26) When there is deception jamming, the detection and suppression of deception jamming are carried out; the spatial spectrum is expressed as: (27) Use the spatial spectrum to perform spectral peak search in the angle interval , and the angle where the maximum spectral peak is located is the direction of arrival of the deception jamming , update the steering vector matrix based on the physical array element, expressed as: (28) Calculate the orthogonal complement space of as the anti-suppression-deception jamming weight: (29) Define , then the signal after anti-suppression-deception jamming processing is expressed as: (30).

[0011] An electronic device, comprising: a processor and a memory; the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory, so that the electronic device executes the above interference detection and suppression method.

[0012] ​A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the interference detection and suppression method.

[0013] A chip comprising a processor for calling and running a computer program from a memory, so that a device installed with the chip implements the interference detection and suppression method.

[0014] A computer program product comprising a computer storage medium storing a computer program comprising instructions executable by at least one processor, which, when executed by the at least one processor, implements the interference detection and suppression method.

[0015] The beneficial effects of the present application are as follows: Compared with the currently widely used PI algorithm, the method of the present application can detect and suppress both the suppression jamming and the deception jamming in the suppression-deception mixed jamming scene, and has the advantages of high jamming detection rate and high-precision DOA estimation. Meanwhile, the use of the co-prime array effectively improves the array degree of freedom, effectively ensuring the safety and reliability of the satellite navigation system in the suppression-deception complex jamming scene. BRIEF DESCRIPTION OF DRAWINGS

[0016] SSE is a flowchart of the method of the present application.

[0017] Figure 1 is a comparison diagram of the distribution of the physical array elements and the virtual array elements of the co-prime array used in the present application (2M=6, N=7).

[0018] Figure 2 is the spatial spectrum obtained when the method of the present application is used to estimate the DOA of the suppression jamming.

[0019] Figure 3 is the anti-jamming array beam pattern obtained after the implementation of the present application.

[0020] Figure 5 is the receiver capture result of the PRN1 satellite signal before and after the application of the present application, Figure 5(a) is before the anti-jamming processing, and Figure 5(b) is after the anti-suppression-deception mixed jamming processing. DETAILED DESCRIPTION

[0021] The present application will be further described below in conjunction with the drawings and examples.

[0022] In scenarios involving mixed suppression and deception interference, current methods for detecting and suppressing single-type interference struggle to accurately detect and distinguish between real signals, suppressed interference, and deception interference. These methods generally suffer from limited array degrees of freedom, low interference detection accuracy, and poor anti-interference performance. To address these issues, this invention proposes a method for detecting and suppressing mixed suppression and deception interference based on coprime arrays, thereby improving the reliability and security of satellite navigation systems under complex suppression and deception interference environments.

[0023] This invention provides a method for detecting and suppressing hybrid suppression-spoofing interference based on coprime arrays. First, the data covariance matrix after noise reduction is calculated based on the periodic stationary characteristics of navigation signals. Then, the covariance matrix is ​​vectorized and redundancy-removed to construct an equivalent received signal model of a virtual array. The continuous portion of this model is selected for spatial desmoothing to restore the matrix's rank. Subsequently, the discreteness of its eigenvalues ​​is used as the detection metric for spoofing interference. Combined with a Multiple Signal Classification (MUSIC) algorithm and a signal subspace projection algorithm, the detection and suppression of both suppression and spoofing interference are achieved. Specifically, this invention includes the following steps: Step 1: Construct a signal reception model based on a coprime array. The coprime array consists of two uniform linear arrays with different numbers and spacings of elements. The element spacing of subarray 1 is... Figure 4 The quantity is 2 M The element spacing of subarray 2 is Nd The quantity is N。 The two subarrays overlap only in their first element, and the combined array has a total of 2 Md -1 array element, totaling 2 M+N +2 M- One continuous virtual array element, d The value is taken as half the wavelength of the satellite signal. It is assumed that there exists [satellite signal] in the airspace. L A real satellite signal, Q A deception and interference and K One suppression interference. The array receive expression is: (1) In the formula, , as well as They represent in MN Time of the first l The first real satellite signal, the first q The first deceptive interference signal and the first k Sampling data of the envelope of the suppression interference signal. , as well as They represent the first l One direction is The steering vector of the real satellite signal, the first q One direction is The steering vector of the deception signal and the first k One direction is The guiding vector for suppressing interference. This represents spatial white noise that follows a Gaussian distribution. , as well as They can be represented as follows: (2) (3) (4) In the formula, This represents transposition. Indicates the signal wavelength; Indicates the first i The distance between each antenna physical element and the reference element. .

