Target intelligent fusion detection method and system assisted by main data screening, terminal and medium

Abstract

The invention relates to the field of radar detection, and particularly discloses an intelligent target fusion detection method and system assisted by main data screening, a terminal and a medium, and the method comprises the steps: sorting main data, carrying out the estimation of the number of unknown scattering points, and carrying out the joint estimation calculation of a clutter covariance matrix through the combination of the estimation of the number of the unknown scattering points, then the distance extension target detection statistic is calculated, whether a target exists or not is judged according to a threshold, the accuracy of clutter covariance matrix estimation is effectively improved in a mode of calculating covariance matrix joint estimation by combining scattering point number estimation, and the detection precision is improved. Clutter component information is extracted through main data and used for supplementing auxiliary data, the covariance matrix estimation precision is improved, and the self-adaptive detection capacity of the distance extension target under the small sample condition is improved.

Description

Technical Field

[0001] The present invention relates to the field of radar detection, and in particular to a method, system, terminal and medium for intelligent target fusion detection assisted by master data screening. Background Art

[0002] Radar target detection is the process of determining whether a target exists within a specific area, which can generally be described as a binary hypothesis testing problem. With the continuous increase in radar bandwidth, the corresponding problem of target detection in high-range-resolution radars has gradually become a hot research area. Currently, adaptive detection methods for range-extended targets in multi-channel wideband radars have achieved certain results. These methods typically use the range bins surrounding the target to be detected to estimate the clutter covariance matrix. The generalized adaptive matched filter (GAMF) detector adopts a two-step design strategy, which reduces computational complexity to a certain extent while maintaining detection performance. However, the increasingly complex external environment places increasingly stringent requirements on radar target detection. In actual detection, there is often a lack of auxiliary data. In such cases, if traditional wideband radar range-extended target detection methods are used, the clutter covariance matrix estimation will be inaccurate, affecting detection performance and even causing the clutter covariance matrix to become irreversible, making traditional detection methods completely ineffective.

[0003] In actual detection, small sample sizes are often difficult to obtain for training. Many researchers have exploited the skew-symmetric structure of the covariance matrix to design wideband radar range-extended target detectors for these small sample sizes. However, these researchers have not considered methods for acquiring more auxiliary data, and further research is needed on adaptive detection methods for these small sample sizes. To address the challenge of degradation or even complete failure of existing wideband radar range-extended target detectors under small sample sizes, the key to improving intelligent target fusion detection methods using primary data screening and assistance is to fully utilize the primary data, extract the clutter component information, and use it to supplement the auxiliary data, thereby improving the accuracy of the covariance matrix estimation. This is one of the most pressing challenges currently in need of resolution. Summary of the Invention

[0004] To solve the above problems, the present invention provides a method, system, terminal and medium for intelligent fusion detection of targets assisted by primary data screening, which uses primary data to extract clutter component information to supplement auxiliary data, improve the accuracy of covariance matrix estimation, and enhance the adaptive detection capability of range-extended targets under small sample conditions.

[0005] In a first aspect, the technical solution of the present invention provides a method for intelligent fusion detection of targets assisted by master data screening, comprising the following steps:

[0006] S1, obtain the detection data of K distance units to be detected as the main data Z = [z1, z2, ..., z K ], obtain the detection data of R distance units around the distance unit to be detected as auxiliary data Z R =[z′1,z′2,…,z′ R ], using auxiliary data Z R Obtain auxiliary data covariance matrix estimate Covariance estimation based on auxiliary data Sort the master data Z by the generalized inner product;

[0007] S2, perform scattering point estimation on the sorted master data Z to obtain the estimated number of unknown scattering points in the distance unit to be detected

[0008] S3, using the unknown number of scattering points to estimate Differentiate the master data Z and obtain the master data estimate containing the target scattering points and the main data estimation without target scatter points And use Z R 、 and Obtain a joint estimate of the clutter covariance matrix Joint estimation of the clutter covariance matrix based on the generalized likelihood ratio detection criterion Obtain range-extended target detection statistics;

[0009] S4, judging whether the range-extended target detection statistic is greater than or equal to a predetermined threshold; if so, judging that a target exists in the range unit to be detected; otherwise, judging that no target exists in the range unit to be detected.

[0010] In an optional embodiment, the auxiliary data covariance estimate Sort the master data Z by the generalized inner product, specifically including:

[0011] Covariance estimation based on auxiliary data The generalized inner product statistic is established for the master data Z, and the generalized inner product statistic δ of the i-th master data is i It can be expressed as,

[0012]

[0013] Where i = 1, 2, .., K;

[0014] For the main data, according to the generalized inner product statistic δ i Sort from large to small, assuming that the i-th statistic after sorting is δ (i) , then the main data Z is sorted and re-expressed as Z=[z (1) ,z(2) ,…,z (K) ].

