A method to combat dense false targets based on cumulative sampling

Abstract

The present invention discloses a method for resisting dense false targets based on sample cumulative sampling, comprising: constructing a first covariance matrix based on echo data, and after eigendecomposition, arranging the first eigenvalue and the first eigenvector; determining the first eigenvector with a preset desired target guidance vector v based on the maximum correlation coefficient; s0 The first eigenvector with the strongest correlation and its position ρ in the arrangement result; comparing the maximum correlation coefficient with a preset threshold η to make a target judgment, and when a target exists, selecting data greater than the sampling threshold from the echo data, and further obtaining sampled data through cumulative sampling; constructing a second covariance matrix based on the sampled data and performing eigendecomposition to obtain a second eigenvalue and a second eigenvector; calculating the sum of the correlation coefficients between at least part of the second eigenvectors and a preset expected target guidance vector based on ρ, and determining the range gate where the target is located when the sum of the correlation coefficients meets the preset conditions, thereby achieving main lobe deception interference that suppresses dense false targets.

Description

Technical Field

[0001] The invention belongs to the technical field of radar signal processing, and in particular relates to a method for resisting dense false targets based on sample accumulation sampling. Background Art

[0002] Interference suppression and target location have important applications in both military and civilian fields. The echo signal received by a radar typically contains the angle and distance information of the target signal. However, due to deceptive jamming and other factors, the real target is often obscured by a false target, making it impossible to directly obtain target information. Therefore, addressing the problem of mainlobe deceptive jamming is crucial.

[0003] Mainlobe deceptive interference suppression can be achieved by leveraging the range-angle coupling characteristics of the beam to identify true and false targets within the transmit and receive dimensions. Furthermore, blind source separation algorithms can be used to separate range deceptive interference and targets in different channels. Transmission uses QPC (quadratic phase code), which can be decoded and compensated for main value distance to distinguish between interference and targets. Mainlobe deceptive interference can be suppressed using space-time dual-domain algorithms or algorithms based on oblique projection.

[0004] However, the above methods are all based on ideal sampling and do not consider the problem when dense false target interference samples contain real targets, resulting in a sharp decline in the main lobe interference suppression performance. Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a method for resisting dense false targets based on sample accumulation sampling. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0006] The present invention provides a method for resisting dense false targets based on cumulative sampling of samples, comprising:

[0007] Constructing a first covariance matrix based on the received echo data, and performing eigendecomposition on the first covariance matrix, and arranging the obtained first eigenvalues ​​and their corresponding first eigenvectors;

[0008] Calculating the correlation coefficient between each first eigenvector and the preset expected target-oriented vector, and determining the first eigenvector with the strongest correlation with the preset expected target-oriented vector and its position in the arrangement result based on the maximum correlation coefficient;

[0009] Target determination is performed by comparing the maximum correlation coefficient with a preset threshold η, and when a target exists, the echo data with an echo greater than the sampling threshold is selected. data;

[0010] By The data is cumulatively sampled to obtain sampling data;

[0011] After constructing a second covariance matrix according to the sampled data, performing eigendecomposition on the second covariance matrix to obtain a second eigenvalue and a corresponding second eigenvector;

[0012] Based on the position of the first eigenvector with the strongest correlation with the preset expected target steering vector in the arrangement result, the sum of the correlation coefficients between at least part of the second eigenvectors and the preset expected target steering vector is calculated, and when the sum of the correlation coefficients meets a preset condition, the range gate where the target is located is determined.

[0013] In one embodiment of the present invention, the step of constructing a first covariance matrix based on the received echo data, performing eigendecomposition on the first covariance matrix, and arranging the obtained first eigenvalues ​​and their corresponding first eigenvectors includes:

[0014] Construct the first covariance matrix based on the received echo data:

[0015] R x =E[xx H ];

[0016] Wherein, x represents the echo data, H represents the conjugate transpose, and E represents the expectation;

[0017] Perform eigendecomposition on the first covariance matrix:

[0018]

[0019] Where N represents the dimension of the received data, λ k 、u k Respectively represent the first covariance matrix R x The kth first eigenvalue and its corresponding first eigenvector obtained after eigendecomposition;

[0020] Arrange the first eigenvalues ​​and their corresponding first eigenvectors in descending order according to the magnitude of the first eigenvalues.