[0024] Step 2: Establish the equivalent received signal for the virtual array elements. Delay the received signal by one C / A code cycle using a time delay tap. ,get As a reference signal: (5) In the formula, This represents one C / A code cycle of the navigation signal.

[0025] Denoising data covariance matrix Represented as: (6) In the formula, N s This represents the number of snapshots used. This represents the conjugate transpose. , as well as Representing the first l The first real satellite signal, the first q The first deceptive interference signal and the first k The cyclic autocorrelation functions of the suppressed interference signals can be expressed as follows: (7) (8) (9) In the formula, The term represents conjugate.

[0026] Denoising data covariance matrix Vectorization yields an equivalent signal reception model for a coprime array virtual domain. : (10) In the formula, This represents a vectorization operation, which involves rearranging a matrix and concatenating its columns to form a new column vector.

[0027] Remove The repeated rows in the text, at this point the dimension of z becomes The corresponding virtual domain has a total of There are several virtual array elements. These virtual array elements are not continuously distributed. A portion of the virtual array elements is selected as continuous, i.e., - nT - M +1 to MN -1 out of 2 MN+M 2 M- One virtual array element corresponds to the selection of the z-th element. Arriving at the Okay, get .

[0028] Step 3: Utilize the equivalent signal receiving model corresponding to continuous virtual array elements To detect and suppress interference. Utilizing... The calculated covariance matrix has a rank of 1, making it impossible to locate the interference source. Spatial smoothing techniques are used for decoherence operations to restore the rank. Let the sliding window length be... MN+ + M There are a total of MN + M The nth virtual subarray, then the nth j Each virtual subarray receives data. It can be represented as: (11) No. j The covariance matrix of each virtual subarray It can be represented as: (12) All MN + M Summing and averaging the covariance matrices yields the rank-restored data covariance matrix. : (13) Step 4: Detect and suppress interference. The covariance matrix of the data after rank recovery... Perform eigenvalue decomposition: (14) in, for The i large eigenvalue, is the i eigenvalue corresponding to the eigenvector. When there is a jammer, there are obviously K obvious large eigenvalues, and the signal subspace and the noise subspace are divided according to the number of large eigenvalues. is a diagonal matrix with as the main diagonal, is a signal subspace composed of eigenvectors corresponding to eigenvalues . is a diagonal matrix with as the main diagonal, is a noise subspace composed of eigenvectors corresponding to eigenvalues . and are respectively expressed as: (15) (16) The spatial spectrum can be expressed as: (17) In the formula, is the steering vector on the virtual array element according to the dimension of the covariance matrix after rank restoration, and is expressed as: (18) In the formula, represents the position of the i th virtual array element relative to the reference array element, , . The spatial spectrum performs spectral peak search in the angle interval , when there are K jammers, the spatial spectrum only has K maximum values, K the directions of the maximum values are the directions of the jammers. The steering vector group based on the physical array elements is constructed as a constraint matrix according