Radar target robust intelligent detection and interference adaptive suppression method and system

Abstract

The invention relates to the technical field of broadband radar signals, and provides a radar target robust intelligent detection and interference adaptive suppression method and system, and the method comprises the steps: carrying out the decorrelation transformation of received data and a parameter matrix through employing the skew symmetry characteristic of a clutter covariance matrix, and completing the preprocessing of initial data; carrying out correlation calculation estimation under the condition of no target hypothesis and under the condition of target hypothesis, and constructing distance extension target Gradient detection statistics under the condition of a known clutter skew symmetry covariance matrix; constructing a detection statistic lambda of the radar target robust intelligent detection and interference adaptive suppression method; and determining a detection threshold T through theoretical calculation or Monte Carlo simulation according to a preset false alarm probability, comparing the detection statistic lambda with the detection threshold T, and judging whether a distance expansion target exists in the current distance unit to be detected or not according to a comparison result. According to the method, an analytical expression design is adopted, a complex iterative calculation process is avoided, and the operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of broadband radar signals, and in particular to a method and system for robust intelligent detection and adaptive interference suppression of radar targets. Background Art

[0002] As radar bandwidth increases, its range resolution further improves. Wideband radar is widely used in modern military and civilian applications, including anti-interference, counter-reconnaissance, precision detection and imaging, high-precision tracking, and target identification. Adaptive detection of range-extended targets with wideband radar has become a hot topic in the radar community. Unlike narrowband radar, where target echo signals typically occupy only one range resolution unit, with wideband radar, target scattering energy can spread across adjacent range units, appearing as a "one-dimensional range profile," creating a range-extended target. If the point target detection method is still used to detect the target on the echo signal of a single range unit, and the statistical characteristics of the background clutter are estimated by sampling the adjacent range units; on the one hand, the energy of the strong scattering point of the range-extended target is easy to leak into the adjacent range units, resulting in signal pollution, and further forming a masking effect on the target signal of the single range unit to be detected, making the point target detection method ineffective; on the other hand, in practical applications, radar detection faces a complex electromagnetic environment, which may have natural or man-made interference sources such as electronic countermeasure signals or various civilian electromagnetic signals. In addition, the environment in which the target is located is complex and changeable, which enhances the non-uniformity of the background clutter. The number of pure clutter training data that meets the independent and identical distribution is relatively limited. Compared with narrowband radar, this problem is particularly prominent in the target detection scenario of wideband radar, making it difficult for existing range-extended target detection methods to achieve ideal detection results.

[0003] Furthermore, in commonly used rank-one signal target detection models, the target's steering vector is typically assumed to be a known fixed vector. However, in practical applications, due to beam pointing errors and multipath, the target's steering vector may be mismatched. To address this issue, a subspace model can be considered for modeling the target signal. In a subspace model, the signal is represented as the product of a known subspace matrix and an unknown coordinate matrix. If subspace modeling is employed for the target and interfering signals based on a comprehensive dataset consisting of test and training data from multiple range bins to be detected, and the detection statistic is constructed using the GLRT test criterion, a subspace GLRT detector (abbreviated as 2S-GLRT-HE) for robust and intelligent radar target detection can be obtained. However, the construction of the GLRT detector involves solving the maximum likelihood estimation of unknown parameters under both the target and no target assumptions, resulting in a complex and computationally complex detector construction process. Furthermore, given the difficulty in obtaining sufficient pure clutter training data in real environments, the clutter covariance matrix of radar receivers using centrosymmetric linear arrays or centrosymmetric spaced pulse trains exhibits a unique skew-symmetric structure. Utilizing skew-symmetric structural information can often improve detector performance and reduce the amount of training data required. Taking into account skew-symmetric prior information, if subspace modeling is adopted and the Rao test criterion is used to construct detection statistics, a skew-symmetric subspace Rao detector (abbreviated as P2S-Rao-HE) for robust intelligent detection of radar targets can be obtained. However, in practical implementation, this approach faces the challenge of solving the Fisher information matrix, and the detector construction process is also relatively complex.

[0004] In the presence of external interference and a small amount of uniform training data, multi-channel broadband radar range-extended target detection is difficult to strike a balance between computational complexity and detection performance. How to fully utilize the skew-symmetric structure information, reduce the actual demand for training data volume, improve the estimation accuracy of the unknown clutter covariance matrix, and then construct a closed-form intelligent detection method for range-extended targets. While maintaining the constant false alarm rate (CFAR) characteristics, it effectively suppresses interference signals and strikes an effective balance between computational complexity and detection performance. This is the key to improving the detection capability of broadband radar in complex interference environments, and is also one of the difficulties faced by multi-channel broadband radar range-extended target adaptive detection. Summary of the Invention

[0005] The purpose of the present invention is to solve at least one technical problem in the background technology and provide a method and system for robust intelligent detection and adaptive interference suppression of radar targets.

[0006] To achieve the above objectives, the present invention provides a method for robust intelligent detection of radar targets and adaptive interference suppression, comprising:

[0007] Test data Z is collected from K test range cells, and training data Y is extracted from R adjacent reference cells. Utilizing the skew symmetry of the clutter covariance matrix, a decorrelation transformation is performed on the test data Z, the clutter covariance matrix M, the target signal coordinate matrix P, and the interference signal coordinate matrix Q to complete the initial data preprocessing.

[0008] In the absence of target assumption, solve the maximum likelihood estimation of the interference signal coordinate matrix Q; in the presence of target assumption, solve the target parameter vector Θ rp-1s The maximum likelihood estimate of , and the target parameter vector Θ is estimated using the complex Gaussian probability density function of the test data Z rp-1s Obtain the partial derivative, and then substitute the maximum likelihood estimate of the interference signal coordinate matrix Q obtained under the no-target assumption into the derivative result to obtain the calculation result; substitute the calculation result and the maximum likelihood estimate of the target parameter vector under the target assumption into the range-extended target gradient detection statistic, and construct the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix; finally, obtain the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data, and substitute the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix, replacing the unknown clutter skew-symmetric covariance matrix, and construct the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method;

[0009] The detection threshold T is determined by theoretical calculation or Monte Carlo simulation based on the preset false alarm probability. The detection statistic λ is compared with the detection threshold T to determine whether there is a range-extended target in the current range unit to be detected.

