Target fusion detection method and system for non-uniform clutter and interference cooperative suppression

Abstract

The invention discloses a non-uniform clutter and interference cooperative suppression target fusion detection method and system, and relates to the technical field of broadband radar signal processing, and the method comprises the steps: carrying out the unitary transformation of test data, a clutter covariance matrix, a target coordinate matrix and an interference coordinate matrix based on the skew symmetry of the clutter covariance matrix; constructing a distance extension target Gradient detection statistical magnitude under the condition of a known clutter skew symmetry covariance matrix; calculating the maximum likelihood estimation of the clutter skew symmetry covariance matrix; based on the maximum likelihood estimation of the clutter skew-symmetric covariance matrix and the distance expansion target Gradant detection statistic, constructing a target detection statistic of non-uniform clutter and interference cooperative suppression; performing target fusion detection on the target detection unit based on the target detection statistical magnitude and a preset detection threshold; according to the invention, the technical problems of complex construction process and high calculation complexity of the target detector in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of broadband radar signal processing, and in particular to a target fusion detection method and system for collaborative suppression of non-uniform clutter and interference. Background Art

[0002] As radar bandwidth increases, its range resolution further improves. Wideband radar is widely used in 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, wideband radar target scattering energy can spread to adjacent range units, appearing as a "one-dimensional range profile," forming a range-extended target. If the point target detection method is still used to detect the echo signal for 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 easily leaked to the adjacent range units, resulting in signal pollution, and further forming a masking effect on the target signal of the single target detection unit, 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 broadband radar, making it difficult for existing range-extended target detection methods to achieve ideal detection results.

[0003] In fact, although the global uniformity of the complex clutter background is destroyed, the local uniformity of the clutter is still reflected within a certain radial distance range. At this time, the clutter can be modeled using a partially uniform model, that is, the clutter components in the target detection unit and the reference unit have the same covariance matrix structure and different power levels. This model can make full use of the local uniformity of the clutter, but the number of reference units that can be used is limited by the actual degree of clutter non-uniformity.

[0004] 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 interference 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-PHE) for cooperative suppression of inhomogeneous clutter and interference 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, considering the difficulty in obtaining sufficient pure clutter training data in practical 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 the detection statistic, a skew-symmetric subspace Rao detector (abbreviated as P2S-Rao-PHE) can be obtained for collaborative suppression of inhomogeneous clutter and interference. However, in practical implementation, this approach faces the challenge of solving the Fisher information matrix, and the detector construction process is also relatively complex. Summary of the Invention

[0005] The purpose of the present invention is to provide a target fusion detection method and system with coordinated suppression of non-uniform clutter and interference in order to solve at least one of the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a target fusion detection method for cooperative suppression of non-uniform clutter and interference, which is applied to a broadband radar; the method comprises: based on the skew symmetry of the clutter covariance matrix, performing a unitary transformation on test data, a clutter covariance matrix, a target coordinate matrix and an interference coordinate matrix; the test data is data collected based on multiple target detection units; the clutter covariance matrix and the interference coordinate matrix are signal matrices corresponding to the clutter component and the interference component of the disturbance signal of the test data, respectively; based on the maximum likelihood estimate of the interference coordinate matrix, the target parameter vector and the probability density function of the test data, a known clutter skew symmetric covariance matrix is constructed. The invention relates to a range-extended target gradient detection statistic under the condition of the present invention; based on the training data, calculating the maximum likelihood estimate of the clutter skew-symmetric covariance matrix; the clutter skew-symmetric covariance matrix is the clutter covariance matrix after the unitary transformation; the training data is data collected based on multiple reference units adjacent to the multiple target detection units; based on the maximum likelihood estimate of the clutter skew-symmetric covariance matrix and the range-extended target gradient detection statistic, constructing a target detection statistic for cooperative suppression of non-uniform clutter and interference; based on the target detection statistic and a preset detection threshold, performing target fusion detection on the target detection unit; the target detection statistic includes:

[0007]

[0008] Where λ P2S-Gradient-PHE represents the target detection statistic, tr(·) represents the trace of the matrix, is the test data after unitary transformation under the condition of the clutter skew-symmetric covariance matrix, (·) H represents the conjugate transposed matrix, Represents projection to matrix The orthogonal projection matrix of the column space, Represents projection to matrix The orthogonal projection matrix of the orthogonal complement space of the column space of is the target multi-rank subspace after unitary transformation, is the interference multi-rank subspace after unitary transformation.

[0009] Furthermore, the test data, clutter covariance matrix, target coordinate matrix and interference coordinate matrix are unitarily transformed, and the matrices after unitary transformation include:

[0010]

[0011] Where,

[0012]

[0013] Zp is the test data after unitary transformation, P p is the target coordinate matrix after unitary transformation, C m×n represents a set of m×n dimensional complex matrices, Q p is the interference coordinate matrix after unitary transformation, is the maximum likelihood estimate of the clutter covariance matrix after unitary transformation, represents the real part value, represents the imaginary part value, and Y represents the training data.