to the jammer DOA estimation results: (19) The orthogonal complement space of is calculated as the anti-jamming weight: (20) Then the signal after anti-jammer processing is expressed as: (21) Step 5: Detect and suppress deceptive interference. After the anti-suppression interference processing is completed, repeat equations (10)-(16) to obtain the data covariance matrix after restoring the rank. And perform feature decomposition: (twenty two) in, for No. i Large eigenvalues For the first i The eigenvectors corresponding to the eigenvalues. Since the deceptive signal originates from the same direction, this spatial characteristic significantly affects the distribution of the first four eigenvalues. Based on this characteristic, the signal subspace is partitioned as follows: The main diagonal is diagonal matrix, It is an eigenvalue The signal subspace formed by the corresponding eigenvectors. The main diagonal is diagonal matrix, It is an eigenvalue The noise subspace formed by the corresponding feature vectors. and They are represented as follows: (twenty three) (twenty four) calculate The sum of squared errors of the first four eigenvalues ​​is used as the detection quantity for deception interference. MN : (25) This represents the average value of the set of feature values, and a threshold for detecting deception and interference is set. When deception interferes with measurement MN When the value exceeds this threshold, the maximum value of the spatial spectrum corresponds to the direction. The deception interference detection strategy is as follows: (26) When deceptive interference exists, it is detected and suppressed. Spatial spectrum. Represented as (27) Using spatial spectrum In the angle range Perform a spectral peak search; the angle at which the largest spectral peak is located indicates the direction of the deception interference. Update the steering vector matrix based on the physical array elements, expressed as: (28) calculate The orthogonal complement space is used as the anti-suppression-deception interference weight: (29) definition The signal after anti-suppression-spoofing interference processing Represented as: (30) Example: This invention proposes a method for detecting and suppressing hybrid interference based on coprime arrays, specifically a suppression-spoofing method. It assumes that the center frequency of both the satellite signal and the spoofing interference carrier is 1575.42MHz, which is down-converted to 46.5MHz. The satellite signal wavelength... , Using 2 M =6, N A coprime array with a density of 7 is used for signal reception, and subarray 1 has 2 M =6 array elements, with a spacing of... Subarray 2 has N =7 array elements, with a spacing of... A comparison diagram of the physical and virtual element distributions of a coprime array is shown below. SSE As shown. Assume that there exists in the spatial domain. The satellite signals with asterisks PRN1 to PRN6, their origin... ;exist The interference suppression consists of one spot-frequency interference with an interference-to-signal ratio of 80 dB and one broadband interference with an interference-to-signal ratio of 75 dB, originating from... ; A forwarding deception interference to... All angles were -20°, satellite signal power was uniformly set to -158dBW, and signal-to-noise ratio was set to -20dB. This example used a total of 4092 snapshots. Assuming the ratio of spoofing interference power to satellite signal power is 3dB, anti-suppression-spoofing hybrid interference processing was performed according to the flowchart of this invention.

