Dense false target resisting method based on sample accumulative sampling

Abstract

The invention discloses an anti-dense false target method based on sample accumulative sampling, and the method comprises the steps: constructing a first covariance matrix based on echo data, carrying out the feature decomposition, and arranging a first feature value and a first feature vector; based on the maximum correlation coefficient, determining a first feature vector with the strongest correlation with a preset expected target steering vector vs0 and the position rho of the first feature vector in the arrangement result; comparing the maximum correlation coefficient with a preset threshold eta to perform target judgment, and when a target exists, selecting data greater than a sampling threshold # imgabs0 # from the echo data, and further performing accumulative sampling to obtain sampling data; constructing a second covariance matrix according to the sampling data and performing characteristic decomposition to obtain a second characteristic value and a second characteristic vector; and calculating the sum of correlation coefficients between at least part of the second feature vector and a preset expected target steering vector according to the rho, and determining a range gate where the target is located when the sum of the correlation coefficients meets a preset condition, thereby realizing suppression of main lobe deception jamming of 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 positioning have important applications in both military and civilian fields. Usually, the echo signal received by the radar contains the angle and distance information of the target signal, but due to deceptive interference and other reasons, the real target is often covered by the false target, and the target information cannot be directly obtained. Therefore, it is necessary to solve the problem of main lobe deceptive interference.

[0003] For main lobe deception interference suppression, the distance-angle coupling characteristics of the beam can be used to identify true and false targets in the transmit and receive dimensions, thereby achieving interference suppression. In addition, a blind source separation algorithm can be used to separate distance deception interference and targets in different channels. The transmission uses QPC (quadratic phase code), and the interference and target can be distinguished through decoding and main value distance compensation. For main lobe deception interference, it can be suppressed by a space-time dual-domain algorithm or an oblique projection-based algorithm.

[0004] However, the above methods are all based on ideal sampling and do not take into account the problem when dense false target interference samples contain real targets, resulting in a sharp drop 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 cumulative 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 sample cumulative sampling, comprising:

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

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

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

[0010] By Cumulative sampling is performed on the data to obtain sampling data;

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

[0012] According to the position of the first eigenvector having 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 eigenvector 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, and arranging the obtained first eigenvalues ​​and their corresponding first eigenvectors after eigendecomposing the first covariance matrix comprises:

[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 conjugate transposition, and E represents 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] According to the magnitude of the first eigenvalues, the first eigenvalues ​​and their corresponding first eigenvectors are arranged in descending order.

[0021] In one embodiment of the present invention, the steps of calculating the correlation coefficient between each first feature vector and a preset expected target orientation vector, and determining the first feature vector with the strongest correlation with the preset expected target orientation 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 feature vector 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 judgment 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 comprises:

[0026] Target determination is performed by comparing the maximum correlation coefficient with a preset threshold value η:

[0027]

[0028] In the formula, 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 means that at 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 echo data with a value 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 sampled data X of the qth cumulative sampling q :

[0032]

[0033] In the formula, 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 the second covariance matrix according to the sampling data matrix, the step of performing eigendecomposition on the second covariance matrix to obtain the second eigenvalue and its corresponding second eigenvector includes:

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

[0036]

[0037] In the formula, H represents conjugate transpose and E represents expectation;

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

[0039]

[0040] In the formula, ρ represents the first eigenvector u that has the strongest correlation with the preset expected target orientation vector l The position in the ranking 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 feature vectors and the preset expected target steering vector according to the position of the first feature vector 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 and the preset desired 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 and the preset desired 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 correlation coefficients μ q-1 Whether the preset conditions are met; if so, it means that the target is located at the lth q On the other hand, let q = q + 1, and return the value greater than the sampling threshold when the qth cumulative sampling is performed. The step of sampling the data q times, wherein q∈[1:Q], Q is the value 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 features 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 of 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 in combination with 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 It 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;

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

[0053] Figure 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 orientation vector provided by an embodiment of the present invention

[0055] Figure 5 It 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 is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0057] Figure 1 FIG. 1 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, comprising:

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

[0059] S2, 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;

[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 Cumulative sampling is performed on the data to obtain sampling data;

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

[0063] S6. Calculate the sum of correlation coefficients between at least part of the second eigenvectors and the preset expected target steering vector according to the position of the first eigenvector having 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 arranging the obtained first eigenvalues ​​and their corresponding first eigenvectors after eigendecomposing the first covariance matrix comprises:

[0065] S101, constructing a first covariance matrix based on the received echo data:

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

[0067] In the formula, 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 magnitudes of the first eigenvalues.