[0014] Furthermore, based on the interference coordinate matrix, the maximum likelihood estimate of the target parameter vector and the probability density function of the test data, a gradient detection statistic for a range-extended target under the condition of a known clutter skew-symmetric covariance matrix is constructed, including: under the assumption of no target, solving the maximum likelihood estimate of the interference coordinate matrix to obtain a first estimator; under the assumption of a target, solving the maximum likelihood estimate of the target parameter vector to obtain a second estimator; using the probability density function of the test data to obtain a partial derivative of the target parameter vector, and then substituting the first estimator into the derivative result to obtain an intermediate relationship; substituting the intermediate relationship and the second estimator into the gradient detection statistic for the range-extended target, and combining the maximum likelihood estimate of the clutter power factor under the assumption of no target to construct the gradient detection statistic for the range-extended target under the condition of a known clutter skew-symmetric covariance matrix.

[0015] Furthermore, based on the maximum likelihood estimate of the clutter skew-symmetric covariance matrix and the range-extended target Gradient detection statistic, a target detection statistic for collaborative suppression of inhomogeneous clutter and interference is constructed, including: substituting the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target Gradient detection statistic, replacing the unknown clutter skew-symmetric covariance matrix in the range-extended target Gradient detection statistic with the maximum likelihood estimate of the clutter skew-symmetric covariance matrix, and constructing the target detection statistic for collaborative suppression of inhomogeneous clutter and interference.

[0016] Furthermore, based on the target detection statistic and the preset detection threshold, target fusion detection is performed on the target detection unit, including: determining the preset detection threshold based on a preset false alarm probability; judging whether the target detection statistic is greater than or equal to the preset detection threshold; if so, determining that the target detection unit has a range-extended target; if not, determining that the target detection unit does not have a range-extended target.

[0017] In a second aspect, an embodiment of the present invention further provides a target fusion detection system for collaborative suppression of non-uniform clutter and interference, which is applied to a broadband radar; the system comprises: a transformation module, a first construction module, a calculation module, a second construction module and a detection module; wherein the transformation module is used to perform a unitary transformation on the test data, the clutter covariance matrix, the target coordinate matrix and the interference coordinate matrix based on the skew symmetry of the clutter covariance matrix; the test data is data collected based on multiple target detection units; the clutter covariance matrix and the interference coordinate matrix are signal matrices corresponding to the clutter component and the interference component of the disturbance signal of the test data, respectively; the first construction module is used to construct a known clutter matrix based on the maximum likelihood estimate of the interference coordinate matrix, the target parameter vector and the probability density function of the test data. Gradient detection statistic of a range-extended target under the condition of a skew-symmetric covariance matrix of a clutter wave; the calculation module is used to calculate the maximum likelihood estimate of the skew-symmetric covariance matrix of the clutter wave based on the training data; the clutter skew-symmetric covariance matrix is the clutter covariance matrix after unitary transformation; the training data is data collected based on multiple reference units adjacent to the multiple target detection units; the second construction module is used to construct a target detection statistic for collaborative suppression of non-uniform clutter and interference based on the maximum likelihood estimate of the clutter skew-symmetric covariance matrix and the range-extended target Gradient detection statistic; the detection module is used to perform target fusion detection on the target detection unit based on the target detection statistic and a preset detection threshold; the target detection statistic includes:

[0018]

[0019] Where λ P2S-Gradient-PHE represents the target detection statistic, tr(·) represents the trace of the matrix, is the test data after unitary transformation under the condition of the clutter skew-symmetric covariance matrix, (·) H represents the conjugate transposed matrix, Represents projection to matrix The orthogonal projection matrix of the column space, Represents projection to matrix The orthogonal projection matrix of the orthogonal complement space of the column space of is the target multi-rank subspace after unitary transformation, is the interference multi-rank subspace after unitary transformation.

[0020] Furthermore, the first construction module is also used to: under the assumption of no target, solve the maximum likelihood estimation of the interference coordinate matrix to obtain a first estimator; under the assumption of a target, solve the maximum likelihood estimation of the target parameter vector to obtain a second estimator; use the probability density function of the test data to calculate the partial derivative of the target parameter vector, and then substitute the first estimator into the derivative result to obtain an intermediate relationship; substitute the intermediate relationship and the second estimator into the range-extended target gradient detection statistic, and combine the maximum likelihood estimation of the clutter power factor under the assumption of no target to construct the range-extended target gradient detection statistic under the condition of a known clutter skew-symmetric covariance matrix.

[0021] Furthermore, the detection module is also used to: determine a preset detection threshold based on a preset false alarm probability; determine whether the target detection statistic is greater than or equal to the preset detection threshold; if so, determine that there is a range-extended target in the target detection unit; if not, determine that there is no range-extended target in the target detection unit.

[0022] In a third aspect, an embodiment of the present invention further provides an electronic device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0023] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method provided in the embodiment of the present invention is implemented.

[0024] The present invention provides a target fusion detection method and system for collaboratively suppressing inhomogeneous clutter and interference. This system constructs an adaptive target detector for collaboratively suppressing inhomogeneous clutter and interference. The method features a closed-form detection statistic expression, eliminates the need for iterative computation, requires less training data, and exhibits enhanced detection robustness against steering vector mismatch signals. While maintaining a constant false alarm rate (CFAR), the system effectively strikes a reasonable balance between intelligent anti-interference performance, mismatch robustness, and detection performance, alleviating the technical challenges of complex target detector construction and high computational complexity in existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 A flow chart of a target fusion detection method for collaborative suppression of non-uniform clutter and interference provided by an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of a target fusion detection system for collaborative suppression of inhomogeneous clutter and interference provided by an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of another target fusion detection system for collaborative suppression of inhomogeneous clutter and interference provided by an embodiment of the present invention;