[0015] In an optional embodiment, step S2 specifically includes:

[0016] S21, assuming that there are an unknown number of target scattering points p in the master data Z, that is, a multiple hypothesis H with an unknown number of scattering points is formed. p , in H p Assume that Z p =[z (1) ,z (2) ,…,z (p) ] represents the main data containing the target scattering point, ZZ p =[z (p+1) ,z (p+2) ,…,z (K) ] represents the main data without target scattering points;

[0017] S22, assuming that the target scattering point master data Z p =[z (1) ,z (2) ,…,z (p) ] and master data ZZ without target scattering points p =[z (p+1) ,z (p+2) ,…,z (K) ] is known, and is represented by M p and M R Under this assumption, the maximum value of K likelihood functions is optimized when the number of scattering points is unknown, and the M p and M R Estimation expression of the number of scattering points;

[0018] S23, using the master data Z containing the target scattering points p Calculate the covariance of the main data containing the target scatter points

[0019] Matrix Estimation Using auxiliary data Z R Obtain master data ZZ without target scattering points p The covariance matrix estimate of

[0020] S24, use and Replace M in the scattering point number estimation expression p and M R , obtain the estimated number of unknown scattering points within the distance unit to be detected

[0021] In an optional embodiment, under this assumption, the maximum value of K likelihood functions is optimized when the number of scattering points is unknown, and the relevant M p and M R The estimated expression of the number of scattering points includes:

[0022] The joint conditional probability density function of Z of the master data is expressed as,

[0023]

[0024] Where det(·) represents the determinant of the matrix, and exp(·) represents the exponential function with the natural coefficient e as the base;

[0025] In order to make f(Z|M R ,M p ; p) takes the largest value, Expressed as That is, the estimated expression for the number of scattering points;

[0026] Furthermore, the number of unknown scattering points is estimated Expressed as

[0027] In an optional embodiment, the master data Z containing target scatter points is used. p Calculate the covariance matrix estimate of the main data containing the target scatter points Specifically include:

[0028] Assume M p Satisfies the noise additive structure, and the noise power is 1, that is, M p =I N +M q , where I N is the N×N dimensional identity matrix, M q is an N×N dimensional unknown matrix, where N is the number of channels of the radar system;

[0029] Then M p The fast maximum likelihood estimation of is expressed as

[0030]

[0031] Among them, Φ p and Λ p Respectively The eigenvector matrix and eigenvalue diagonal matrix after eigenvalue decomposition, Λ p =Diag(λ1,λ2,…,λ N ) is loaded with eigenvalues ​​smaller than the noise power to a size equal to the noise power to obtain the reconstruction matrix

[0032] In an optional embodiment, the joint estimation of the clutter covariance matrix Expressed as,

[0033]

[0034] Where s is the known target normalized steering vector.

[0035] In an optional embodiment, the range extended target detection statistic is expressed as,

[0036]

[0037] In a second aspect, the technical solution of the present invention provides a target intelligent fusion detection system assisted by master data screening, comprising:

[0038] The main data sorting module is used to obtain the detection data of K distance units to be detected as the main data Z = [z1, z2, ..., z K ], obtain the detection data of R distance units around the distance unit to be detected as auxiliary data Z R =[z1′,z2′,…,z′ R ], using auxiliary data Z R Obtain auxiliary data covariance matrix estimate Covariance estimation based on auxiliary data Sort the master data Z by the generalized inner product;

[0039] The scattering point number estimation module is used to estimate the scattering points of the sorted main data Z and obtain the estimated number of unknown scattering points in the distance unit to be detected.

[0040] Detection statistics calculation module, used to estimate the number of unknown scattering points Differentiate the master data Z and obtain the master data estimate containing the target scattering points and the main data estimation without target scatter points And use Z R 、 and Obtain a joint estimate of the clutter covariance matrix Joint estimation of the clutter covariance matrix based on the generalized likelihood ratio detection criterion Obtain range-extended target detection statistics;

[0041] The target existence judgment module is used to judge whether the range-extended target detection statistic is greater than or equal to a predetermined threshold. If so, it is determined that a target exists in the range unit to be detected; otherwise, it is determined that no target exists in the range unit to be detected.

[0042] In a third aspect, the technical solution of the present invention provides a terminal, including:

[0043] A memory for storing a target intelligent fusion detection program assisted by master data screening;

[0044] The processor is used to implement the steps of the master data screening assisted target intelligent fusion detection method as described in any of the above items when executing the master data screening assisted target intelligent fusion detection program.