[0010] According to one aspect of the present invention, collecting test data Z through K distance units to be measured and extracting training data Y from adjacent R reference units includes:

[0011] The radar system collects test data Z from K adjacent range units. After appropriate sampling and organization, it forms an N×1 dimensional vector, which is represented by z t ∈￡ N×1 ,t=1,2,...,K, where N represents the product of the number of antenna elements and the number of pulses, represents a set of N×1 dimensional complex matrices; the disturbance in the test data consists of clutter components and interference components, which are represented by c t ∈￡ N×1 and j t ∈￡ N×1 ,t=1,2,...,K;set the interference component j t ,t=1,2,...,K is modeled as a deterministic subspace signal, belonging to the known multi-rank subspace J∈￡N×q , denoted as j t =Jq t , where q t ∈￡ q×1 , t=1,2,...,K represents the unknown complex coordinate vector of the interference signal; and the clutter component c t , t=1,2,...,K are independent and identically distributed among different distance units and obey the zero-mean circularly symmetric complex Gaussian distribution with covariance matrix M, that is, c t ~CN(0 N×1 ,M); where M is an unknown Hermitian positive definite matrix; in addition, set the target signal s t ,t=1,2,...,K is also modeled as a deterministic subspace signal, belonging to the known multi-rank subspace H∈￡ N×p , denoted as s t =Hp t , where p t ∈￡ p×1 , t=1,2,...,K represents the unknown complex coordinate vector of the target signal;

[0012] To facilitate derivation, define the augmented matrix In order to estimate the unknown clutter covariance matrix M, it is assumed that there is a set of training data containing only clutter components, which is usually collected from R distance units adjacent to the distance unit to be measured, and is recorded as Y = [y1, y2, ..., y R ]∈￡ N×R , where y l =c l ,l=1,2,...,R is also independent and identically distributed and satisfies c l ~CN(0 N×1 ,M).

[0013] According to one aspect of the present invention, the skew symmetry of the clutter covariance matrix is utilized to perform decorrelation transformation on the received data and the parameter matrix to complete the initial data preprocessing, including:

[0014] Assuming that the clutter covariance matrix M is known, the two-step gradient test decision formula of the complex signal can be expressed as

[0015]

[0016] Among them, λ 2S-Gradient and T 2S-Gradient Represent the test statistic and threshold respectively; test data Z=[z1,z2,...,z K ]∈￡ N×K , target signal coordinate matrix P=[p1,p2,...,p K ]∈￡ p×K, interference signal coordinate matrix Q=[q1,q2,...,q K ]∈￡ q×K ;Unknown parameter set The relevant parameter Θ r =vec(P)∈￡ pK×1 , interference parameters represents the maximum likelihood estimate of Θ under the assumption H0, Represents Θ r The maximum likelihood estimate under the H1 assumption, Θ r0 Represents Θ r True value under the H0 hypothesis; superscript (·) T represents the transpose, and the vec(·) function realizes the vectorization of the matrix; it should be noted that under the H0 assumption, the target does not exist, so Θ r0 =0 pK×1 .f i (Z|iP,Q) is the conditional probability density function of the test data Z under the H0 hypothesis or H1, expressed as:

[0017] f i (Z|iP,Q)={π N(K+R) |M| K+R} -1 ×exp[-tr(M -1 T i )],i=0,1 (2)

[0018] Among them, T i =(ZB i D)(ZB i D) H , and define the augmented matrix B i =[iH,J] and D = [P T ,Q T ] T ; |·| represents the determinant of a square matrix, the tr(·) function represents the trace of a square matrix, the exp(·) function represents the exponential operation, and the superscript (·) H represents the conjugate transpose; the condition p+q≤N must be satisfied to ensure that B=[H,J] is a column-full rank augmented matrix; in addition, by utilizing the skew symmetry of the clutter covariance matrix M, it can be derived that:

[0019] tr(M -1 T i )=tr[D N (M * ) -1 D N T i ]=tr(M -1 D N T i* D N ),i=0,1 (3)

[0020] Among them, the superscript (·) * represents conjugation; therefore, Equation (2) can be rewritten as:

[0021] f i (Z|iP,Q)={π N(K+R) |M| K+R} -1 ×exp[-tr(M -1 T ip )],i=0,1 (4)

[0022] Among them, T ip =(T i +D N T i * D N ) / 2=(Z p -B i D p )(Z p -B i D p ) H ,and

[0023]

[0024] In the above formula, and represent the real and imaginary parts respectively, and

[0025]

[0026] According to one aspect of the present invention, in the absence of a target hypothesis, the maximum likelihood estimation of the interference signal coordinate matrix Q is solved; in the presence of a target hypothesis, the target parameter vector Θ is solved. rp-1s The maximum likelihood estimate of , and the target parameter vector Θ is estimated using the complex Gaussian probability density function of the test data Z rp-1sObtain the partial derivative, and then substitute the maximum likelihood estimate of the interference signal coordinate matrix Q obtained under the no-target assumption into the derivative result to obtain the calculation result; substitute the calculation result and the maximum likelihood estimate of the target parameter vector under the target assumption into the range-extended target gradient detection statistic, and construct the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix; finally, obtain the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data, and substitute the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix, replacing the unknown clutter skew-symmetric covariance matrix, and construct the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method, including:

[0027] Assuming that the clutter covariance matrix M is known, the skew-symmetric two-step gradient test decision formula can be restated as:

[0028]

[0029] in, express The maximum likelihood estimate under the H0 hypothesis, Θ rp-2s =vec(P p )∈￡ 2pK×1 ,Θ sp-2s =vec(Q p )∈￡ (2qK+1)×1 , Represents Θ rp-2s In the maximum likelihood estimation under the H1 hypothesis, the conditional probability density function of the test data Z can be expressed as:

[0030]

[0031] Under the assumption H0, let equation (8) be differentiated with respect to the interference signal coordinate matrix Q and set the result to zero. The maximum likelihood estimate of the interference signal coordinate matrix Q under the given clutter covariance matrix M can be obtained:

[0032]

[0033] in, Under the H1 assumption, let the natural logarithm of equation (8) be rp-2s Taking partial derivatives, we can get:

[0034]

[0035] Substitute equation (9) into Further:

[0036]

[0037] in, Represents projection to matrix The orthogonal projection matrix of the column space of the orthogonal complement space; Next, let the natural logarithm of formula (8) be D p Taking the partial derivative, we can get D p The maximum likelihood estimate under the H1 hypothesis is:

[0038]

[0039] in, because Under the condition of given clutter covariance matrix M, we can get P p The maximum likelihood estimate under the H1 hypothesis is:

[0040]

[0041] We can further obtain:

[0042]

[0043] Substituting Equations (11) and (14) into Equation (7), and then performing algebraic processing and simplification, we can obtain the Gradient test statistic when the clutter covariance matrix M is given:

[0044]

[0045] in, Represents projection to matrix The orthogonal projection matrix of the column space;

[0046] The maximum likelihood estimate of the unknown skew-symmetric covariance matrix M is obtained using the training data, which is exactly the same as the maximum likelihood estimate in formula (5) Then, replace M in formula (15) with The final Gradient test statistic is:

[0047]

[0048] According to one aspect of the present invention, a detection threshold T is determined by theoretical calculation or Monte Carlo simulation based on a preset false alarm probability, and a detection statistic λ is compared with the detection threshold T. It is determined whether a range-extended target exists in the current range unit to be detected based on the comparison result, including:

[0049] When λ≥T, it is determined that there is a range-extended target in the current range unit to be detected, and the test data is marked as detected and excluded from the training data update process of the subsequent sliding window;

[0050] When λ < T, it is determined that there is no range - extended target in the current range cell to be detected, and the current test data is incorporated into the training data set as a valid sample for updating the reference cell of subsequent detection range cells.

[0051] To achieve the above object, the present invention further provides a radar target robust intelligent detection and interference adaptive suppression system, including:

[0052] A data pre - processing module collects test data Z through K range cells to be measured, and extracts training data Y from R adjacent reference cells; utilizes the skew - symmetric property of the clutter covariance matrix to perform decorrelation transformation on the test data Z, the clutter covariance matrix M, the target signal coordinate matrix P, and the interference signal coordinate matrix Q to complete the initial data pre - processing;

[0053] A detection statistic construction module, under the hypothesis of no target, solves the maximum likelihood estimate of the interference signal coordinate matrix Q; under the hypothesis of having a target, solves the maximum likelihood estimate of the target parameter vector Θ rp-1s and takes the partial derivative of the target parameter vector Θ rp-1s using the complex Gaussian probability density function of the test data Z, and then substitutes the maximum likelihood estimate of the interference signal coordinate matrix Q obtained under the no - target hypothesis into the derivative result to obtain a calculation result; substitutes the calculation result and the maximum likelihood estimate of the target parameter vector under the target hypothesis into the range - extended target Gradient detection statistic to construct the range - extended target Gradient detection statistic under the condition of a known clutter skew - symmetric covariance matrix; finally, obtains the maximum likelihood estimate of the clutter skew - symmetric covariance matrix based on the training data, and substitutes the maximum likelihood estimate of the clutter skew - symmetric covariance matrix into the range - extended target Gradient detection statistic under the condition of a known clutter skew - symmetric covariance matrix to replace the unknown clutter skew - symmetric covariance matrix, and constructs the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method;

[0054] A target detection decision module determines the detection threshold T through theoretical calculation or Monte Carlo simulation according to a preset false - alarm probability, and determines whether there is a range - extended target in the current range cell to be detected according to the comparison result by comparing the detection statistic λ with the detection threshold T.

[0055] To achieve the above object, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor, and when the computer program is executed by the processor, it implements the radar target robust intelligent detection and interference adaptive suppression method as described above.

[0056] To achieve the above-mentioned object, the present invention also provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for robust intelligent detection of radar targets and adaptive interference suppression as described above is implemented.

[0057] According to the solution of the present invention, the present invention can achieve the following beneficial effects:

[0058] 1) The robust intelligent detection of radar targets and the adaptive interference suppression method are designed using analytical expressions, avoiding complex iterative calculation processes and significantly improving computational efficiency;

[0059] 2) Compared with methods such as Rao detection that require explicit calculation of complex Fisher information matrices, the method of the present invention significantly reduces the computational complexity, has more real-time advantages in engineering applications, and is easier to deploy and implement.

[0060] 3) By deeply exploring the skew symmetry of the clutter covariance matrix, we improve the matrix estimation method, which not only improves the estimation accuracy but also significantly reduces the requirement for the amount of training data, providing an efficient solution for range-extended target detection under small sample conditions.

[0061] 4) For structured interference environments, the proposed detector can adaptively suppress interference signals of varying strengths while maintaining robust detection performance for mismatched signals, significantly improving detection reliability in complex electromagnetic environments.

[0062] 5) The method of the present invention can be effectively extended to some non-wideband radar detection scenarios, and is particularly suitable for the following two typical applications: one is the robust detection of large-scale targets by low / medium-resolution radars; the other is the efficient detection of point target groups with the same movement speed and dense spatial distribution (such as ship formations, aircraft formations, vehicle formations, etc.), which has important engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flowchart schematically illustrating a method for robust intelligent detection of radar targets and adaptive interference suppression according to an embodiment of the present invention;

[0064] Figure 2 1 is a comparison chart of the detection performance of the method of Example 1 of the present invention and the existing detection method under matching environments;

[0065] Figure 3 3 is a comparison chart of the detection performance of the method of Example 2 of the present invention and the existing detection method in a mismatch environment. DETAILED DESCRIPTION

[0066] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only intended to enable those skilled in the art to better understand and implement the present invention, rather than to imply any limitation on the scope of the present invention.

[0067] As used herein, the term "including" and variations thereof are to be interpreted as open-ended terms meaning "including, but not limited to." The term "based on" is to be interpreted as "based, at least in part, on." The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment."

[0068] Figure 1 The flowchart of the radar target robust intelligent detection and interference adaptive suppression method according to one embodiment of the present invention is schematically shown. Figure 1 As shown, in this embodiment, the radar target robust intelligent detection and interference adaptive suppression method includes:

[0069] Test data Z is collected from K test range cells, and training data Y is extracted from R adjacent reference cells. Using the skew symmetry of the clutter covariance matrix, the test data Z, the clutter covariance matrix M, the target signal coordinate matrix P, and the interference signal coordinate matrix Q are subjected to decorrelation transformation to complete the initial data preprocessing.