[0029] Figure 4 A comparison chart of the detection performance of the target fusion detection method for collaborative suppression of non-uniform clutter and interference provided by an embodiment of the present invention and existing detection methods in a matching environment;

[0030] Figure 5 This is a comparison chart of the detection performance of the target fusion detection method with collaborative suppression of non-uniform clutter and interference provided by an embodiment of the present invention and the existing detection method in a mismatch environment. DETAILED DESCRIPTION

[0031] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0032] Example 1

[0033] Figure 1 This is a flow chart of a target fusion detection method for cooperative suppression of non-uniform clutter and interference provided by an embodiment of the present invention, which is applied to broadband radar. Figure 1 As shown, the method specifically includes the following steps:

[0034] Step S102, based on the skew symmetry of the clutter covariance matrix, unitary transformation is performed on the test data, the clutter covariance matrix, the target coordinate matrix and the interference coordinate matrix; the test data is test sample data collected based on multiple target detection units; the clutter covariance matrix and the interference coordinate matrix are signal matrices corresponding to the clutter component and the interference component of the disturbance signal of the test data, respectively.

[0035] Step S104 : constructing a range-extended target gradient detection statistic under the condition of a known clutter skew-symmetric covariance matrix based on the interference coordinate matrix, the maximum likelihood estimate of the target parameter vector, and the probability density function of the test data.

[0036] Step S106 , calculating the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data; the clutter skew-symmetric covariance matrix is the clutter covariance matrix after unitary transformation; the training data is data collected based on multiple reference units adjacent to multiple target detection units.

[0037] Step S108 : constructing a target detection statistic for cooperative suppression of inhomogeneous clutter and interference based on the maximum likelihood estimation of the clutter skew-symmetric covariance matrix and the range-extended target gradient detection statistic.

[0038] Step S110 : performing target fusion detection on the target detection unit based on the target detection statistics and the preset detection threshold.

[0039] Specifically, target detection statistics include:

[0040]

[0041] Where λ P2S-Gradient-PHE represents the target detection statistic, tr(·) represents the trace of the matrix, is the test data after unitary transformation under the condition of the clutter skew-symmetric covariance matrix, (·) H represents the conjugate transposed matrix, Represents projection to matrix The orthogonal projection matrix of the column space, Represents projection to matrix The orthogonal projection matrix of the orthogonal complement space of the column space of is the target multi-rank subspace after unitary transformation, is the interference multi-rank subspace after unitary transformation.

[0042] Specifically, the present invention employs a dual-channel data acquisition mechanism, acquiring test samples Z from K target detection units and simultaneously extracting training samples Y from R spatially adjacent reference units. Under conditions where system parameters are unknown, the invention innovatively introduces unitary spatial transformation technology. This technology, based on the skew symmetry of the clutter covariance matrix, applies a unitary transformation to the received data matrix and related parameters.

[0043] Specifically, the test data, the clutter covariance matrix, the target coordinate matrix, and the interference coordinate matrix are unitarily transformed, and the matrices obtained after the unitary transformation include:

[0044]

[0045] Where,

[0046]

[0047] Z pis the test data after unitary transformation, P p is the target coordinate matrix after unitary transformation, C m×n represents a set of m×n dimensional complex matrices, Q p is the interference coordinate matrix after unitary transformation, is the maximum likelihood estimate of the clutter covariance matrix after unitary transformation, represents the real part value, represents the imaginary part value, and Y represents the training data.

[0048] Assume that the radar system collects test data from K adjacent target detection units, and after appropriate sampling and organization, 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, m×n Represents a set of complex matrices of dimension m×n.

[0049] The disturbance in the test data consists of clutter component and interference component, which are represented by c t ∈￡ N×1 and j t ∈￡ N×1 ,t=1,2,...,K. Assume that the interference component j t ,t=1,2,...,K is modeled as a deterministic subspace signal, belonging to a known interference multi-rank subspace J∈￡ N×q , denoted as j t =Hq 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 zero-mean circularly symmetric complex Gaussian distribution with covariance matrix γM, that is, c t ~CN(0 N×1 ,γM). Where M is an unknown Hermitian positive definite matrix and γ>0 represents a scaling factor that describes the unknown power mismatch between the test data and the training data.

[0050] In addition, assuming that the target signal s t ,t=1,2,...,K is also modeled as a deterministic subspace signal, belonging to the known target 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 subsequent derivation, define

[0051] 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 reference units adjacent to the target detection unit, denoted 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).

[0052] Assuming that the clutter covariance matrix M is known, the two-step gradient test decision formula for complex signals can be expressed as:

[0053]

[0054] Among them, λ 2S-Gradient and T 2S-Gradient Represent the test statistic and threshold respectively; Z=[z1,z2,...,z K ]∈￡ N×K ,P=[p1,p2,...,p K ]∈￡ p×K ,Q=[q1,q2,...,q K ]∈￡ q×K ; The relevant parameter Θ r =vec(P)∈￡ pK×1 , interference parameter Θ s =[γ,vec T (Q)] T ∈￡ (qK+1)×1 ; 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,γ) is assumed to be i , the conditional probability density function of the test data Z under i=0,1 is expressed as:

[0055] f i (Z|iP,Q,γ)={πN(K+R) γ NK |M| K+R} -1 ×exp[-tr(M -1 T i )],i=0,1 (2)