[0021] In one embodiment of the present invention, the steps of calculating the correlation coefficient between each first eigenvector and a preset expected target-oriented vector, and determining the first eigenvector with the strongest correlation with the preset expected target-oriented vector and its position in the arrangement result based on the maximum correlation coefficient include:

[0022] Calculate the first eigenvectors and the preset desired target orientation vector v s0 The correlation coefficient between:

[0023]

[0024] According to the maximum correlation coefficient, determine the first eigenvector u that has the strongest correlation with the preset expected target orientation vector l and its position ρ in the arrangement result.

[0025] In one embodiment of the present invention, the step of performing target determination by comparing the maximum correlation coefficient with a preset threshold η and selecting data greater than a sampling threshold θ from the echo data when a target exists includes:

[0026] Target decision is made by comparing the maximum correlation coefficient with a preset threshold η:

[0027]

[0028] Where H1 represents the maximum correlation coefficient β max When it is greater than or equal to the preset threshold η, it is judged that there is a target, and H0 represents the maximum correlation coefficient β max When it is less than the preset threshold η, it is judged that there is no target;

[0029] When it is determined that there is a target, select the echo data that is greater than the sampling threshold. data.

[0030] In one embodiment of the present invention, by The steps of cumulatively sampling the data to obtain the sampled data include:

[0031] At the qth cumulative sampling, the The data is sampled q times, and the data of each sample is returned in columns to construct the sample data X of the qth cumulative sampling q :

[0032]

[0033] Where x jp Indicates the p-th accumulated data returned at the q-th cumulative sampling.

[0034] In one embodiment of the present invention, after constructing a second covariance matrix according to the sampled data matrix, the step of performing eigendecomposition on the second covariance matrix to obtain a second eigenvalue and a corresponding second eigenvector thereof includes:

[0035] According to the sampling data X of the qth cumulative sampling q Construct the second covariance matrix:

[0036]

[0037] Where H represents conjugate transpose and E represents expectation;

[0038] For the second covariance matrix R Xq Perform eigendecomposition:

[0039]

[0040] Where ρ represents the first eigenvector u that has the strongest correlation with the preset desired target orientation vector l Position in the permutation result, λ m 、u m Respectively represent the second covariance matrix R Xq The mth second eigenvalue and its corresponding second eigenvector obtained after eigendecomposition, where N represents the dimension of the received data.

[0041] In one embodiment of the present invention, the step of calculating the sum of correlation coefficients between at least part of the second eigenvectors and the preset expected target steering vector based on the position of the first eigenvector having the strongest correlation with the preset expected target steering vector in the arrangement result, and determining the range gate where the target is located when the sum of the correlation coefficients meets a preset condition includes:

[0042] Calculate the second covariance matrix R Xq The first ρ second eigenvectors of the expected target orientation vector v s0 The sum of the correlation coefficients μ q ;

[0043] Get the second covariance matrix R constructed by the q-1th cumulative sampling Xq-1 The first ρ second eigenvectors of the expected target orientation vector v s0 The sum of the correlation coefficients μ q-1 ;

[0044] Determine the sum of correlation coefficients μ q and the sum of the correlation coefficients μ q-1 Whether the preset conditions are met; if so, it means the target is located at the lth q On the other hand, let q = q + 1, and return to the qth cumulative sampling, for the The data is sampled q times, wherein q∈[1:Q], Q is the number of samples greater than the sampling threshold. The amount of data.

[0045] In one embodiment of the present invention, the preset condition is:

[0046] Δu q-1 =|μ q -μ q-1 |≥ζ;

[0047] Where ζ represents the preset decision threshold.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention provides a method for resisting dense false targets based on sample cumulative sampling. First, a first covariance matrix of noise interference is constructed based on the echo signal of the space-time coding array, and its eigenvalues ​​are decomposed to obtain the first eigenvalues. Then, the first eigenvector u with the strongest correlation with the preset expected target steering vector is determined by calculating the correlation coefficients between each first eigenvalue and the preset expected target steering vector. l and its position ρ in the arrangement result; then, by selecting The echo data is used to filter out the interference of noise, and then the position of the real target covered by dense false targets is determined by combining the cumulative sampling, thereby realizing the main lobe deception interference that suppresses dense false targets.