[0070] In the absence of target assumption, solve the maximum likelihood estimation of the interference signal coordinate matrix Q; in the presence of target assumption, solve the target parameter vector Θ rp-1s The maximum likelihood estimate of , and the target parameter vector Θ is estimated using the complex Gaussian probability density function of the test data Z rp-1s Obtain the partial derivative, and then substitute the maximum likelihood estimate of the interference signal coordinate matrix Q obtained under the no-target assumption into the derivative result to obtain the calculation result; substitute the calculation result and the maximum likelihood estimate of the target parameter vector under the target assumption into the range-extended target gradient detection statistic, and construct the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix; finally, obtain the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data, and substitute the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix, replacing the unknown clutter skew-symmetric covariance matrix, and construct the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method;

[0071] The detection threshold T is determined by theoretical calculation or Monte Carlo simulation based on the preset false alarm probability. The detection statistic λ is compared with the detection threshold T to determine whether there is a range-extended target in the current range unit to be detected.

[0072] Furthermore, according to an embodiment of the present invention, collecting test data Z through K distance units to be measured and extracting training data Y from adjacent R reference units includes:

[0073] The radar system collects test data Z from K adjacent range units. After appropriate sampling and organization, it forms an N×1 dimensional vector, which is represented by z t ∈￡ N×1 ,t=1,2,...,K, where N represents the product of the number of antenna elements and the number of pulses, represents a set of N×1 dimensional complex matrices; the disturbance in the test data consists of clutter components and interference components, which are represented by c t ∈￡ N×1 and j t ∈￡ N×1 ,t=1,2,...,K;set the interference component j t ,t=1,2,...,K is modeled as a deterministic subspace signal, belonging to the known multi-rank subspace J∈￡ N×q , denoted as j t =Jq t , where q t ∈￡ q×1 , t=1,2,...,K represents the unknown complex coordinate vector of the interference signal; and the clutter component c t , t=1,2,...,K are independent and identically distributed among different distance units and obey the zero-mean circularly symmetric complex Gaussian distribution with covariance matrix M, that is, c t ~CN(0 N×1 ,M); where M is an unknown Hermitian positive definite matrix; in addition, set the target signal s t ,t=1,2,...,K is also modeled as a deterministic subspace signal, belonging to the known multi-rank subspace H∈￡ N×p , denoted as s t =Hp t , where p t ∈￡ p×1 , t=1,2,...,K represents the unknown complex coordinate vector of the target signal;

[0074] To facilitate derivation, define the augmented matrix In order to estimate the unknown clutter covariance matrix M, it is assumed that there is a set of training data containing only clutter components, which is usually collected from R distance units adjacent to the distance unit to be measured, and is recorded as Y = [y1, y2, ..., y R ]∈￡ N×R , where y l =c l ,l=1,2,...,R is also independent and identically distributed and satisfies c l~CN(0 N×1 ,M).

[0075] Furthermore, according to an embodiment of the present invention, a decorrelation transformation is performed on the received data and the parameter matrix using the skew symmetry of the clutter covariance matrix to complete the initial data preprocessing, including:

[0076] Assuming that the clutter covariance matrix M is known, the two-step gradient test decision formula of the complex signal can be expressed as

[0077]

[0078] Among them, λ 2S-Gradient and T 2S-Gradient Represent the test statistic and threshold respectively; test data Z=[z1,z2,...,z K ]∈￡ N×K , target signal coordinate matrix P=[p1,p2,...,p K ]∈￡ p×K , interference signal coordinate matrix Q=[q1,q2,...,q K ]∈￡ q×K ;Unknown parameter set The relevant parameter Θ r =vec(P)∈￡ pK×1 , interference parameters represents the maximum likelihood estimate of Θ under the assumption H0, Represents Θ r The maximum likelihood estimate under the H1 assumption, Θ r0 Represents Θ r True value under the H0 hypothesis; superscript (·) T represents the transpose, and the vec(·) function realizes the vectorization of the matrix; it should be noted that under the H0 assumption, the target does not exist, so Θ r0 =0 pK×1 .f i (Z|iP,Q) is the conditional probability density function of the test data Z under the H0 hypothesis or H1, expressed as:

[0079] f i (Z|iP,Q)={π N(K+R) |M| K+R} -1 ×exp[-tr(M -1 T i )],i=0,1 (2)

[0080] Among them, T i =(ZB i D)(ZB i D) H, and define the augmented matrix B i =[iH,J] and D = [P T ,Q T ] T ; |·| represents the determinant of a square matrix, the tr(·) function represents the trace of a square matrix, the exp(·) function represents the exponential operation, and the superscript (·) H represents the conjugate transpose; the condition p+q≤N must be satisfied to ensure that B=[H,J] is a column-full rank augmented matrix; in addition, by utilizing the skew symmetry of the clutter covariance matrix M, it can be derived that:

[0081] tr(M -1 T i )=tr[D N (M * ) -1 D N T i ]=tr(M -1 D N T i * D N ),i=0,1 (3)

[0082] Among them, the superscript (·) * represents conjugation; therefore, Equation (2) can be rewritten as:

[0083] f i (Z|iP,Q)={π N(K+R) |M| K+R} -1 ×exp[-tr(M -1 T ip )],i=0,1 (4)

[0084] Among them, T ip =(T i +D N T i * D N ) / 2=(Z p -B i D p )(Z p -B i D p ) H ,and

[0085]

[0086] In the above formula, and represent the real and imaginary parts respectively, and

[0087]

[0088] Further, according to an embodiment of the present invention, in the absence of a target assumption, the maximum likelihood estimation of the interference signal coordinate matrix Q is solved; in the presence of a target assumption, the target parameter vector Θ is solved. rp-1s The maximum likelihood estimate of , and the target parameter vector Θ is estimated using the complex Gaussian probability density function of the test data Z rp-1s Obtain the partial derivative, and then substitute the maximum likelihood estimate of the interference signal coordinate matrix Q obtained under the no-target assumption into the derivative result to obtain the calculation result; substitute the calculation result and the maximum likelihood estimate of the target parameter vector under the target assumption into the range-extended target gradient detection statistic, and construct the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix; finally, obtain the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data, and substitute the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix, replacing the unknown clutter skew-symmetric covariance matrix, and construct the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method, including:

[0089] Assuming that the clutter covariance matrix M is known, the skew-symmetric two-step gradient test decision formula can be restated as:

[0090]

[0091] in, express The maximum likelihood estimate under the H0 hypothesis, Θ rp-2s =vec(P p )∈￡ 2pK×1 ,Θ sp-2s =vec(Q p )∈￡ (2qK+1)×1 , Represents Θ rp-2s In the maximum likelihood estimation under the H1 hypothesis, the conditional probability density function of the test data Z can be expressed as:

[0092]

[0093] Under the assumption H0, let equation (8) be differentiated with respect to the interference signal coordinate matrix Q and set the result to zero. The maximum likelihood estimate of the interference signal coordinate matrix Q under the given clutter covariance matrix M can be obtained:

[0094]

[0095] in, Under the H1 assumption, let the natural logarithm of equation (8) berp-2s Taking partial derivatives, we can get:

[0096]

[0097] Substitute equation (9) into Further:

[0098]

[0099] in, Represents projection to matrix The orthogonal projection matrix of the column space of the orthogonal complement space; Next, let the natural logarithm of formula (8) be D p Taking the partial derivative, we can get D p The maximum likelihood estimate under the H1 hypothesis is:

[0100]

[0101] in, because Under the condition of given clutter covariance matrix M, we can get P p The maximum likelihood estimate under the H1 hypothesis is:

[0102]

[0103] We can further obtain:

[0104]

[0105] Substituting Equations (11) and (14) into Equation (7), and then performing algebraic processing and simplification, we can obtain the Gradient test statistic when the clutter covariance matrix M is given:

[0106]

[0107] in, Represents projection to matrix The orthogonal projection matrix of the column space;

[0108] The maximum likelihood estimate of the unknown skew-symmetric covariance matrix M is obtained using the training data, which is exactly the same as the maximum likelihood estimate in formula (5) Then, replace M in formula (15) with The final Gradient test statistic is:

[0109]

[0110] Further, according to an embodiment of the present invention, to ensure the effectiveness of constant false alarm rate (CFAR) detection, a detection threshold T is determined through theoretical calculation or Monte Carlo simulation based on a preset false alarm probability. By comparing the detection statistic λ with the detection threshold T, it is determined whether there is a range extended target in the currently to-be-detected range cell according to the comparison result, including:

[0111] When λ≥T, it is determined that there is a range extended target in the currently to-be-detected range cell, and at the same time, the test data is marked as the detected state and excluded from the subsequent training data update process of the sliding window;

[0112] When λ<T, it is determined that there is no range extended target in the currently to-be-detected range cell, and the current test data is used as a valid sample and incorporated into the training data set for the update of the reference cell of the subsequent to-be-detected range cell.

[0113] According to the above solution of the present invention, for the problem that the existing wideband radar range extended target adaptive detector under a structured interference background is difficult to balance the CFAR characteristics, detection performance and mismatch robustness, and considering the problem that it is difficult to obtain pure clutter training data due to actual clutter non-uniformity, how to fully exploit the structure information of the clutter covariance matrix, further reduce the requirement for the amount of training data, improve the estimation accuracy of the unknown clutter covariance matrix, and then construct a radar target robust intelligent detection and interference adaptive suppression method with a closed form, while ensuring the CFAR characteristics, taking into account the multi-faceted requirements such as the intelligent anti-interference, detection performance and mismatch robustness of the range extended target adaptive detection algorithm, and enhancing the adaptive detection performance of multi-channel wideband radar for weak targets in a complex interference environment.

[0114] According to the above solution of the present invention, the method of the present invention constructs a radar target robust intelligent detection and interference adaptive suppressor. It can be seen from Equation (16) that the proposed radar target robust intelligent detection and interference adaptive suppression method avoids the iterative calculation process in the traditional method by establishing an explicit detection statistic expression, significantly improving the operation efficiency. Compared with the P2S-Rao-HE detector, this method significantly reduces the implementation complexity by avoiding the calculation of the Fisher information matrix and enhances the engineering applicability at the same time. Additionally, it is worth noting that compared with the 2S-GLRT-HE detector for range extended targets, the radar target robust intelligent detection and interference adaptive suppression method has a lower requirement for the amount of training data and stronger detection robustness for the steering vector mismatch signal. Generally speaking, the radar target robust intelligent detection and interference adaptive suppression method of the present invention can effectively balance the algorithm computational complexity, mismatch robustness and detection performance while maintaining the CFAR characteristics.

[0115] Furthermore, to achieve the above-mentioned object, the present invention also provides a radar target robust intelligent detection and interference adaptive suppression system, comprising:

[0116] The data preprocessing module collects test data Z from K test range cells and extracts training data Y from R adjacent reference cells. It uses the skew symmetry of the clutter covariance matrix to perform decorrelation transformation on the test data Z, the clutter covariance matrix M, the target signal coordinate matrix P, and the interference signal coordinate matrix Q to complete the initial data preprocessing.

[0117] The detection statistic construction module solves the maximum likelihood estimation of the interference signal coordinate matrix Q in the absence of target assumptions; solves the target parameter vector Θ in the presence of target assumptions rp-1s The maximum likelihood estimate of , and the target parameter vector Θ is estimated using the complex Gaussian probability density function of the test data Z rp-1s Obtain the partial derivative, and then substitute the maximum likelihood estimate of the interference signal coordinate matrix Q obtained under the no-target assumption into the derivative result to obtain the calculation result; substitute the calculation result and the maximum likelihood estimate of the target parameter vector under the target assumption into the range-extended target gradient detection statistic, and construct the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix; finally, obtain the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data, and substitute the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix, replacing the unknown clutter skew-symmetric covariance matrix, and construct the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method;

[0118] The target detection and judgment module determines the detection threshold T through theoretical calculation or Monte Carlo simulation based on the pre-set false alarm probability, compares the detection statistic λ with the detection threshold T, and determines whether there is a range-extended target in the current range unit to be detected based on the comparison result.