[0045] In a fourth aspect, the technical solution of the present invention provides a computer-readable storage medium, on which a target intelligent fusion detection program assisted by master data screening is stored. When the target intelligent fusion detection program assisted by master data screening is executed by a processor, the steps of the target intelligent fusion detection method assisted by master data screening as described in any one of the above items are implemented.

[0046] As can be seen from the above technical solution, the present application has the following advantages: first, the main data is sorted, then the number of unknown scattering points is estimated, and further combined with the unknown scattering point number estimation to perform a joint estimation calculation of the clutter covariance matrix, and then the range extension target detection statistic is calculated, and the presence of the target is determined according to the threshold. The method of combining the calculation of the covariance matrix with the scattering point number estimation effectively improves the accuracy of the clutter covariance matrix estimation and improves the detection accuracy. The present invention constructs a target intelligent fusion detection method assisted by main data screening, which can perform detection in the extreme case of sample missing, and sorts the main data by the generalized inner product method, reducing the computational complexity and facilitating engineering implementation. At the same time, combined with the prior information of the noise covariance matrix structure, it improves the estimation accuracy of the unknown covariance matrix structure, and ensures that the estimated covariance matrix is ​​a non-singular matrix, which is convenient for engineering implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 The present invention provides an architectural diagram of a method for intelligent target fusion detection assisted by master data screening.

[0049] Figure 2 A flow chart of a target intelligent fusion detection method assisted by master data screening provided by an embodiment of the present invention.

[0050] Figure 3The figure is a schematic diagram comparing the detection performance of the method of the present invention and existing detection methods for marine environment detection.

[0051] Figure 4 A schematic block diagram of the structure of a target intelligent fusion detection system assisted by master data screening provided by an embodiment of the present invention.

[0052] Figure 5 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the application objectives, features, and advantages of this application more obvious and easy to understand, the technical solutions protected by this application will be clearly and completely described below using specific embodiments and drawings. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by those skilled in the art to which the present invention pertains. The terms used in this application and in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention.

[0055] Under the range extension target, the main data contains clutter components of multiple range units, and the GAMF detector only uses the auxiliary data for covariance matrix estimation. The clutter components in the main data are discarded, resulting in information loss. Therefore, the clutter components in the main data can be utilized and supplemented as auxiliary data to improve the accuracy of covariance matrix estimation. In the main data, the observation signal can be expressed as the sum of the target component and the clutter component. For sparse scattered point targets, the amplitude of some scattering points in the main data is close to 0, and its echo data is only clutter data. The generalized inner product (GIP) is expressed as the inner product of the observation signal after covariance matrix whitening, which can be used as a reasonable means to extract clutter components in the main data. Based on this, an embodiment of the present invention provides a target intelligent fusion detection method assisted by main data screening, Figure 1This is a schematic diagram of the architecture of this method. Primary data and auxiliary data are obtained, and the covariance matrix estimate of the auxiliary data is calculated. Based on the estimate, the primary data is sorted by generalized inner product. Then, based on the sorted primary data, scattering point estimation is performed to obtain primary data containing target scattering points and primary data of non-target scattering points. Finally, the auxiliary data, primary data containing target scattering points, and primary data of non-target scattering points are combined to obtain a joint estimate of the clutter covariance matrix. Based on the joint estimate, the range-extended target detection statistic is calculated, and the statistic is used to perform threshold discrimination to determine whether a target exists in the range cell.

[0056] Figure 2 A flow chart of a target intelligent fusion detection method assisted by master data screening provided by an embodiment of the present invention. Figure 2 The execution entity may be a master data screening-assisted target intelligent fusion detection system. The master data screening-assisted target intelligent fusion detection method provided in embodiments of the present invention is executed by a computer device. Accordingly, the master data screening-assisted target intelligent fusion detection system runs on the computer device. The order of the steps in this flowchart may be changed, and some steps may be omitted, depending on different needs.

[0057] like Figure 2 As shown, the method includes the following steps.

[0058] S1, obtain the detection data of K distance units to be detected as the main data Z = [z1, z2, ..., z K ], obtain the detection data of R distance units around the distance unit to be detected as auxiliary data Z R =[z′1,z′2,…,z′ R ], using auxiliary data Z R Obtain auxiliary data covariance matrix estimate Covariance estimation based on auxiliary data Sort the master data Z by the generalized inner product.

[0059] Specifically, obtain N×K dimensional master data Z=[z1,z2,…,z K ], obtain N×R dimensional auxiliary data Z from the distance cells around the distance cell to be detected R =[z′1,z′2,…,z′ R ]; Using auxiliary data Z R Obtain auxiliary data covariance estimates use Calculate the GIP statistic for each main data distance unit and sort the main data distance units from large to small according to the GIP statistic.