[0050] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a method for resisting dense false targets based on cumulative sampling of samples provided by an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of a method for combating dense false targets based on cumulative sampling of samples provided by an embodiment of the present invention;

[0053] Figure 3 3. It is a schematic diagram of an echo signal model under dense false target interference provided by an embodiment of the present invention.

[0054] Figure 4 Schematic diagram of the correlation between the first feature vector and the expected feature-oriented vector provided by an embodiment of the present invention

[0055] Figure 5 This is a schematic diagram of target positioning based on cumulative sampling provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0057] Figure 1 This is a flow chart of a method for resisting dense false targets based on sample cumulative sampling provided by an embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a method for resisting dense false targets based on sample cumulative sampling, including:

[0058] S1. Constructing a first covariance matrix based on the received echo data, and performing eigendecomposition on the first covariance matrix, and arranging the obtained first eigenvalues ​​and their corresponding first eigenvectors;

[0059] S2. Calculate the correlation coefficient between each first eigenvector and the preset expected target-oriented vector, and determine the first eigenvector with the strongest correlation with the preset expected target-oriented vector and its position in the arrangement result based on the maximum correlation coefficient;

[0060] S3, by comparing the maximum correlation coefficient with the preset threshold η to make a target decision, and when there is a target, select the echo data that is greater than the sampling threshold data;

[0061] S4, by sampling threshold The data is cumulatively sampled to obtain sampling data;

[0062] S5. After constructing a second covariance matrix based on the sampled data, perform eigendecomposition on the second covariance matrix to obtain a second eigenvalue and its corresponding second eigenvector;

[0063] S6. Calculate the sum of the correlation coefficients between at least part of the second eigenvectors and the preset expected target steering vector based on the position of the first eigenvector that has the strongest correlation with the preset expected target steering vector in the arrangement result, and determine the range gate where the target is located when the sum of the correlation coefficients meets a preset condition.

[0064] Optionally, in step S1, the step of constructing a first covariance matrix based on the received echo data, and performing eigendecomposition on the first covariance matrix, and then arranging the obtained first eigenvalues ​​and their corresponding first eigenvectors includes:

[0065] S101. Construct a first covariance matrix based on the received echo data:

[0066] R x =E[xx H ];

[0067] Where x represents echo data, H represents conjugate transpose, and E represents expectation;

[0068] S102. Perform eigendecomposition on the first covariance matrix:

[0069]

[0070] Where N represents the dimension of the received data, λ k 、u k Respectively represent the first covariance matrix R xThe kth first eigenvalue and its corresponding first eigenvector obtained after eigendecomposition;

[0071] S103 : Arrange the first eigenvalues ​​and their corresponding first eigenvectors in descending order according to the magnitude of the first eigenvalues.

[0072] It should be noted that after receiving the echo signal, it needs to be sampled from the analog signal to a digital signal to obtain echo data, and then the first covariance matrix is ​​constructed based on the echo data and eigendecomposition is performed:

[0073]

[0074] In the above formula, λ k 、u k Respectively represent the first covariance matrix R x The kth first eigenvalue and its corresponding first eigenvector obtained after eigendecomposition, where λ j 、u j Represent the eigenvalue and corresponding eigenvector of the interference signal, λ s 、u s They represent the eigenvalue and eigenvector of the true target respectively.

[0075] Furthermore, according to the first eigenvalue λ k The first eigenvalue λ is arranged in descending order k and its corresponding first eigenvector u k .

[0076] Generally speaking, under ideal conditions, the expected target guidance vector v is preset s0 The correlation between the feature vectors corresponding to the real target is the strongest. Therefore, this embodiment uses this feature to determine whether there is a target in the received echo signal.

[0077] In step S2, the steps of calculating the correlation coefficient between each first eigenvector and the preset expected target-oriented vector, and determining the first eigenvector with the strongest correlation with the preset expected target-oriented vector and its position in the arrangement result based on the maximum correlation coefficient include:

[0078] Calculate the first eigenvectors and the preset desired target orientation vector v s0 The correlation coefficient between:

[0079]

[0080] According to the maximum correlation coefficient, determine the first eigenvector u that has the strongest correlation with the preset expected target orientation vector l and its position ρ in the arrangement result.