[0119] The above-mentioned radar target robust intelligent detection and interference adaptive suppression system according to the present invention can implement the above-mentioned radar target robust intelligent detection and interference adaptive suppression method. The specific process steps are as described above and will not be repeated here.

[0120] Furthermore, to achieve the above-mentioned objectives, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the method for robust intelligent detection of radar targets and adaptive interference suppression as described above is implemented.

[0121] Furthermore, to achieve the above-mentioned objectives, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for robust intelligent detection of radar targets and adaptive interference suppression as described above is implemented.

[0122] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiment described herein is only an optimal embodiment of the present invention and is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0123] Example 1

[0124] Reference Figure 1 The radar target robust intelligent detection and interference adaptive suppression method of Example 1 is divided into the following steps:

[0125] Step A1: Use the sea detection radar to illuminate the sea area to be detected and obtain the test data Z of K distance units to be detected; the test data Z is converted into the skew symmetric transformation test data Z according to formula (5) and formula (6): p ; Transform the skew symmetric test data Z p According to formula (9), Q under the H0 hypothesis is obtained p The maximum likelihood estimate of According to formula (11), we can obtain the test data Z under the H1 hypothesis. p The complex Gaussian probability density function of the target parameter vector Θ r The derivative result of Θ under the H1 hypothesis is obtained according to formula (14). rp The maximum likelihood estimate of The above results are used to construct the gradient detection statistic of the range-extended target under the condition of known clutter skew-symmetric covariance matrix according to formula (18).

[0126] It is worth noting that in step A1, considering that external interference may exist in the actual ocean environment and adversely affect the adaptive detection of range-extended targets, the robust intelligent detection and adaptive interference suppression method of radar targets of the present invention takes external interference into account during the detector design process and uses subspace signals to model the interference to reduce the mismatch effect of the interference signal. In the presence of subspace-structured interference environments, the intelligent detection method of range-extended targets with gradients of the present invention can effectively suppress interference signals of varying intensities, demonstrating excellent intelligent anti-interference capabilities.

[0127] Step A2: Radar illumination is performed on the target-free range around the sea area to be detected to obtain R reference range units containing only pure sea clutter training data Y. The training data Y is used according to equations (5) and (6) to obtain the maximum likelihood estimation of the clutter skew-symmetric covariance matrix based on the training data: Will Substitute the range-extended target Gradient detection statistic obtained in step A1 and replace the unknown clutter skew-symmetric covariance matrix therein, and construct the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method according to formula (19).

[0128] Notably, in step A2, the method of the present invention achieves superior detection performance compared to detectors such as 2S-GLRT-HE and P2S-Rao-HE for range-extended targets, particularly in the absence of sufficient training data. Furthermore, the robust intelligent detection of radar targets and adaptive interference suppression method of the present invention has a closed-form expression. Compared to existing adaptive detection methods for range-extended targets, this method maintains the CFAR characteristics while striking a reasonable balance between detection performance and computational complexity, thereby enhancing the adaptive detection capability of multi-channel broadband radars for small, weak targets on the sea surface in complex electromagnetic environments.

[0129] Step A3: Set the detection threshold T according to the preset false alarm probability: Specifically, set the false alarm probability to P fa According to the Monte Carlo method, based on the 100 / P accumulated in the early stage fa The detection threshold T is calculated based on the measured sea clutter data. Considering the difficulty of obtaining sea clutter, if the actual amount of pure sea clutter measured data R is less than 100 / P fa , then the missing 100 / P fa -R clutter data can be simulated using a sea clutter simulation model, where the model parameters are reasonably estimated and set based on the obtained pure sea clutter measured data. Furthermore, the detection statistic λ is compared with the detection threshold T. If λ≥T, it is determined that the current K range cells to be detected have a range-extended target, and the test data is not used as training data for the subsequent range cells to be detected. Conversely, if λ<T, it is determined that the current K range cells to be detected do not have a range-extended target, and the test data is used as training data for the subsequent range cells to be detected.

[0130] The performance comparison results of the detectors under the target-oriented vector matching environment are shown in the attached Figure 2 The results show that compared with existing detectors for extended range targets such as 2S-GLRT-HE and P2S-Rao-HE, the proposed method has better detection performance in a matching environment.

[0131] Example 2:

[0132] Reference Figure 1The radar target robust intelligent detection and interference adaptive suppression method of Example 2 is divided into the following steps:

[0133] Step B1: Use the ground detection radar to illuminate the area to be detected and obtain the test data Z of K distance units to be detected; the test data Z is converted into the skew-symmetric transformation test data Z according to equations (5) and (6). p ; Transform the skew symmetric test data Z p According to formula (9), Q under the H0 hypothesis is obtained p The maximum likelihood estimate of According to formula (11), we can obtain the test data Z under the H1 hypothesis. p The complex Gaussian probability density function of the target parameter vector Θ r The derivative result of Θ under the H1 hypothesis is obtained according to formula (14). rp The maximum likelihood estimate of The above results are used to construct the gradient detection statistic of the range-extended target under the condition of known clutter skew-symmetric covariance matrix according to formula (18).

[0134] It is worth noting that in step B1, the present invention's robust intelligent radar target detection and adaptive interference suppression method incorporates external interference into the detector design process, taking into account the potential for external interference in actual ocean environments, which can adversely affect the adaptive detection of range-extended targets. It also uses subspace signals to model the interference to reduce the potential mismatch effects of the interference signal. In environments with subspace-structured interference, the present invention's intelligent gradient target detection method for range-extended targets can effectively suppress interference signals of varying intensities, demonstrating superior intelligent anti-interference capabilities.

[0135] Step B2: Radar illumination is performed on the target-free range around the area to be detected to obtain R reference range units containing only pure ground clutter training data Y. The training data Y is used according to equations (5) and (6) to obtain the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data: Will Substitute the range-extended target Gradient detection statistic obtained in step B1, replace the unknown clutter skew-symmetric covariance matrix, and construct the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method according to formula (19).