[0060] For a radar system with N channels, assuming that the range-extended target may occupy K consecutive range units to be detected, the i-th primary data is expressed as Assume that the clutter component Obey the mean of 0 N×1 , the covariance matrix is Gaussian distribution, where M is unknown, that is:

[0061]

[0062] Indicates that the clutter obeys a complex Gaussian distribution, Represents a complex set.

[0063] Specifically, under the assumption that there is no target (denoted as H0), the observed data z i It can be simply expressed as the clutter component c i ,Right now:

[0064] z i =c i , i=1,2,…,K (1)

[0065] Under the assumption that there is a target (denoted as H1), the observed data z i is the sum of the target signal component and the clutter component, that is:

[0066] z i =a i s+c i , i=1,2,…,K (2)

[0067] Among them, a i represents the target signal amplitude of the target in the i-th range unit, and s represents the known target normalized steering vector.

[0068] In order to estimate the unknown clutter covariance matrix, it is necessary to collect a set of auxiliary data of pure clutter near the range unit to be detected. It can be expressed as

[0069] z i ′=c i ′, i=1,2,…,R (3)

[0070] Among them, the pure clutter component c that assists data acquisition i ′ and the clutter component c in the main data i are independent and identically distributed.

[0071] In summary, the hypothesis test of distance expansion target can be expressed as

[0072]

[0073] in,

[0074] First, use Z R Obtain auxiliary data covariance matrix estimate It can be expressed as

[0075]

[0076] Secondly, combined with the auxiliary data covariance matrix estimation The generalized inner product statistics are established for the main data and sorted. The GIP statistics of the i-th main data δ i It can be expressed as

[0077]

[0078] Sort the main data distance units from large to small according to the GIP statistic. Assume that the ith statistic after sorting is δ (i) , and δ (i) >δ (i+1) , the main data is sorted and re-expressed as Z = [z (1) ,z (2) ,…,z (K) ].

[0079] S2, perform scattering point estimation on the sorted master data Z to obtain the estimated number of unknown scattering points in the distance unit to be detected

[0080] Specifically, establish multiple hypotheses H with unknown number of scattering points p , (p=1,2,…,K), first, assuming that the clutter covariance matrix is ​​known, the maximum value of K likelihood functions is optimized when the number of scattering points is unknown, and the expression for estimating the number of scattering points is obtained; secondly, when the covariance matrix is ​​unknown, calculate H p Estimation of the covariance matrix of the main data containing target scattering points under the assumption Estimation of the covariance matrix of the main data without target scattering points Replace the known covariance matrix to obtain an estimate of the number of unknown scattering points

[0081] For sparse scattered point targets, not all primary data contain target components. The amplitude of the scattered points in some primary data is close to 0, and its echo data is only clutter data. The primary data is divided into two parts: Z = [Z y ,Z n ], where Z y Represents the main data containing the target scattering point, Z n Represents the main data without target scatter points.

[0082] Since the number of target scattering points is unknown, multiple hypotheses are considered. Assume that there are unknown number p of target scattering points in the master data. p Assume that Z p =[z (1) ,z (2) ,…,z (p) ] represents the main data containing the target scattering point, ZZ p =[z (p+1) ,z (p+2) ,…,z (K) ] represents the main data without target scatter points. The hypothesis test of the number of target scatter points can be expressed as

[0083]

[0084] Among them ZZ p It does not contain target scattering points, and its covariance matrix is ​​the same as the auxiliary data, which is M R , M p Represents the covariance matrix of the primary data containing the target scatter points.

[0085] First, assume that M R and M p It is known that the joint conditional probability density function of Z can be expressed as

[0086]

[0087] Where det(·) represents the determinant of the matrix, and exp(·) represents the exponential function with the natural coefficient e as the base.

[0088] In order to make f(Z|M R ,M p ; p) takes the largest value, It can be expressed as

[0089]

[0090] Secondly, the covariance matrix of the main data containing the target scattering points is estimated by using the main data and auxiliary data Estimation of the covariance matrix of the main data without target scatter points in It has been calculated in step 1, so we only need to consider

[0091] Assume M p Satisfies the noise additive structure, and the noise power is 1, that is, M p =I N +M q , where I N is the N×N dimensional identity matrix, Mq is an N×N dimension unknown matrix. Therefore, M p The fast maximum likelihood estimation of is expressed as:

[0092]

[0093] Among them, Φ p and Λ p Respectively The eigenvector matrix and eigenvalue diagonal matrix after eigenvalue decomposition, Λ p =Diag(λ1,λ2,...,λ N ) is loaded with eigenvalues ​​smaller than the noise power to a size equal to the noise power to obtain the reconstruction matrix It should be noted that the first formula in formula (11) shows how to obtain the matrix The second formula is for the matrix Decompose.