[0029] Step 1: Establish a coprime array-based system SSE Sampling time signal reception model: (1) in, , , as well as They can be represented as follows: (2) (3) (4) where, denotes the distance of the i th physical antenna element relative to the reference antenna element.

[0030] Step two: Establish the virtual element equivalent received signal. The received signal is delayed 1 ms as the reference signal : (5) The de-noised data covariance matrix is denoted as: (6) where, , and represent the cyclic autocorrelation functions of the l th real satellite signal, the q th deceptive jamming signal and the k th suppressive jamming signal, respectively, which can be denoted as: (7) (8) (9) The data covariance matrix is vectorized, and the equivalent signal receiving model z of the coprime array virtual domain is denoted as: (10) The dimension of z is , and the repeated rows in z are removed, at which time the dimension of z is . The corresponding part of 47 continuous virtual elements is selected, that is, the 7th to 53rd rows of z, to obtain .

[0031] Step three: The spatial smoothing technique is adopted to perform the decorrelation operation to restore the rank. The sliding window length is 24, and there are 24 virtual subarrays. For the j th virtual subarray, its received data can be denoted as: (11) For the j th virtual subarray, its covariance matrix can be denoted as: (12) All 24 covariance matrices are summed and averaged to obtain the data covariance matrix after rank restoration, which is denoted as: (13) Step 4: Detect and suppress interference. Utilize the rank-recovered data covariance matrix... Perform eigenvalue decomposition: (14) The first four eigenvalues They are respectively If two significantly large eigenvalues ​​correspond to two suppression interferences, then... The main diagonal is , diagonal matrix, It is the signal subspace formed by the eigenvectors corresponding to the first two eigenvalues. The main diagonal is diagonal matrix, It is an eigenvalue The noise subspace formed by the corresponding eigenvectors, and They are represented as follows: (15) (16) Then the MUSIC spatial spectrum It can be represented as: (17) Spatial spectrum like Figure 2 As shown, it was observed that the spatial spectrum has only two peaks at the 5° and 30° directions. It was determined that the corresponding 5° and 30° directions are suppression interference. Based on the physical array elements, a steering vector matrix was constructed: (18) calculate The orthogonal complement space is used as the weight for resisting suppression interference: (19) The signal after anti-suppression interference processing is represented as follows: (20) Step 5: Detect and suppress deception interference. Repeat equations (40)-(43) to obtain the received data covariance matrix after recovering the rank against suppression interference. If we perform eigenvalue decomposition, we have: (twenty one) The first four eigenvalues They are respectively Its mean It is 8.075×10-30 Calculate the amount of deception interference detection nT for: (twenty two) Set a threshold for deception interference detection. 10 -57 Deceptive interference detection was detected. This is used to deceive and interfere with DOA estimation. noise subspace Represented as: (twenty three) Depend on The obtained spatial spectrum for: (twenty four) For spatial spectrum A spectral peak search was performed, and the direction of origin corresponding to the largest spectral peak was identified as the direction of deception interference. Seeking to deceive and interfere with For -20.1°, update the constraint matrix: (25) Pick Orthogonal complement space as a weight for resisting suppression-deception hybrid interference : (26) definition The signal after anti-suppression-spoofing interference processing Represented as: (27) A comparison of the beam pattern generated by the method of this invention with the beam pattern generated by the PI method in the same scene is shown below. Figure 3 SSE Figure 4 As shown, the PI method cannot form nulls upwards from spoofing interference, meaning it cannot detect or suppress spoofing interference. In contrast, the method of this invention can simultaneously form nulls upwards from both suppression and spoofing interference. The signal after suppressing the mixed suppression-spoofing interference is captured. Figure 5 shows a comparison of PRN1 satellite signal capture results before and after implementing the method of this invention. It can be seen that the method of this invention can successfully ensure the normal operation of the satellite navigation system under complex suppression-spoofing interference scenarios.

Claims

1. A method of jammer detection and suppression based on a mixed pressing-deception jamming, characterized in that, The method comprises the following steps: Step 1: constructing a signal receiving model based on a coprime array; Step 2: establishing an equivalent received signal of a virtual array element; Step 3: Using the equivalent signal reception model of the continuous virtual array element Detection and suppression of jamming is performed; Step 4: detecting and suppressing a suppressing jamming; Step 5: detecting and suppressing a deceptive jamming.

2. The method of claim 1, wherein, The step 1 is specifically: The coprime array is composed of two uniform linear arrays with different element number and interval, named as subarray 1 and subarray 2; wherein, the element interval of subarray 1 is Nd , the number is 2 M , the element interval of subarray 2 is Md , the number is N ; the two subarrays only overlap the first element, the total number of elements of the synthesized array is 2 M+N -1, and there are 2 MN +2 M- 1 continuous virtual elements, d the value is half wavelength of satellite signal; it is assumed that there are L real satellite signals, 2 Q deception jamming and 2 K suppression jamming in the space domain; the array receiving expression is: （1） wherein, , and respectively represent the sampling data of the first nT th real satellite signal, the first l th spoofing jamming signal and the first q th suppressing jamming signal envelope at the time moment k ; , and respectively represent the steering vector of the first l th real satellite signal with the direction , the steering vector of the first q th spoofing signal with the direction and the steering vector of the first k th suppressing jamming with the direction ; represents the spatial white noise subject to Gaussian distribution; , and respectively represent （2） （3） （4） wherein represents a transpose, represents a signal wavelength; represents the distance of the i antenna physical array element relative to the reference array element, .