[0136] Notably, in step B2, the method of the present invention achieves superior detection performance compared to detectors such as 2S-GLRT-HE and P2S-Rao-HE for range-extended targets, particularly when training data is scarce. Furthermore, the robust intelligent detection of radar targets and adaptive interference suppression method of the present invention has a closed-form expression. Compared to existing adaptive detection methods for range-extended targets, this method maintains the CFAR characteristic while striking a reasonable balance between detection performance and computational complexity, thereby enhancing the adaptive detection capability of multi-channel broadband radars for small, weak ground targets in complex electromagnetic environments.

[0137] Step B3, setting the detection threshold T according to the preset false alarm probability: Specifically, setting the false alarm probability to P fa According to the Monte Carlo method, based on the 100 / P accumulated in the early stage fa The detection threshold T is calculated based on the measured ground clutter data. Considering the difficulty of obtaining ground clutter, if the amount of pure ground clutter measured data R actually obtained is less than 100 / P fa , then the missing 100 / P fa -R clutter data can be simulated using a ground clutter simulation model, where the model parameters are reasonably estimated and set based on the obtained pure ground clutter measured data. Furthermore, the detection statistic λ is compared with the detection threshold T. If λ≥T, it is determined that the current K range cells to be detected have a range-extended target, and the test data is not used as training data for the subsequent range cells to be detected. Conversely, if λ<T, it is determined that the current K range cells to be detected do not have a range-extended target, and the test data is used as training data for the subsequent range cells to be detected.

[0138] The performance comparison results of the detectors under the target-oriented vector mismatch environment are shown in the attached Figure 3 The results show that compared with existing detectors for extended range targets such as 2S-GLRT-HE and P2S-Rao-HE, the proposed method has better detection performance in mismatched environments.

[0139] Those skilled in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0140] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods and will not be repeated here.

[0141] In the embodiments provided in this application, it should be understood that the disclosed 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 modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules 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 modules, which can be electrical, mechanical or other forms.

[0142] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the objectives of the embodiments of the present invention.

[0143] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0144] If the functions are implemented as software modules and sold or used as standalone products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the energy-saving signal transmission / reception method according to various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0145] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

[0146] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of the present invention.

Claims

1. A radar target robust intelligent detection and interference adaptive suppression method, characterized in that: including: Collect test data Z through K distance cells to be measured, and extract training data Y from adjacent R reference cells; Utilize the skew-symmetric property of the clutter covariance matrix to perform a decorrelation transformation on the test data Z, the clutter covariance matrix M, the target signal coordinate matrix P, and the interference signal coordinate matrix Q to complete the initial data preprocessing; In the absence of target assumption, solve the maximum likelihood estimation of the interference signal coordinate matrix Q; in the presence of target assumption, solve the target parameter vector Θ rp-1s The maximum likelihood estimate of , and the target parameter vector Θ is estimated using the complex Gaussian probability density function of the test data Z rp-1s Obtain the partial derivative, and then substitute the maximum likelihood estimate of the interference signal coordinate matrix Q obtained under the no-target assumption into the derivative result to obtain the calculation result; substitute the calculation result and the maximum likelihood estimate of the target parameter vector under the target assumption into the range-extended target gradient detection statistic, and construct the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix; finally, obtain the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data, and substitute the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix, replacing the unknown clutter skew-symmetric covariance matrix, and construct the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method; Determine the detection threshold T through theoretical calculation or Monte Carlo simulation according to the preset false alarm probability. By comparing the detection statistic λ with the detection threshold T, determine whether there is a range extended target in the current distance cell to be detected according to the comparison result.

2. The radar target robust intelligent detection and interference adaptive suppression method according to claim 1 is characterized in that: Collect test data Z through K distance cells to be measured, and extract training data Y from adjacent R reference cells, including: The radar system collects test data Z from K adjacent range units. After appropriate sampling and organization, it forms an N×1 dimensional vector, which is represented by z t ∈￡ N×1 ,t=1,2,...,K, where N represents the product of the number of antenna elements and the number of pulses, represents a set of N×1 dimensional complex matrices; the disturbance in the test data consists of clutter components and interference components, which are represented by c t ∈￡ N×1 and j t ∈￡ N×1 ,t=1,2,...,K;set the interference component j t ,t=1,2,...,K is modeled as a deterministic subspace signal, belonging to the known multi-rank subspace J∈￡ N×q , denoted as j t =Jq t , where q t ∈￡ q×1 , t=1,2,...,K represents the unknown complex coordinate vector of the interference signal; and the clutter component c t , t=1,2,...,K are independent and identically distributed among different distance units and obey the zero-mean circularly symmetric complex Gaussian distribution with covariance matrix M, that is, c t ~CN(0 N×1 ,M); where M is an unknown Hermitian positive definite matrix; in addition, set the target signal s t ,t=1,2,...,K is also modeled as a deterministic subspace signal, belonging to the known multi-rank subspace H∈￡ N×p , denoted as s t =Hp t , where p t ∈￡ p×1 , t=1,2,...,K represents the unknown complex coordinate vector of the target signal; To facilitate derivation, define the augmented matrix In order to estimate the unknown clutter covariance matrix M, it is assumed that there is a set of training data containing only clutter components, which is usually collected from R distance units adjacent to the distance unit to be measured, and is recorded as Y = [y1, y2, ..., y R ]∈￡ N×R , where y l =c l ,l=1,2,...,R is also independent and identically distributed and satisfies c l ~CN(0 N×1 ,M).