[0094]

[0095] S3, using the unknown number of scattering points to estimate Differentiate the master data Z and obtain the master data estimate containing the target scattering points and the main data estimation without target scatter points And use Z R 、 and Obtain a joint estimate of the clutter covariance matrix Joint estimation of the clutter covariance matrix based on the generalized likelihood ratio detection criterion Get the range-extended object detection statistics.

[0096] Specifically, a two-step GLRT detection statistic for range-extended targets is constructed under the condition that the clutter covariance matrix is ​​unknown. First, assuming that the clutter covariance matrix is ​​known, the range-extended target detection statistic is derived. Second, the main data Z and the auxiliary data Z are used to calculate the range-extended target detection statistic. R The covariance matrix is ​​estimated and replaced with the known clutter covariance matrix in the hypothesis.

[0097] For the hypothesis testing problem, under the premise that the clutter covariance matrix M is known, according to the generalized likelihood ratio test criterion, the range extended target detection statistic can be preliminarily expressed as

[0098]

[0099] Among them, under the assumption H1, the MLE of the unknown complex amplitude vector a of the range extension target is

[0100]

[0101] Substituting Equation (14) into Equation (13) gives the detection statistic of the range extended target under the condition that M is known.

[0102]

[0103] In the formula, M is assumed to be known. In practical applications, the clutter covariance matrix M is unknown and needs to be estimated using the primary data Z and the auxiliary data Z. R Estimate the covariance matrix.

[0104] It should be noted that the primary data can be further distinguished using the estimated number of scatterers in Step 2. The primary data estimation containing target scatterers The primary data estimation without target scatterers can further extract clutter information. Therefore, the joint estimation of the clutter covariance matrix can be expressed as

[0105]

[0106] Substitute the unknown M in Equation (15) to obtain the adaptive detector for range extended targets under small sample conditions:

[0107]

[0108] S4. Determine whether the detection statistic of the range extended target is greater than or equal to a predetermined threshold. If so, it is determined that there is a target in the range cell to be detected; otherwise, it is determined that there is no target in the range cell to be detected.

[0109] Specifically, set a predetermined detection threshold T and compare the range extended detection statistic t with the detection threshold. If t ≥ T, it is determined that there is a target in the detection range cell; conversely, if t < T, it is determined that there is no target in the range cell to be detected. <s

[0110] The following provides two specific embodiments to illustrate the effectiveness of the method. The first specific embodiment is for the sea detection environment, and the second specific embodiment is for the land detection environment. Specific Embodiment 1:

[0112] Step A1. Use a sea detection radar to irradiate the area to be detected and the surrounding target-free area with radar to obtain the primary data of K range cells and the R auxiliary data Send the auxiliary data Z R to the covariance matrix estimation module, and calculate the estimated covariance matrix of the auxiliary data according to Equation (6) Subsequently, the estimated covariance matrix of the auxiliary data The main data Z is sent to the generalized inner product sorting module to sort the main data. After sorting, it is determined that the front distance units are more likely to contain target scattering points.

[0113] It is worth noting that the number of target scattering points in the master data is unknown, and the actual range unit permutations and combinations are too large to be calculated in real time. Therefore, it is considered to use the generalized inner product to sort the master data, degenerating the many cases corresponding to one scattering point into a single case, so that the estimation of the master data containing target scattering points is simplified to the estimation of the number of scattering points, which greatly improves the computational efficiency.

[0114] Step A2: Send the sorted primary data to the scattering point estimation module to establish multiple hypotheses with unknown number of scattering points. First, assume that the clutter covariance matrix is ​​known, and optimize the maximum value of K likelihood functions under the unknown number of scattering points to obtain the expression for estimating the number of scattering points. Calculate the covariance matrix estimation of the primary data excluding the target scattering points according to equations (6) and (11). and H p Estimation of the covariance matrix of the main data containing target scattering points under the assumption Replace the known covariance matrix and obtain the unknown scattering point number estimate according to formula (12):

[0115] It is worth noting that when calculating H p Estimation of the covariance matrix of the main data containing target scattering points under the assumption In the process, the structural prior information of the noise covariance matrix is ​​fully utilized to improve the estimation accuracy of the unknown covariance matrix structure. Under different assumptions, the amount of abnormal data auxiliary data may be less than the number of array elements N. The fast maximum likelihood method ensures that the estimated covariance matrix is ​​a non-singular matrix.