[0056] Among them, T i =(ZB i D)(ZB i D) H / γ,B i =[iH,J],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 M, we can deduce:

[0057] 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)

[0058] Among them, the superscript (·) * represents conjugation. Therefore, (2) can be rewritten as:

[0059] f i (Z|iP,Q,γ)={π N(K+R) γ NK |M| K+R} -1 ×exp[-tr(M -1 T ip )],i=0,1 (4)

[0060] Among them, T ip =(T i +D N T i * D N ) / 2=(Z p -B i D p )(Zp -B i D p ) H / γ, and

[0061]

[0062] In the above formula, and represent the real and imaginary parts respectively, and

[0063]

[0064] Specifically, step S104 further includes the following steps:

[0065] Step S1041, under the assumption of no target, solving the maximum likelihood estimation of the interference coordinate matrix to obtain a first estimator;

[0066] Step S1042: Under the target hypothesis, solving the maximum likelihood estimation of the target parameter vector to obtain a second estimator;

[0067] Step S1043, using the probability density function of the test data to calculate the partial derivative of the target parameter vector, and then substituting the first estimator into the derivative result to obtain an intermediate relationship;

[0068] Step S1044 , substituting the intermediate relationship and the second estimator into the range-extended target gradient detection statistic, and combining the maximum likelihood estimation of the clutter power factor under the no-target assumption, constructing the range-extended target gradient detection statistic under the condition of a known clutter skew-symmetric covariance matrix.

[0069] Specifically, step S108 includes: substituting the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target Gradient detection statistic, replacing the unknown clutter skew-symmetric covariance matrix in the range-extended target Gradient detection statistic with the maximum likelihood estimate of the clutter skew-symmetric covariance matrix, and constructing a target detection statistic for collaborative suppression of inhomogeneous clutter and interference.

[0070] Specifically, under the assumption that M is known, the skew-symmetric two-step gradient (abbreviated as P2S-Gradient) test decision formula can be reformulated as:

[0071]

[0072] in, express The maximum likelihood estimate under the H0 hypothesis, Θ rp-2s =vec(P p )∈￡ 2pK×1 ,Θsp-2s =[γ,vec T (Q p )] T ∈￡ (2qK+1)×1 , Represents Θ rp-2s Maximum likelihood estimation under the H1 hypothesis. The conditional probability density function of the test data Z can be expressed as:

[0073]

[0074] Under the assumption H0, let (8) be differentiated with respect to Q and set the result to zero, and we can obtain the maximum likelihood estimate of Q under the given M condition:

[0075]

[0076] in, Under the H1 assumption, let the natural logarithm of (8) be rp-2s Taking partial derivatives, we can get:

[0077]

[0078] Substitute (9) into Further:

[0079]

[0080] in, Next, let the natural logarithm of (8) be D p Taking the partial derivative, we can get D p The maximum likelihood estimate under the H1 hypothesis is:

[0081]

[0082] in, because Under the condition of given M, we can get P p The maximum likelihood estimate under the H1 hypothesis is:

[0083]

[0084] We can further obtain:

[0085]

[0086] Substituting (11) and (14) into (7), and then performing algebraic processing and simplification, we can obtain the P2S-Gradient test statistic under the given γ condition:

[0087]

[0088] in,

[0089] Under the given assumption of M, in order to obtain the P2S-Gradient test statistic, it is also necessary to derive the maximum likelihood estimate of γ under the H0 hypothesis. Next, substituting (9) into (8), we can obtain:

[0090]

[0091] Let the derivative of (16) with respect to γ be equal to zero, and the maximum likelihood estimate of γ under the assumption H0 can be obtained as:

[0092]

[0093] Substituting (17) into (15) and ignoring the constant term, the distance expansion target gradient test statistic for a given M, namely the P2S-Gradient test statistic (denoted as P2S-Gradient-PHE), can be expressed as:

[0094]

[0095] Finally, the training data is used to obtain the maximum likelihood estimate of the unknown skew-symmetric covariance matrix M, which is exactly the same as in formula (5) Then, replace M in (18) with The final target detection statistic (P2S-Gradient test statistic) is:

[0096]

[0097] As can be seen from formula (19), the target detection statistic constructed by the present invention has a closed-form detection statistic expression and does not require iterative calculations. It is also worth noting that compared with the 2S-GLRT-HE detector for range-extended targets, the target fusion detection method with coordinated suppression of non-uniform clutter and interference requires less training data and has stronger detection robustness against guidance vector mismatch signals. Overall, the target fusion detection method with coordinated suppression of non-uniform clutter and interference of the present invention can effectively balance the algorithm's intelligent anti-interference ability, mismatch robustness, and detection performance while maintaining the CFAR characteristics.

[0098] Specifically, step S110 further includes the following steps:

[0099] Step S1101: determining a preset detection threshold based on a preset false alarm probability.

[0100] Step S1102, determine whether the target detection statistic is greater than or equal to a preset detection threshold; if yes, execute step S1103; if not, execute step S1104.

[0101] Step S1103: Determine whether the target detection unit has a range-extended target.

[0102] Step S1104: Determine whether the target detection unit has no range-extended target.