3. The method of claim 2, wherein, The step 2 is specifically: The received signal is delayed by one C / A code period by a delay tap , obtaining as a reference signal: （5） wherein denotes one C / A code period of the navigation signal; Denoised data covariance matrix is represented as: （6） wherein N s represents the number of snapshots used, represents the conjugate transpose; , and represent the cyclic autocorrelation function of the l th real satellite signal, the q th deceptive jamming signal and the k th barrage jamming signal, respectively, and are given by （7） （8） （9） In the formulae, represents a group selected from the group consisting of a conjugate; Denoising data covariance matrix Vectorization yields a coprime array virtual domain equivalent signal reception model : （10） In the formula, represents a vectorization operation, which means to rearrange a matrix and connect each column of the matrix to form a new column vector. remove duplicate rows in z, when the dimension of z becomes , corresponding to the co- virtual elements; select a part of the continuous virtual elements, i.e. MN - M +1 to MN+M -1, total 2 MN+ 2 M- 1 virtual element, corresponding to the selection of the th row to the th row in z, to get .

4. The method of claim 3, wherein, The step 3 is specifically: Let the sliding window length be MN M , there are MN M virtual sub-arrays, the first j virtual sub-array receives data represented as:​​ （11） The first j covariance matrix of the virtual subarray may be expressed as: （12） Sum all MN + M Covariance matrices and average to get the rank-restored data covariance matrix : （13）。 5. The method of claim 4, wherein, The step 4 is specifically: the rank-restored data covariance matrix perform eigen decomposition: （14） wherein, is the i largest eigenvalue, is i the eigenvector corresponding to the largest eigenvalue; is a diagonal matrix with the main diagonal being the eigenvectors corresponding to the eigenvalues ; is a diagonal matrix with the main diagonal being the eigenvectors corresponding to the eigenvalues ; and are respectively: （15） （16） spatial spectrum is represented as: （17） wherein is the steering vector of the dimension of the covariance matrix after rank restoration on the virtual array element, denoted as: （18） In the formula, represents the position of the i th virtual array element relative to the reference array element, , ; spatial spectrum Peak searching is performed in the angle interval When there are K restrained interferences, the spatial spectrum only has K maximum values, K The direction of the maximum value is the direction of the restrained interference, and the DOA estimation result of the restrained interference is used to construct a group of steering vectors based on physical array elements as a constraint matrix: （19） Computing the orthogonal complement space of as the anti-suppression interference weight: （20） then the signal after the anti-interference processing is expressed as: （21）。 6. The method of claim 5, wherein, The step 5 is specifically: After the anti-interference processing is completed, repeat (10)-(16) to obtain the data covariance matrix after the rank is recovered and perform eigen decomposition: （22） wherein, is the i largest eigenvalue, is i the eigenvector corresponding to the eigenvalue is the signal subspace, is a diagonal matrix with the eigenvectors corresponding to the eigenvalues as its columns; is the noise subspace, is a diagonal matrix with the eigenvectors corresponding to the eigenvalues as its columns, and are given by: （23） （24） Computations The first four eigenvalue error sum of squares as the spoofing jammer detection quantity SSE : （25） wherein denotes the average of the set of eigenvalues; Setting a spoofing jammer detection threshold When a spoofing jammer measurement SSE is greater than the threshold, the spatial spectrum maximum corresponds to a direction of arrival of the spoofing jammer, i.e. the spoofing jammer detection strategy is: （26） When there is a deceptive jamming, detection and suppression of the deceptive jamming is performed; spatial spectrum is represented as: （27） Utilizing spatial spectrum In the angle interval Performing spectrum peak search, the angle where the maximum spectrum peak is located is the direction of the deceptive jamming Updating the steering vector matrix based on physical array elements, denoted as: （28） Computing the orthogonal complement space of as anti-suppression-deception interference weight: （29） Definitions the signal after the compression-deception jamming interference processing is represented as: （30）。 7. An electronic device, comprising: The method comprises: A processor and a memory; The memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory, so that the electronic device executes the method as claimed in any one of claims 1 to 6.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method as claimed in any one of claims 1 to 6.

9. A chip, characterized by The method comprises: A processor is used for calling and running a computer program from a memory, so that a device installed with the chip executes the method as claimed in any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer program product comprises a computer storage medium, the computer storage medium stores a computer program, and the computer program comprises instructions executable by at least one processor, and when the instructions are executed by the at least one processor, the method as claimed in any one of claims 1 to 6 is implemented.

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