[0072] It should be noted that after receiving the echo signal, it is necessary to sample it from an analog signal to a digital signal to obtain echo data, and then construct a first covariance matrix based on the echo data and perform eigendecomposition:

[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 Respectively 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] Further, according to the first eigenvalue λ k The size of 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-directed vector v is preset s0 The correlation between the feature vectors corresponding to the real target is the strongest, so this embodiment uses this feature to determine whether there is a target in the received echo signal.

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

[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 feature vector 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 of collecting data include:

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

[0083]

[0084] In the formula, 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 means that at 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 value 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 it is necessary to analyze the position of the target in the time domain 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 filtering the The steps of cumulatively sampling the data to obtain the sampled data include:

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

[0089]

[0090] In the formula, 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, we can choose a continuous sampling point number 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, recorded as x j1 ,x j2 ,…,xjq ,…,x jQ .

[0092]

[0093] In the formula, 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] In the formula, H represents conjugate transpose and E represents expectation;

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

[0099]

[0100] In the formula, ρ represents the first eigenvector u that has the strongest correlation with the preset expected target orientation vector l The position in the ranking 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, according to the position of the first feature vector 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 feature vector and the preset expected target steering vector is calculated, and when the sum of the correlation coefficients meets the preset conditions, the step of determining the range gate where the target is located includes:

[0102] S601, calculate the second covariance matrix R Xq The first ρ second eigenvectors of and the preset desired 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 and the preset desired 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 correlation coefficients μ q-1 Whether the preset conditions are met; if so, it means that the target is located at the lth q On the other hand, let q = q + 1, and return to the above-mentioned cumulative sampling when the qth sampling is greater than the sampling threshold. The step of sampling the data q times, where q∈[1:Q], Q is the preset cumulative sampling number.

[0106] Since the real 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 condition in step S603 is:

[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 cumulative 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 array element spacing of half a wavelength. The number of transmitting array elements and the number of receiving array 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 delay difference of Δt=1us, a signal bandwidth of B=1MHz, a carrier frequency of f0=3GHz, and an array 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; wherein 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 Numeric parameter Numeric Number of transmitting 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 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 When =5, the interference target can be sampled.

[0115] Simulation 2

[0116] Figure 4 is a schematic diagram of the correlation between the first eigenvector and the expected feature steering vector provided by an 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, and the Figure 4 It can be seen that after the covariance matrix is ​​eigendecomposed and sorted, the correlation between the first eigenvector corresponding to the 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 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 by using the cumulative sampling method, and 50 Monte Carlo experiments are performed. Figure 5 It can be seen that when the decision threshold ζ=30 is set, 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 the interference of dense false targets 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 features 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 of 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 in combination with 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 as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