[0116] Step A3, use the number of unknown scattering points to estimate Distinguish the K master data and obtain the master data estimation containing the target scattering points and the main data estimation without target scatter points Then, combined with the auxiliary data Z R , calculate the joint estimate of the clutter covariance matrix according to formula (16) Substitute into formula (17) to calculate the statistic, and finally compare it with the threshold to determine whether there is a target in the range unit to be detected.

[0117] It is worth noting that in actual detection, small sample sizes are often difficult to obtain for training samples. When using traditional broadband radar range-extended target detection methods, the clutter covariance matrix estimation is inaccurate, affecting detection performance and even causing the clutter covariance matrix to become irreversible, rendering the traditional detection method ineffective. In step A3, not only auxiliary data is utilized, but also the clutter information in the primary data is fully mined as a supplement to better cope with small sample sizes.

[0118] Figure 3 This is a schematic diagram comparing the detection performance of the method of the present invention and existing detection methods for marine environment detection. Figure 3 In the case of R=10, N=8, K=15, p=9, the false alarm rate P fa =10 -3 . Specific embodiment 2:

[0120] Step B1: Use ground detection radar to illuminate the target area and the surrounding target-free range to obtain the master data of K range units. and R auxiliary data The auxiliary data Z R Send it to the covariance matrix estimation module and calculate the auxiliary data covariance matrix estimation according to formula (6) Then the auxiliary data covariance matrix is ​​estimated The main data Z is sent to the generalized inner product sorting module to sort the main data. After sorting, it is determined that the front distance units are more likely to contain target scattering points.

[0121] It is worth noting that the number of target scattering points in the master data is unknown, and the actual range unit permutations and combinations are too large to be calculated in real time. Therefore, it is considered to use the generalized inner product to sort the master data, degenerating the many cases corresponding to one scattering point into a single case, so that the estimation of the master data containing target scattering points is simplified to the estimation of the number of scattering points, which greatly improves the computational efficiency.

[0122] Step B2: Send the sorted master data to the scattering point estimation module to establish multiple hypotheses with unknown number of scattering points. First, assume that the clutter covariance matrix is ​​known, and optimize the maximum value of K likelihood functions under the unknown number of scattering points to obtain the expression for estimating the number of scattering points. Calculate the covariance matrix estimation of the master data excluding the target scattering points according to equations (6) and (11). and H p Estimation of the covariance matrix of the main data containing target scattering points under the assumption Replace the known covariance matrix and obtain the unknown scattering point number estimate according to formula (12):

[0123] It is worth noting that when calculating H pEstimation of the covariance matrix of the main data containing target scattering points under the assumption In the process, the structural prior information of the noise covariance matrix is ​​fully utilized to improve the estimation accuracy of the unknown covariance matrix structure. Under different assumptions, the amount of abnormal data auxiliary data may be less than the number of array elements N. The fast maximum likelihood method ensures that the estimated covariance matrix is ​​a non-singular matrix.

[0124] Step B3, use the number of unknown scattering points to estimate Distinguish the K master data and obtain the master data estimation containing the target scattering points and the main data estimation without target scatter points Then, combined with the auxiliary data Z R , calculate the joint estimate of the clutter covariance matrix according to formula (16) Substitute into formula (17) to calculate the statistic, and finally compare it with the threshold to determine whether there is a target in the unit to be detected.

[0125] It is worth noting that in actual detection, small sample sizes are often difficult to obtain for training samples. When using the traditional wideband radar range extension target detection method, the clutter covariance matrix estimation is inaccurate, which affects the detection performance and even makes the clutter covariance matrix irreversible, making the traditional detection method ineffective. In step B3, not only the auxiliary data is utilized, but also the clutter information in the main data is fully mined as a supplement to better cope with small sample sizes.

[0126] The above describes in detail an embodiment of a target intelligent fusion detection method assisted by master data screening. Based on the target intelligent fusion detection method assisted by master data screening described in the above embodiment, an embodiment of the present invention also provides a target intelligent fusion detection system assisted by master data screening corresponding to this method.

[0127] Figure 4 This is a block diagram of the structure of a system for intelligent fusion detection of targets assisted by master data screening, provided in an embodiment of the present invention. In this embodiment, the system 400 can be divided into multiple functional modules based on the functions they perform. A module, as used herein, refers to a series of computer program segments that can be executed by at least one processor and perform fixed functions, and is stored in a memory.

[0128] The main data sorting module 410 is used to obtain the detection data of K distance units to be detected as the main data Z = [z1, z2, ..., z K ], obtain the detection data of R distance units around the distance unit to be detected as auxiliary data Z R =[z′1,z′2,…,z′ R], using auxiliary data Z R Obtain auxiliary data covariance matrix estimate Covariance estimation based on auxiliary data Sort the master data Z by the generalized inner product.