[0103] Specifically, the present invention uses a dynamic data management mechanism to achieve constant false alarm rate control. Its core operating process is as follows: first, a preset detection threshold is determined based on a preset false alarm probability; second, the target detection statistics obtained in real time are compared and analyzed with the preset detection threshold. When the target detection statistics exceed the preset detection threshold, a dual judgment is made: 1) a distance-extended target exists in the current target detection unit; 2) the test data of the current target detection unit will enter an isolated state and will not be used as part of the training sample library for subsequent adjacent unit detection. Conversely, if the target detection statistics do not reach the preset detection threshold, it is determined that there is no target in the current target detection unit, and the test data of the target detection unit is included in the dynamically updated training data set, providing a real-time calibration basis for subsequent detection units.

[0104] The present invention provides a target fusion detection method for collaboratively suppressing non-uniform clutter and interference, which has the following technical effects compared with the prior art:

[0105] 1) A target adaptive detector for cooperative suppression of inhomogeneous clutter and interference with a closed analytical expression is proposed. This detector achieves fast computation through analytical solution, avoiding the iterative optimization process required by traditional methods. It also eliminates the need for explicit calculation of the Fisher information matrix, significantly reducing implementation complexity and providing real-time advantages in engineering applications.

[0106] 2) By deeply exploring the skew symmetry of the clutter covariance matrix, the accuracy of matrix estimation is effectively improved, the dependence on the number of training samples is greatly reduced, and a new technical approach is provided for range-extended target detection under small sample conditions;

[0107] 3) In real-world scenarios with steering vector mismatch, the proposed method demonstrates superior robustness compared to traditional detectors and exhibits stronger environmental adaptability.

[0108] 4) For structured interference environments, the target adaptive detector for cooperative suppression of non-uniform clutter and interference of the present invention can effectively suppress interference signals of different intensities and has good intelligent anti-interference performance;

[0109] 5) The method of the present invention is applicable to some non-wideband radar detection situations, for example, using low / medium resolution radar to detect large targets or to detect groups of spatially adjacent point targets moving at the same speed (such as ship formations, aircraft formations, and vehicle formations), and has good application prospects.

[0110] Example 2

[0111] Figure 2 FIG. 1 is a schematic diagram of a target fusion detection system for cooperative suppression of non-uniform clutter and interference according to an embodiment of the present invention, which is applied to a broadband radar. Figure 2 As shown, the system includes: a transformation module 10 , a first construction module 20 , a calculation module 30 , a second construction module 40 and a detection module 50 .

[0112] Specifically, the transformation module 10 is used to perform unitary transformation on the test data, the clutter covariance matrix, the target coordinate matrix and the interference coordinate matrix based on the skew symmetry of the clutter covariance matrix; the test data is data collected based on multiple target detection units; the clutter covariance matrix and the interference coordinate matrix are signal matrices corresponding to the clutter component and the interference component of the disturbance signal of the test data, respectively;

[0113] The first construction module 20 is used to construct a range-extended target gradient detection statistic under the condition of a known clutter skew-symmetric covariance matrix based on the interference coordinate matrix, the maximum likelihood estimate of the target parameter vector and the probability density function of the test data;

[0114] A calculation module 30 is used to calculate the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data; the clutter skew-symmetric covariance matrix is the clutter covariance matrix after unitary transformation; the training data is data collected based on multiple reference units adjacent to the multiple target detection units;

[0115] The second construction module 40 is used to construct a target detection statistic for cooperative suppression of inhomogeneous clutter and interference based on the maximum likelihood estimation of the clutter skew-symmetric covariance matrix and the range-extended target gradient detection statistic;

[0116] The detection module 50 is configured to perform target fusion detection on the target detection unit based on target detection statistics and a preset detection threshold.

[0117] Specifically, target detection statistics include:

[0118]

[0119] Where λ P2S-Gradient-PHE represents the target detection statistic, tr(·) represents the trace of the matrix, is the test data after unitary transformation under the condition of the clutter skew-symmetric covariance matrix, (·) H represents the conjugate transposed matrix, Represents projection to matrix The orthogonal projection matrix of the column space, Represents projection to matrix The orthogonal projection matrix of the orthogonal complement space of the column space, is the target multi-rank subspace after unitary transformation, is the interference multi-rank subspace after unitary transformation.

[0120] Specifically, the first building block 20 is further configured to:

[0121] Under the assumption of no target, solve the maximum likelihood estimation of the interference coordinate matrix to obtain the first estimator;

[0122] Under the target hypothesis, the maximum likelihood estimation of the target parameter vector is solved to obtain the second estimator;

[0123] Use the probability density function of the test data to find the partial derivative of the target parameter vector, and then substitute the first estimator into the derivative result to obtain the intermediate relationship;

[0124] The intermediate relationship and the second estimator are substituted into the range-extended target gradient detection statistic. Combined with the maximum likelihood estimation of the clutter power factor under the no-target assumption, the range-extended target gradient detection statistic is constructed under the condition of a known clutter skew-symmetric covariance matrix.

[0125] Specifically, the detection module 50 is further configured to:

[0126] determining a preset detection threshold based on a preset false alarm probability;

[0127] Determine whether the target detection statistic is greater than or equal to a preset detection threshold;

[0128] If yes, it is determined that the target detection unit has a range-extended target;

[0129] If not, it is determined that the target detection unit does not have a range-extended target.