[0081] Optionally, in step S3, the target is determined by comparing the maximum correlation coefficient with a preset threshold η, and when a target exists, the echo data with a value greater than the sampling threshold is selected. The steps for collecting data include:

[0082] Target judgment is made by comparing the maximum correlation coefficient with the preset threshold η:

[0083]

[0084] Where H1 represents the maximum correlation coefficient β max When it is greater than or equal to the preset threshold η, it is judged that there is a target, and H0 represents the maximum correlation coefficient β max When it is less than the preset threshold η, it is judged that there is no target;

[0085] When a target is judged to exist, select echo data with a value greater than the sampling threshold. data.

[0086] Specifically, this embodiment determines whether the target exists by using a preset threshold η. For example, if the maximum correlation coefficient β max If the maximum correlation coefficient β is greater than or equal to the preset threshold η, it is judged that there is a target and the position of the target in the time domain needs to be analyzed based on cumulative sampling; if the maximum correlation coefficient β is less than the preset threshold η, it is judged that there is no target.

[0087] Optionally, by The steps of cumulatively sampling the data to obtain the sampled data include:

[0088] At the qth cumulative sampling, the value of The data is sampled q times, and the data of each sample is returned in columns to construct the sample data X of the qth cumulative sampling q :

[0089]

[0090] Where x jp Indicates the p-th accumulated data returned at the q-th cumulative sampling.

[0091] Considering that the noise energy may be higher than the set sampling threshold, the number of continuous sampling points can be selected to be higher than the sampling threshold. The data with the sampling points greater than 2 / 3 of the signal pulse width is taken as the cumulative sampling samples. In general, when DFTJ (Dense False Targets Jamming) completely suppresses the target after pulse compression, the data that meets the conditions are mostly DFTJ, which is recorded as x j1 ,x j2 ,…,xjq ,…,x jQ .

[0092]

[0093] Where, is the sampling threshold, x jq The selected value is greater than the sampling threshold data.

[0094] Optionally, in step S5, after constructing the second covariance matrix according to the sampled data matrix, the step of performing eigendecomposition on the second covariance matrix to obtain the second eigenvalue and its corresponding second eigenvector includes:

[0095] S501, based on the sampling data X of the qth cumulative sampling q Construct the second covariance matrix:

[0096]

[0097] Where H represents conjugate transpose and E represents expectation;

[0098] S502, the second covariance matrix R Xq Perform eigendecomposition:

[0099]

[0100] Where ρ represents the first eigenvector u that has the strongest correlation with the preset desired target orientation vector l Position in the permutation result, λ m 、u m Respectively represent the second covariance matrix R Xq The mth second eigenvalue and its corresponding second eigenvector obtained after eigendecomposition, where N represents the dimension of the received data.

[0101] Optionally, in step S6, the step of calculating the sum of the correlation coefficients between at least part of the second eigenvectors and the preset desired target steering vector based on the position of the first eigenvector having the strongest correlation with the preset desired target steering vector in the arrangement result, and determining the range gate where the target is located when the sum of the correlation coefficients meets a preset condition, includes:

[0102] S601, calculate the second covariance matrix R Xq The first ρ second eigenvectors of the expected target orientation vector v s0 The sum of the correlation coefficients μ q :

[0103]

[0104] S602: Obtain the second covariance matrix R constructed by the q-1th cumulative sampling Xq-1 The first ρ second eigenvectors of the expected target orientation vector v s0 The sum of the correlation coefficients μ q-1 ;

[0105] S603. Determine the sum of correlation coefficients μ q and the sum of the correlation coefficients μ q-1 Whether the preset conditions are met; if so, it means the target is located at the lth q On the other hand, let q = q + 1, and return to the above-mentioned cumulative sampling time, when the value is greater than the sampling threshold. The step of sampling the data q times, where q∈[1:Q], Q is the preset cumulative number of samplings.

[0106] Since the true target is only located at the lth q The false target coverage on the range gate, so the target signal is only in the qth cumulative sampling, which will enhance the uth obtained after feature decomposition q The correlation between the second eigenvector and the preset expected target guidance vector. If the interference contains a real target, the u q will be much larger than u obtained in the previous cumulative sampling q-1 Therefore, the preset conditions in step S603 are:

[0107] Δu q-1 =|μ q -μ q-1 |≥ζ;

[0108] Where ζ represents the preset decision threshold.

[0109] Next, the method for resisting dense false targets based on sample accumulation sampling provided by the present invention is further described through simulation experiments.