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

[0123] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A method for resisting dense false targets based on sample cumulative sampling, characterized in that: include: Constructing a first covariance matrix based on the received echo data, and arranging first eigenvalues ​​and their corresponding first eigenvectors obtained after eigendecomposing the first covariance matrix; Calculating the correlation coefficient between each first eigenvector and the preset expected target orientation vector, and determining the first eigenvector with the strongest correlation with the preset expected target orientation vector and its position in the arrangement result based on the maximum correlation coefficient; The target is determined by comparing the maximum correlation coefficient with a preset threshold value η, and when a target exists, the echo data with a value greater than the sampling threshold value is selected. data; By Cumulative sampling is performed on the data to obtain sampling data; After constructing a second covariance matrix according to the sampled data, eigendecomposing the second covariance matrix to obtain a second eigenvalue and a second eigenvector corresponding thereto; According to the position of the first eigenvector having 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 eigenvector 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 sample cumulative sampling according to claim 1 is characterized in that: The step of constructing a first covariance matrix based on the received echo data, and arranging the obtained first eigenvalues ​​and their corresponding first eigenvectors after eigendecomposing the first covariance matrix comprises: Construct the first covariance matrix based on the received echo data: R x =E[xx H ]; Wherein, x represents the echo data, H represents the conjugate transpose, and E represents the expectation; Perform eigendecomposition on the first covariance matrix: 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; According to the magnitude of the first eigenvalues, the first eigenvalues ​​and their corresponding first eigenvectors are arranged in descending order.

3. The method for resisting dense false targets based on sample cumulative sampling according to claim 2 is characterized in that: The step of calculating the correlation coefficient between each first feature vector and a preset expected target orientation vector, and determining the first feature vector with the strongest correlation with the preset expected target orientation vector and its position in the arrangement result based on the maximum correlation coefficient, comprises: Calculate the first eigenvectors and the preset desired target orientation vector v s0 The correlation coefficient between: According to the maximum correlation coefficient, determine the first feature vector u that has the strongest correlation with the preset expected target orientation vector l and its position ρ in the arrangement result.

4. The method for resisting dense false targets based on sample cumulative sampling according to claim 1, characterized in that: The target is determined by comparing the maximum correlation coefficient with a preset threshold value η, and when a target exists, the echo data with a value greater than the sampling threshold value is selected. The steps of collecting data include: Target determination is performed by comparing the maximum correlation coefficient with a preset threshold value η: In the formula, 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 means that at the maximum correlation coefficient β max When it is less than the preset threshold η, it is judged that there is no target; When it is determined that there is a target, select echo data with a value greater than the sampling threshold. data.

5. The method for resisting dense false targets based on sample cumulative sampling according to claim 4 is characterized in that: By The steps of cumulatively sampling the data to obtain the sampled data include: 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 sampled data X of the qth cumulative sampling q : In the formula, x jp Indicates the p-th accumulated data returned at the q-th cumulative sampling.

6. The method for resisting dense false targets based on sample cumulative sampling according to claim 5 is characterized in that: After constructing a second covariance matrix according to the sampling data matrix, the step of performing eigendecomposition on the second covariance matrix to obtain a second eigenvalue and a second eigenvector corresponding thereto comprises: According to the sampling data X of the qth cumulative sampling q Construct the second covariance matrix: In the formula, H represents conjugate transpose and E represents expectation; For the second covariance matrix R Xq Perform eigendecomposition: In the formula, ρ represents the first eigenvector u that has the strongest correlation with the preset expected target orientation vector l The position in the ranking 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.

7. The method for resisting dense false targets based on sample cumulative sampling according to claim 6 is characterized in that: The step of calculating the sum of correlation coefficients between at least part of the second feature vector and the preset expected target steering vector according to the position of the first feature vector 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, comprises: Calculate the second covariance matrix R Xq The first ρ second eigenvectors of and the preset desired target orientation vector v s0 The sum of the correlation coefficients μ q ; Get the second covariance matrix R constructed by the q-1th cumulative sampling Xq-1 The first ρ second eigenvectors of and the preset desired target orientation vector v s0 The sum of the correlation coefficients μ q-1 ; Determine the sum of correlation coefficients μ q and the sum of correlation coefficients μ q-1 Whether the preset conditions are met; if so, it means that the target is located at the lth q On the other hand, let q = q + 1, and return the value greater than the sampling threshold when the qth cumulative sampling is performed. The step of sampling the data q times, wherein q∈[1:Q], Q is the value greater than the sampling threshold The amount of data.

8. The method for resisting dense false targets based on sample cumulative sampling according to claim 7 is characterized in that: The preset conditions are: Thu q-1 =|μ q -m q-1 |≥ζ; Where ζ represents the preset decision threshold.

Images