[0130] Example 3

[0131] Figure 3 FIG. 1 is a schematic diagram of another target fusion detection system for cooperative suppression of non-uniform clutter and interference provided by an embodiment of the present invention. Figure 3As shown, the system includes: a data transformation module, a maximum likelihood estimation solution module under the H0 assumption, a probability density function derivation module under the H1 assumption, a maximum likelihood estimation solution module under the H1 assumption, a gradient detection statistic construction module under the condition of suppressed covariance matrix, a target adaptive detector construction module for cooperative suppression of non-uniform clutter and interference, a skew-symmetric clutter covariance matrix maximum likelihood estimation module and a detection decision module.

[0132] A specific implementation of the target fusion detection system provided in the embodiment of the present invention is divided into the following steps:

[0133] Step A1: Use the sea detection radar to illuminate the sea area to be detected and obtain the test data Z of K target detection units. Send the test data Z to the data transformation module; in the data transformation module, according to equations (5) and (6), the skew-symmetric transformation test data Z is obtained. p ; Transform the skew symmetric test data Z p It is sent to the maximum likelihood estimation solution module under the H0 hypothesis, the probability density function derivation module under the H1 hypothesis, and the maximum likelihood estimation solution module under the H1 hypothesis. In the maximum likelihood estimation solution module under the H0 hypothesis, the Q under the H0 hypothesis is obtained according to formula (9) and formula (17). p and the maximum likelihood estimate of γ and In the derivative module of the probability density function under the H1 hypothesis, the test data Z under the H1 hypothesis is obtained according to formula (11): p The complex Gaussian probability density function of the target parameter vector Θ r The derivative result of ; In the maximum likelihood estimation solution module under the H1 assumption, according to formula (14), Θ under the H1 assumption is obtained rp The maximum likelihood estimate of The results obtained from the maximum likelihood estimation solution module under the H0 assumption, the derivative module of the probability density function under the H1 assumption, and the maximum likelihood estimation solution module under the H1 assumption are sent to the gradient detection statistic construction module under the condition of known covariance matrix. According to formula (18), the gradient detection statistic of the range-extended target under the condition of known clutter skew-symmetric covariance matrix is constructed and sent to the target adaptive detector construction module for cooperative suppression of non-uniform clutter and interference.

[0134] It should be noted that in step A1, considering that external interference in the actual ocean environment may adversely affect the adaptive detection of range-extended targets, the target adaptive detection model for collaborative suppression of non-uniform clutter and interference constructed by the present method takes external interference into account during the detector design process and 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 method for intelligent detection of range-extended targets using gradients can effectively suppress interference signals of varying intensities, demonstrating excellent intelligent anti-interference capabilities. Furthermore, the detector of the present method is applicable to non-uniform sea clutter environments, such as partially uniform ones. By fully exploiting the local uniformity of sea clutter and utilizing maximum likelihood estimation of the clutter power factor under the no-target assumption, the detector's intelligent adaptability to non-uniform sea clutter environments is enhanced.

[0135] 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 sent to the data transformation module; in the data transformation module, the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data is obtained according to equations (5) and (6): It is sent to the target adaptive detector building module for cooperative suppression of non-uniform clutter and interference. Substitute the range-extended target gradient detection statistic obtained in step A1, replace the unknown clutter skew-symmetric covariance matrix, and construct the target detection statistic λ of the target fusion detection method with cooperative suppression of non-uniform clutter and interference according to formula (19), and send λ to the detection decision module.

[0136] Notably, in step A2, the proposed method achieves superior detection performance compared to detectors such as 2S-GLRT-PHE and P2S-Rao-PHE for range-extended targets, particularly when training data is scarce. Furthermore, the proposed adaptive target detector for collaborative suppression of non-uniform clutter and interference 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, enhancing the adaptive detection capability of multi-channel broadband radars for small, weak targets on the sea surface in complex electromagnetic environments.

[0137] Step A3: Set the preset detection threshold T according to the preset false alarm probability: Specifically, the preset false alarm probability is P fa According to the Monte Carlo method, based on the 100 / P accumulated in the early stage fa The preset 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 / Pfa -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 target detection statistic λ is compared with the preset detection threshold T. If λ≥T, it is determined that the current K target detection units have range-extended targets, and the test data is not used as training data for subsequent target detection units. Conversely, if λ<T, it is determined that the current K target detection units do not have range-extended targets, and the test data is used as training data for subsequent target detection units.

[0138] The performance comparison results of the detectors under the target-oriented vector matching environment are shown in the attached Figure 4 . Figure 4 Some parameters in the are set as follows: N = 16, K = 15, R = 32, p = 2, q = 2, false alarm probability P fa =10 -4 ,Interference clutter power ratio ICR=15dB. The results show that compared with the existing range extended target detectors such as 2S-GLRT-PHE and P2S-Rao-PHE, the detector of the proposed method has better detection performance in the matching environment.