3. The radar target robust intelligent detection and interference adaptive suppression method according to claim 2, characterized in that: Utilize the skew-symmetric property of the clutter covariance matrix to perform a decorrelation transformation on the received data and parameter matrices to complete the initial data preprocessing, including: Assume that the clutter covariance matrix M is known, then the two-step Gradient test decision formula for complex signals can be expressed as Among them, λ 2S-Gradient and T 2S-Gradient Represent the test statistic and threshold respectively; test data Z=[z1,z2,...,z K ]∈￡ N×K , target signal coordinate matrix P=[p1,p2,...,p K ]∈￡ p×K , interference signal coordinate matrix Q=[q1,q2,...,q K ]∈￡ q×K ;Unknown parameter set The relevant parameter Θ r =vec(P)∈￡ pK×1 , interference parameters ; represents the maximum likelihood estimate of Θ under the assumption H0, Represents Θ r The maximum likelihood estimate under the H1 assumption, Θ r0 Represents Θ r True value under the H0 hypothesis; superscript (·) T represents the transpose, and the vec(·) function realizes the vectorization of the matrix; it should be noted that under the H0 assumption, the target does not exist, so Θ r0 =0 pK×1 .f i (Z|iP,Q) is the conditional probability density function of the test data Z under the H0 hypothesis or H1, expressed as: Among them, T i =(ZB i D)(ZB i D) H , and define the augmented matrix B i =[iH,J] and D = [P T ,Q T ] T ; |·| represents the determinant of a square matrix, the tr(·) function represents the trace of a square matrix, the exp(·) function represents the exponential operation, and the superscript (·) H represents the conjugate transpose; the condition p+q≤N must be satisfied to ensure that B=[H,J] is a column-full rank augmented matrix; in addition, by utilizing the skew symmetry of the clutter covariance matrix M, it can be derived that: Among them, the superscript (·) * represents conjugation; therefore, Equation (2) can be rewritten as: where, T ip =(T i + D N T i * D N ) / 2 = (Z p - B i D p )(Z p - B i D p ) H , and In the above formula, and represent the real and imaginary parts respectively, and 4. The radar target robust intelligent detection and interference adaptive suppression method according to claim 3 is characterized in that: In the absence of target assumption, solve the maximum likelihood estimation of the interference signal coordinate matrix Q; in the presence of target assumption, solve the target parameter vector Θ rp-1s The maximum likelihood estimate of , and the target parameter vector Θ is estimated using the complex Gaussian probability density function of the test data Z rp-1s Obtain the partial derivative, and then substitute the maximum likelihood estimate of the interference signal coordinate matrix Q obtained under the no-target assumption into the derivative result to obtain the calculation result; substitute the calculation result and the maximum likelihood estimate of the target parameter vector under the target assumption into the range-extended target gradient detection statistic, and construct the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix; finally, obtain the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data, and substitute the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix, replacing the unknown clutter skew-symmetric covariance matrix, and construct the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method, including: Assume that the clutter covariance matrix M is known, the skew-symmetric two-step Gradient test decision formula can be reformulated as: in, express The maximum likelihood estimate under the H0 hypothesis, Θ rp-2s =vec(P p )∈￡ 2pK×1 ,Θ sp-2s =vec(Q p )∈￡ (2qK+1)×1 , Represents Θ rp-2s In the maximum likelihood estimation under the H1 hypothesis, the conditional probability density function of the test data Z can be expressed as: Under the hypothesis H0, take the derivative of Equation (8) with respect to the interference signal coordinate matrix Q and set the result to zero, and the maximum likelihood estimate of the interference signal coordinate matrix Q under the given clutter covariance matrix M can be obtained: in, Under the H1 assumption, let the natural logarithm of equation (8) be rp-2s Taking partial derivatives, we can get: Substitute equation (9) into Further: in, Represents projection to matrix The orthogonal projection matrix of the column space of the orthogonal complement space; Next, let the natural logarithm of formula (8) be D p Taking the partial derivative, we can get D p The maximum likelihood estimate under the H1 hypothesis is: in, because Under the condition of given clutter covariance matrix M, we can get P p The maximum likelihood estimate under the H1 hypothesis is: Furthermore, it can be obtained: Substitute Equation (11) and Equation (14) into Equation (7), and then perform algebraic processing and simplification to obtain the Gradient test statistic under the given clutter covariance matrix M as: in, Represents projection to matrix The orthogonal projection matrix of the column space; The maximum likelihood estimate of the unknown skew-symmetric covariance matrix M is obtained using the training data, which is exactly the same as the maximum likelihood estimate in formula (5) Then, replace M in formula (15) with The final Gradient test statistic is:

5. The method for robust intelligent detection of radar targets and adaptive interference suppression according to any one of claims 1 to 4, characterized in that: Determine the detection threshold T through theoretical calculation or Monte Carlo simulation according to the preset false alarm probability. By comparing the detection statistic λ with the detection threshold T, determine whether there is a range extended target in the current distance cell to be detected according to the comparison result, including: When λ≥T, it is determined that there is a range extended target in the current distance cell to be detected, and at the same time, mark the test data as the detected state and exclude it from the subsequent training data update process of the sliding window; When λ<T, it is determined that there is no range extended target in the current distance cell to be detected, and use the current test data as a valid sample to be included in the training data set for the update of the reference cells of subsequent distance cells to be detected.

6. Radar target robust intelligent detection and interference adaptive suppression system, characterized by: including: A data preprocessing module that collects test data Z through K distance cells to be measured and extracts training data Y from adjacent R reference cells; Utilize the skew-symmetric property of the clutter covariance matrix to perform a decorrelation transformation on the test data Z, the clutter covariance matrix M, the target signal coordinate matrix P, and the interference signal coordinate matrix Q to complete the initial data preprocessing; The detection statistic construction module solves the maximum likelihood estimation of the interference signal coordinate matrix Q in the absence of target assumptions; solves the target parameter vector Θ in the presence of target assumptions rp-1s The maximum likelihood estimate of , and the target parameter vector Θ is estimated using the complex Gaussian probability density function of the test data Z rp-1s Obtain the partial derivative, and then substitute the maximum likelihood estimate of the interference signal coordinate matrix Q obtained under the no-target assumption into the derivative result to obtain the calculation result; substitute the calculation result and the maximum likelihood estimate of the target parameter vector under the target assumption into the range-extended target gradient detection statistic, and construct the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix; finally, obtain the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data, and substitute the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target gradient detection statistic under the condition of known clutter skew-symmetric covariance matrix, replacing the unknown clutter skew-symmetric covariance matrix, and construct the detection statistic λ of the radar target robust intelligent detection and interference adaptive suppression method; A target detection determination module that determines the detection threshold T through theoretical calculation or Monte Carlo simulation according to the preset false alarm probability. By comparing the detection statistic λ with the detection threshold T, determine whether there is a range extended target in the current distance cell to be detected according to the comparison result.

7. An electronic device, characterized in that The invention comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for robust intelligent detection of radar targets and adaptive interference suppression according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for robust intelligent detection and adaptive interference suppression of radar targets according to any one of claims 1 to 5 is implemented.

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