[0110] Specifically, a one-dimensional uniform linear array is used in the simulation process. The array is placed horizontally with an element spacing of half a wavelength. The number of transmitting elements and receiving elements of the STCA-MIMO radar are M=8 and N=8 respectively. The STCA-MIMO radar array transmits an orthogonal linear frequency modulation signal with a time delay difference of Δt=1us, a signal bandwidth of B=1MHz, a carrier frequency of f0=3GHz, and an element spacing of d=0.05m. The target parameters are set to R0=40km, θ0=5°, and located at the 80th range gate. The jammer parameters R j =50km,θ j=5°, and its dense false target interference is located at the 20th, 40th, 60th, 80th, 100th, 120th, 140th, 160th, and 180th range gates; the width of each range gate is 10 snapshots. The above simulation parameters are shown in Table 1:

[0111] Table 1 Simulation parameters

[0112] parameter Numerical parameter Numerical Number of transmitting array elements 8 Number of receiving elements 8 Signal carrier frequency / GHZ 3 Transmit signal bandwidth / MHZ 1 STCA radar delay / μs 1 Snapshots 2000 Wavelength / m 0.1 Element spacing 0.05 Target distance 40km Target angle 5° Jammer distance 50km Jammer Angle 5° Signal-to-noise ratio / dB 5 Interference-to-noise ratio / dB 30

[0113] Simulation 1

[0114] Figure 3 This is a schematic diagram of the echo signal model under dense false target interference provided by the embodiment of the present invention. Under the above simulation parameters, the echo signal model is constructed for simulation, and 50 Monte Carlo experiments are performed. Figure 3 It can be seen that the real target is covered by dense false targets. When the sampling threshold is set =5, the interference target can be sampled.

[0115] Simulation 2

[0116] Figure 4 Schematic diagram of the correlation between the first eigenvector and the expected feature steering vector provided by the embodiment of the present invention. Under the above simulation parameters, a covariance matrix is ​​constructed based on the echo signal, and the eigendecomposition is performed on it, and the correlation between the eigenvector and the expected feature steering vector is calculated. Figure 4 It can be seen that after the covariance matrix is ​​eigendecomposed and sorted, the correlation between the first eigenvector corresponding to its second largest eigenvalue and the expected feature-oriented vector reaches the maximum, which is consistent with the actual result.

[0117] Simulation 3

[0118] Figure 5 This is a schematic diagram of target positioning based on cumulative sampling provided by an embodiment of the present invention. Under the above simulation parameters, the simulation is performed using the cumulative sampling method, and 50 Monte Carlo experiments are performed. Figure 5 It can be seen that when the decision threshold ζ is set to 30, the target can be judged to be in the 4th interference sample and the target can be judged to be in the 80th range gate. The method provided by the present invention can suppress dense false target interference and locate the real target under the condition of dense deceptive main lobe interference.

[0119] It can be seen from the above embodiments that the beneficial effects of the present invention are:

[0120] The present invention provides a method for resisting dense false targets based on sample cumulative sampling. First, a first covariance matrix of noise interference is constructed based on the echo signal of the space-time coding array, and its eigenvalues ​​are decomposed to obtain the first eigenvalues. Then, the first eigenvector u with the strongest correlation with the preset expected target steering vector is determined by calculating the correlation coefficients between each first eigenvalue and the preset expected target steering vector. l and its position ρ in the arrangement result; then, by selecting The echo data is used to filter out the interference of noise, and then the position of the real target covered by dense false targets is determined by combining the cumulative sampling, thereby realizing the main lobe deception interference that suppresses dense false targets.

[0121] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0122] Descriptions with reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

[0123] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for resisting dense false targets based on sample accumulation sampling, characterized in that: include: Constructing a first covariance matrix based on the received echo data, and performing eigendecomposition on the first covariance matrix, and arranging the obtained first eigenvalues ​​and their corresponding first eigenvectors; Calculating the correlation coefficient between each first eigenvector and the preset expected target-oriented vector, and determining the first eigenvector with the strongest correlation with the preset expected target-oriented vector and its position in the arrangement result based on the maximum correlation coefficient; By comparing the maximum correlation coefficient with a preset threshold Perform target judgment and select echo data with a value greater than the sampling threshold value when a target exists. data; By The data is cumulatively sampled to obtain sampling data; After constructing a second covariance matrix according to the sampled data, performing eigendecomposition on the second covariance matrix to obtain a second eigenvalue and a corresponding second eigenvector; Based on the position of the first eigenvector with the strongest correlation with the preset expected target steering vector in the arrangement result, the sum of the correlation coefficients between at least part of the second eigenvectors and the preset expected target steering vector is calculated, and when the sum of the correlation coefficients meets a preset condition, the range gate where the target is located is determined.