[0139] Example 4

[0140] Another specific implementation of the target fusion detection system provided in the embodiment of the present invention is divided into the following steps:

[0141] Step B1: Use the ground detection radar to illuminate the area to be detected and obtain the test data Z of K target detection units. Send the test data Z to the data transformation module; in the data transformation module, according to equations (5) and (6), the skew-symmetric transformation test data Z is obtained. p ; Transform the skew symmetric test data Z p It is sent to the maximum likelihood estimation solution module under the H0 hypothesis, the probability density function derivation module under the H1 hypothesis, and the maximum likelihood estimation solution module under the H1 hypothesis. In the maximum likelihood estimation solution module under the H0 hypothesis, the Q under the H0 hypothesis is obtained according to formula (9) and formula (17). p and the maximum likelihood estimate of γ and In the derivative module of the probability density function under the H1 hypothesis, the test data Z under the H1 hypothesis is obtained according to formula (11): p The complex Gaussian probability density function of the target parameter vector Θ r The derivative result of ; In the maximum likelihood estimation solution module under the H1 assumption, according to formula (14), Θ under the H1 assumption is obtained rp The maximum likelihood estimate of The results obtained from the maximum likelihood estimation solution module under the H0 assumption, the derivative module of the probability density function under the H1 assumption, and the maximum likelihood estimation solution module under the H1 assumption are sent to the gradient detection statistic construction module under the condition of known covariance matrix. According to formula (18), the gradient detection statistic of the range-extended target under the condition of known clutter skew-symmetric covariance matrix is constructed and sent to the target adaptive detector construction module for cooperative suppression of non-uniform clutter and interference.

[0142] It is worth noting that in step B1, considering that external interference may exist in the actual ground-ocean environment and adversely affect the adaptive detection of range-extended targets, the target adaptive detection model for collaborative suppression of non-uniform clutter and interference constructed by the method 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 possible mismatch effects of the interference signal. In the presence of subspace-structured interference environments, the intelligent detection method for range-extended targets based on gradients can effectively suppress interference signals of varying intensities and has good intelligent anti-interference capabilities. At the same time, the detector of the method of the present invention is applicable to non-uniform ground clutter environments, such as partially uniform ground clutter environments. By fully exploiting the local uniformity of ground clutter and utilizing the maximum likelihood estimation of the clutter power factor under the no-target assumption, the detector's intelligent adaptability to non-uniform ground clutter environments is improved.

[0143] Step B2: Radar illumination is performed on the target-free range around the area to be detected to obtain R reference units of training data Y containing only pure ground clutter. The training data Y is sent to the data transformation module; in the data transformation module, the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data is obtained according to equations (5) and (6): It is sent to the target adaptive detector building module for cooperative suppression of non-uniform clutter and interference. Substitute the range-extended target Gradient detection statistic obtained in step B1, replace the unknown clutter skew-symmetric covariance matrix, and construct the target detection statistic λ of the target fusion detection method with cooperative suppression of non-uniform clutter and interference according to formula (19), and send λ to the detection decision module.

[0144] Notably, in step B2, the proposed method achieves superior detection performance compared to detectors such as 2S-GLRT-PHE and P2S-Rao-PHE for range-extended targets, particularly when training data is scarce. Furthermore, the proposed adaptive target detector for collaborative suppression of non-uniform clutter and interference 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, enhancing the adaptive detection capability of multi-channel broadband radars for small, weak ground targets in complex electromagnetic environments.

[0145] Step B3: Set the preset detection threshold T according to the preset false alarm probability: Specifically, the preset false alarm probability is P fa According to the Monte Carlo method, based on the 100 / P accumulated in the early stage fa The preset 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 target detection statistic λ is compared with the preset detection threshold T. If λ ≥ T, it is determined that the current K target detection units have range-extended targets, and the test data is not used as training data for subsequent target detection units. Conversely, if λ < T, it is determined that the current K target detection units do not have range-extended targets, and the test data is used as training data for subsequent target detection units.

[0146] The performance comparison results of the detectors under the target-oriented vector mismatch environment are shown in the attached Figure 5 . Figure 5 Some parameters in are set as follows: N = 16, K = 15, R = 32, p = 2, q = 2, P fa =10 -4 ,ICR=15dB, mismatch angle square value cos 2 φ=0.5. The results show that compared with existing range extended target detectors such as 2S-GLRT-PHE and P2S-Rao-PHE, the detector of the proposed method has better detection performance in mismatched environments.

[0147] The present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0148] The present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the method provided in the embodiment of the present invention is implemented.

[0149] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0150] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A target fusion detection method with cooperative suppression of non-uniform clutter and interference, characterized in that: Applied to broadband radar; the method comprises: Based on the skew symmetry of the clutter covariance matrix, unitary transformation is performed on the test data, the clutter covariance matrix, the target coordinate matrix and the interference coordinate matrix; the test data is test sample data collected based on multiple target detection units; the clutter covariance matrix and the interference coordinate matrix are signal matrices corresponding to the clutter component and the interference component of the disturbance signal of the test data, respectively; Based on the interference coordinate matrix, the maximum likelihood estimate of the target parameter vector and the probability density function of the test data, a gradient detection statistic of the range-extended target under the condition of a known clutter skew-symmetric covariance matrix is constructed; Based on the training data, a maximum likelihood estimate of a clutter skew-symmetric covariance matrix is calculated; the clutter skew-symmetric covariance matrix is a clutter covariance matrix after unitary transformation; the training data is data collected based on a plurality of reference units adjacent to the target detection unit; Based on the maximum likelihood estimation of the clutter skew-symmetric covariance matrix and the range-extended target gradient detection statistic, a target detection statistic for cooperative suppression of non-uniform clutter and interference is constructed; Based on the target detection statistics and a preset detection threshold, performing target fusion detection on the target detection unit; The target detection statistics include: Where λ P2S-Gradient-PHE represents the target detection statistic, tr(·) represents the trace of the matrix, is the test data after unitary transformation under the condition of the clutter skew-symmetric covariance matrix, (·) H represents the conjugate transposed matrix, Represents projection to matrix The orthogonal projection matrix of the column space, Represents projection to matrix The orthogonal projection matrix of the orthogonal complement space of the column space, is the target multi-rank subspace after unitary transformation, is the interference multi-rank subspace after unitary transformation.