[0129] The scattering point number estimation module 420 is used to perform scattering point estimation on the sorted master data Z to obtain an estimate of the number of unknown scattering points within the distance unit to be detected.

[0130] The detection statistics calculation module 430 is used to estimate the number of unknown scattering points Differentiate the master data Z and obtain the master data estimate containing the target scattering points and the main data estimation without target scatter points And use Z R 、 and Obtain a joint estimate of the clutter covariance matrix Joint estimation of the clutter covariance matrix based on the generalized likelihood ratio detection criterion Get the range-extended object detection statistics.

[0131] The target existence determination module 440 is configured to determine whether the range-extended target detection statistic is greater than or equal to a predetermined threshold. If so, it is determined that a target exists within the range unit to be detected; otherwise, it is determined that a target does not exist within the range unit to be detected.

[0132] The target intelligent fusion detection system assisted by master data screening in this embodiment is used to implement the aforementioned target intelligent fusion detection method assisted by master data screening. Therefore, the specific implementation method of this system can be seen in the embodiment part of the target intelligent fusion detection method assisted by master data screening in the previous text. Therefore, its specific implementation method can refer to the description of the corresponding embodiments of each part, and will not be elaborated here.

[0133] In addition, since the master data screening assisted target intelligent fusion detection system of this embodiment is used to implement the aforementioned master data screening assisted target intelligent fusion detection method, its function corresponds to that of the above method and will not be repeated here.

[0134] Figure 5 This is a schematic diagram of the structure of a terminal 500 provided in an embodiment of the present invention, comprising: a processor 510, a memory 520, and a communication unit 530. The processor 510 is configured to implement the process steps of the embodiment of the master data screening-assisted target intelligent fusion detection method when executing the master data screening-assisted target intelligent fusion detection program stored in the memory 520.

[0135] The terminal 500 includes a processor 510, a memory 520, and a communication unit 530. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention; it may be a bus structure or a star structure, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0136] The memory 520 can be used to store execution instructions of the processor 510. The memory 520 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. When the execution instructions in the memory 520 are executed by the processor 510, the terminal 500 can perform some or all of the steps in the following method embodiments.

[0137] The processor 510 is the control center of the storage terminal. It uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 520, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 510 can only include a central processing unit (CPU). In an embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0138] The communication unit 530 is configured to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals, or send user data to other terminals.

[0139] The present invention also provides a computer storage medium, where the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0140] The present invention also provides a computer storage medium, where the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0141] The computer storage medium stores a master data screening assisted target intelligent fusion detection program, which, when executed by the processor, implements the process steps of the master data screening assisted target intelligent fusion detection method embodiment.

[0142] Those skilled in the art will clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or other medium that can store program code, and includes a number of instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention.

[0143] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0145] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0146] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein, but is intended to be construed in the widest manner consistent with the principles and novel features disclosed herein.

Claims

1. A target intelligent fusion detection method assisted by master data screening, characterized in that: The following steps are involved: S1, obtain the detection data of K distance units to be detected as the main data Z = [z1, z2, ..., z K ], obtain the detection data of R distance units around the distance unit to be detected as auxiliary data Z R =[z′1,z′2,…,z′ R ], using auxiliary data Z R Obtain auxiliary data covariance matrix estimate Covariance estimation based on auxiliary data Sort the master data Z by the generalized inner product; S2, perform scattering point estimation on the sorted master data Z to obtain the estimated number of unknown scattering points in the distance unit to be detected S3, using the unknown number of scattering points to estimate Differentiate the master data Z and obtain the master data estimate containing the target scattering points and the main data estimation without target scatter points And use Z R 、 and Obtain a joint estimate of the clutter covariance matrix Joint estimation of the clutter covariance matrix based on the generalized likelihood ratio detection criterion Obtain range-extended target detection statistics; S4, judging whether the range-extended target detection statistic is greater than or equal to a predetermined threshold; if so, judging that a target exists in the range unit to be detected; otherwise, judging that no target exists in the range unit to be detected.

2. The target intelligent fusion detection method assisted by master data screening according to claim 1 is characterized in that: Covariance estimation based on auxiliary data Sort the master data Z by the generalized inner product, specifically including: Covariance estimation based on auxiliary data The generalized inner product statistic is established for the master data Z, and the generalized inner product statistic δ of the i-th master data is i It can be expressed as, Where i = 1, 2, .., K; For the main data, according to the generalized inner product statistic δ i Sort from large to small, assuming that the i-th statistic after sorting is δ (i) , then the main data Z is sorted and re-expressed as Z=[z (1) ,z (2) ,…,z (K) ].