2. The method for resisting dense false targets based on cumulative sampling according to claim 1, characterized in that: The steps of constructing a first covariance matrix based on the received echo data, performing eigendecomposition on the first covariance matrix, and arranging the obtained first eigenvalues ​​and their corresponding first eigenvectors include: Construct the first covariance matrix based on the received echo data: ； Where, represents the echo data, represents the conjugate transpose, express expectations; Perform eigendecomposition on the first covariance matrix: ； Where, Indicates the dimension of the received data, 、 Represents the first covariance matrix After eigendecomposition, the The first eigenvalues ​​and their corresponding first eigenvectors; Arrange the first eigenvalues ​​and their corresponding first eigenvectors in descending order according to the magnitude of the first eigenvalues.

3. The method for resisting dense false targets based on cumulative sampling according to claim 2, characterized in that: The steps of calculating the correlation coefficient between each first eigenvector and a preset expected target-oriented vector, and determining the first eigenvector with the strongest correlation with the preset expected target-oriented vector based on the maximum correlation coefficient and its position in the arrangement result include: Calculate the first eigenvectors and the preset desired target orientation vector The correlation coefficient between: ； According to the maximum correlation coefficient, the first eigenvector with the strongest correlation with the preset expected target orientation vector is determined and its position in the ranking results .

4. The method for resisting dense false targets based on cumulative sampling according to claim 1, characterized in that: By comparing the maximum correlation coefficient with a preset threshold Perform target judgment and select echo data with a value greater than the sampling threshold value when a target exists. The steps for collecting data include: By comparing the maximum correlation coefficient with a preset threshold Make a target decision: ； Where, Indicates the maximum correlation coefficient Greater than or equal to the preset threshold When , it is judged that there is a target, Indicates the maximum correlation coefficient Less than the preset threshold When , the judgment does not have a target; When it is determined that there is a target, select the echo data that is greater than the sampling threshold. data.

5. The method for resisting dense false targets based on cumulative sampling according to claim 4, characterized in that: By The steps of cumulatively sampling the data to obtain the sampled data include: In the When the cumulative sampling is greater than the sampling threshold, The data The data for each sample is returned in columns to construct the Sampling data of cumulative sampling : ； Where, Indicates in The first cumulative sampling is returned The accumulated data.

6. The method for resisting dense false targets based on cumulative sampling according to claim 5, characterized in that: After constructing a second covariance matrix according to the sampled data matrix, the step of performing eigendecomposition on the second covariance matrix to obtain a second eigenvalue and a corresponding second eigenvector thereof includes: According to the said Sampling data of cumulative sampling Construct the second covariance matrix: ； Where, represents the conjugate transpose, express expectations; For the second covariance matrix Perform eigendecomposition: ； Where, The first eigenvector that is most correlated with the preset desired target orientation vector The position in the ranking result, 、 Represents the second covariance matrix After eigendecomposition, the The second eigenvalues ​​and their corresponding second eigenvectors, Indicates the dimension of the received data.

7. The method for resisting dense false targets based on cumulative sampling according to claim 6, characterized in that: The step of calculating the sum of correlation coefficients between at least part of the second eigenvectors and the preset expected target steering vector based on the position of the first eigenvector having the strongest correlation with the preset expected target steering vector in the arrangement result, and determining the range gate where the target is located when the sum of the correlation coefficients meets a preset condition includes: Calculate the second covariance matrix Before The second eigenvector and the preset desired target orientation vector The sum of the correlation coefficients between ; Get through The second covariance matrix constructed by cumulative sampling Before The second eigenvector and the preset desired target orientation vector The sum of the correlation coefficients between ; Determine the sum of correlation coefficients and the sum of the correlation coefficients Whether the preset conditions are met; if so, it means the target is located at On the other hand, , and returns the When the cumulative sampling is greater than the sampling threshold, The data The sampling steps are as follows: , is greater than the sampling threshold The amount of data.

8. The method for resisting dense false targets based on cumulative sampling according to claim 7, characterized in that: The preset conditions are: ； Where, Indicates the preset decision threshold.

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