2. The method according to claim 1, wherein: The test data, clutter covariance matrix, target coordinate matrix and interference coordinate matrix are unitarily transformed. The matrices after unitary transformation include: Where, Z p is the test data after unitary transformation, P p is the target coordinate matrix after unitary transformation, C m×n represents a set of m×n dimensional complex matrices, Q p is the interference coordinate matrix after unitary transformation, is the maximum likelihood estimate of the clutter covariance matrix after unitary transformation, represents the real part value, represents the imaginary part value, and Y represents the training data.

3. The method according to claim 1, wherein: Based on the interference coordinate matrix, the maximum likelihood estimate of the target parameter vector and the probability density function of the test data, a gradient detection statistic of the range-extended target under the condition of a known clutter skew-symmetric covariance matrix is constructed, including: Under the non-target assumption, solving the maximum likelihood estimation of the interference coordinate matrix to obtain a first estimator; Under the target hypothesis, the maximum likelihood estimation of the target parameter vector is solved to obtain the second estimator; Calculating a partial derivative of the target parameter vector using the probability density function of the test data, and then substituting the first estimator into the derivative result to obtain an intermediate relationship; Substituting the intermediate relationship and the second estimator into the range-extended target gradient detection statistic, and combining the maximum likelihood estimation of the clutter power factor under the no-target assumption, the range-extended target gradient detection statistic is constructed under the condition of a known clutter skew-symmetric covariance matrix.

4. The method according to claim 1, wherein: Based on the maximum likelihood estimation of the clutter skew-symmetric covariance matrix and the range-extended target gradient detection statistic, a target detection statistic for cooperative suppression of non-uniform clutter and interference is constructed, including: Substituting the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range-extended target gradient detection statistic, replacing the unknown clutter skew-symmetric covariance matrix in the range-extended target gradient detection statistic with the maximum likelihood estimate of the clutter skew-symmetric covariance matrix, and constructing a target detection statistic for collaborative suppression of non-uniform clutter and interference.

5. The method according to claim 1, wherein: Based on the target detection statistic and a preset detection threshold, the target detection unit performs target fusion detection, including: determining a preset detection threshold based on a preset false alarm probability; Determining whether the target detection statistic is greater than or equal to the preset detection threshold; If yes, determining that the target detection unit has a range-extended target; If not, it is determined that the target detection unit does not have a range-extended target.

6. A target fusion detection system with cooperative suppression of non-uniform clutter and interference, characterized in that: Applicable to broadband radar; including: a transformation module, a first building module, a calculation module, a second building module and a detection module; wherein, The transformation module is used to perform unitary transformation on the test data, the clutter covariance matrix, the target coordinate matrix and the interference coordinate matrix based on the skew symmetry of the clutter covariance matrix; the test data is data collected based on multiple target detection units; the clutter covariance matrix and the interference coordinate matrix are signal matrices corresponding to the clutter component and the interference component of the disturbance signal of the test data, respectively; The first construction module is used to construct a range-extended target gradient detection statistic under the condition of a known clutter skew-symmetric covariance matrix based on the interference coordinate matrix, the maximum likelihood estimate of the target parameter vector and the probability density function of the test data; The calculation module is used to calculate the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the training data; the clutter skew-symmetric covariance matrix is the clutter covariance matrix after unitary transformation; the training data is data collected based on multiple reference units adjacent to the multiple target detection units; The second construction module is used to construct a target detection statistic for cooperative suppression of non-uniform clutter and interference based on the maximum likelihood estimation of the clutter skew-symmetric covariance matrix and the range-extended target gradient detection statistic; The detection module is configured to perform target fusion detection on the target detection unit based on the target detection statistic and a preset detection threshold; The target detection statistics include: Where λ P2S-Gradient-PHE represents the target detection statistic, tr(·) represents the trace of the matrix, is the test data after unitary transformation under the condition of the clutter skew-symmetric covariance matrix, (·) H represents the conjugate transposed matrix, Represents projection to matrix The orthogonal projection matrix of the column space, Represents projection to matrix The orthogonal projection matrix of the orthogonal complement space of the column space of is the target multi-rank subspace after unitary transformation, is the interference multi-rank subspace after unitary transformation.

7. The system according to claim 6, characterized in that: The first building block is further configured to: Under the non-target assumption, solving the maximum likelihood estimation of the interference coordinate matrix to obtain a first estimator; Under the target hypothesis, the maximum likelihood estimation of the target parameter vector is solved to obtain the second estimator; Calculating a partial derivative of the target parameter vector using the probability density function of the test data, and then substituting the first estimator into the derivative result to obtain an intermediate relationship; Substituting the intermediate relationship and the second estimator into the range-extended target gradient detection statistic, and combining the maximum likelihood estimation of the clutter power factor under the no-target assumption, the range-extended target gradient detection statistic is constructed under the condition of a known clutter skew-symmetric covariance matrix.

8. The system according to claim 6, characterized in that: The detection module is further used to: determining a preset detection threshold based on a preset false alarm probability; Determining whether the target detection statistic is greater than or equal to the preset detection threshold; If yes, determining that the target detection unit has a range-extended target; If not, it is determined that the target detection unit does not have a range-extended target.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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