3. The target intelligent fusion detection method assisted by master data screening according to claim 2 is characterized in that: Step S2 specifically includes: S21, assuming that there are an unknown number of target scattering points p in the master data Z, that is, a multiple hypothesis H with an unknown number of scattering points is formed. p , in H p Assume that Z p =[z (1) ,z (2) ,…,z (p) ] represents the main data containing the target scattering point, ZZ p =[z (p+1) ,z (p+2) ,…,z (K) ] represents the main data without target scattering points; S22, assuming that the target scattering point master data Z p =[z (1) ,z (2) ,…,z (p) ] and master data ZZ without target scattering points p =[z (p+1) ,z (p+2) ,…,z (K) ] is known, and is represented by M p and M R Under this assumption, the maximum value of K likelihood functions is optimized when the number of scattering points is unknown, and the M p and M R Estimation expression of the number of scattering points; S23, using the master data Z containing the target scattering points p Calculate the covariance matrix estimate of the main data containing the target scatter points Using auxiliary data Z R Obtain master data ZZ without target scattering points p The covariance matrix estimate of S24, use and Replace M in the scattering point number estimation expression p and M R , obtain the estimated number of unknown scattering points within the distance unit to be detected 4. The target intelligent fusion detection method assisted by master data screening according to claim 3 is characterized in that: Under this assumption, the maximum value of K likelihood functions is optimized when the number of scattering points is unknown, and the M p and M R The estimated expression of the number of scattering points includes: The joint conditional probability density function of Z of the master data is expressed as, Where det(·) represents the determinant of the matrix, and exp(·) represents the exponential function with the natural coefficient e as the base; In order to make f(Z|M R ,M p ; p) takes the largest value, Expressed as That is, the estimated expression for the number of scattering points; Furthermore, the number of unknown scattering points is estimated Expressed as 5. The target intelligent fusion detection method assisted by master data screening according to claim 3 is characterized in that: Using the master data Z containing the target scattering points p Calculate the covariance matrix estimate of the main data containing the target scatter points Specifically include: Assume M p Satisfies the noise additive structure, and the noise power is 1, that is, M p =I N +M q , where I N is the N×N dimensional identity matrix, M q is an N×N dimensional unknown matrix, where N is the number of channels of the radar system; Then M p The fast maximum likelihood estimation of is expressed as, Among them, Φ p and Λ p Respectively The eigenvector matrix and eigenvalue diagonal matrix after eigenvalue decomposition, Λ p =Diag(λ1,λ2,…,λ N ) is loaded with eigenvalues ​​smaller than the noise power to a size equal to the noise power to obtain the reconstruction matrix 6. The method for intelligent target fusion detection assisted by master data screening according to claim 1 is characterized in that: Joint estimation of clutter covariance matrix Expressed as, Where s is the known target normalized steering vector.

7. The method for intelligent fusion detection of targets assisted by master data screening according to any one of claims 1 to 6, characterized in that: The distance extended target detection statistic is expressed as, 8. A target intelligent fusion detection system assisted by master data screening, characterized in that: include: The main data sorting module is used to obtain the detection data of K distance units to be detected as the main data Z = [z1, z2, ..., z K ], obtain the detection data of R distance units around the distance unit to be detected as auxiliary data Z R =[z′1,z′2,…,z′ R ], using auxiliary data Z R Obtain auxiliary data covariance matrix estimate Covariance estimation based on auxiliary data Sort the master data Z by the generalized inner product; The scattering point number estimation module is used to estimate the scattering points of the sorted main data Z and obtain the estimated number of unknown scattering points in the distance unit to be detected. Detection statistics calculation module, used to estimate the number of unknown scattering points Differentiate the master data Z and obtain the master data estimate containing the target scattering points and the main data estimation without target scatter points And use Z R 、 and Obtain a joint estimate of the clutter covariance matrix Joint estimation of the clutter covariance matrix based on the generalized likelihood ratio detection criterion Obtain range-extended target detection statistics; The target existence judgment module is used to judge whether the range-extended target detection statistic is greater than or equal to a predetermined threshold. If so, it is determined that a target exists in the range unit to be detected; otherwise, it is determined that no target exists in the range unit to be detected.

9. A terminal, characterized in that: include: A memory for storing a target intelligent fusion detection program assisted by master data screening; A processor is used to implement the steps of the master data screening assisted target intelligent fusion detection method as described in any one of claims 1 to 7 when executing the master data screening assisted target intelligent fusion detection program.

10. A computer-readable storage medium, characterized in that The readable storage medium stores a target intelligent fusion detection program assisted by master data screening, and when the target intelligent fusion detection program assisted by master data screening is executed by the processor, it implements the steps of the target intelligent fusion detection method assisted by master data screening as described in any one of claims 1 to 7.

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