CN115575946B - Intelligent fusion detection method for radar target oblique symmetry subspace - Google Patents
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Abstract
The invention belongs to the technical field of radar signal processing, and particularly relates to an intelligent fusion detection method for a radar target oblique symmetry subspace. In a broadband radar detection scene, aiming at the problem of target detection performance degradation of a subspace radar, clutter oblique symmetric structure information is fully utilized, the actual demand on training samples is reduced, the estimation precision of an unknown clutter covariance matrix is improved, a distance expansion target intelligent detector with a closed form is constructed under a subspace target coordinate vector model with linearity independence, the demand on the training samples is reduced while the CFAR characteristic of the detector is ensured, the adaptability of the broadband radar to target types is further improved, and the radar target oblique symmetric subspace intelligent fusion detection performance is improved.
Description
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to an intelligent fusion detection method for a radar target oblique symmetry subspace.
Background
Target detection is an important field of signal processing research and is the basis for target identification, tracking and imaging. Compared with the conventional narrow-band radar, the broadband radar has the advantages of high distance resolution, strong anti-interference capability, low interception probability and the like, and the target detection by using the broadband radar has important military and civil values. The distance resolution unit of the conventional narrow-band radar is generally far larger than the geometric size of a common target, and the target echo signal occupies only one distance resolution unit and is presented as a 'point' -shaped target. While the echoes of a wideband radar target are typically distributed in different radial distance units, presenting a "range-extended" target. With the wide application of broadband radar, the radar distance expansion target detection problem is receiving more and more attention, and becomes one of hot spots and difficult problems in the radar signal processing field in recent years.
To achieve adaptive Constant False Alarm Rate (CFAR) detection of distance extended targets, a set of training sample data near the cell to be detected is typically collected when estimating an unknown covariance matrix, each training sample containing only clutter and sharing the same covariance matrix as the data to be detected. In the conventional rank-one signal target detection model, the steering vector of the target is usually assumed to be a known fixed vector, but in a practical environment, there may be a mismatch condition of the steering vector of the target due to beam pointing error and multipath phenomenon. To solve this problem, modeling the target signal with a subspace model may be considered. In the subspace model, the signal is represented as the product of a known subspace matrix and an unknown coordinate matrix. Under the subspace model of the distance expansion target, the subspace distance expansion target detectors (S-GLRT, S-Rao and S-2 SD) based on GLRT, rao and two-step methods can be respectively obtained by using Generalized Likelihood Ratio Test (GLRT) and Rao test.
In a real scene, a small sample environment with limited training sample data acquisition often occurs due to anomalies in terrain conditions and environmental changes. In a small sample environment, the detection performance of the original detector is greatly reduced, and an ideal detection effect is difficult to achieve. When the radar receiver adopts a symmetrical interval linear array or a symmetrical interval pulse sequence, the covariance matrix of the clutter has an hermite oblique symmetrical structure, namely the covariance matrix is symmetrical about a main diagonal and a secondary diagonal at the same time. The oblique symmetry of the clutter covariance matrix structure is equivalent to doubling the number of training data, so that the radar target detection performance in a small sample environment is obviously improved. Under a distance extension target subspace model considering the oblique symmetry priori information of the clutter covariance matrix, the oblique symmetry subspace distance extension target detectors (abbreviated as 1S-PGLRT, PS-Rao, 1S-PWald and 2S-PGLRT respectively) based on GLRT, rao, wald and two-step GLRT can be obtained respectively by utilizing GLRT, rao, wald test criteria. Note that the subspace coordinate vectors of the objects detected by the above-described detectors are linearly related, except for the PS-Rao detector. However, in an actual distance-extended target environment, the target coordinate vectors of different distance-extended units may be linearly independent, so that the target detection criteria under the target coordinate vectors with the subspaces that are linearly independent need to be further optimally designed.
Currently, most subspace distance extension target detector designs mainly aim at the condition that training samples are sufficient, and the detection performance of the target detector is degraded under the condition of small samples with limited training samples. Aiming at the problem of poor detection performance of a subspace radar distance expansion target under the condition of a small sample, how to fully utilize the information of a skewed symmetrical structure, reduce the actual demand on a training sample, improve the estimation precision of an unknown clutter covariance matrix, construct a closed-form distance expansion target intelligent detector under a subspace target coordinate vector model with linearity independence, ensure the CFAR characteristic of the detector, reduce the demand on the training sample, and be a key for improving the intelligent fusion detection capability of the skewed symmetrical subspace of the radar target, and also be one of the current challenges to be solved urgently.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention provides an intelligent fusion detection method for a radar target oblique symmetry subspace.
The technical scheme for solving the technical problems is as follows:
A radar target oblique symmetry subspace intelligent fusion detection method comprises the following steps:
Step 1, acquiring data Z to be detected from K distance units to be detected, and acquiring training samples Y from R distance units adjacent to the distance units to be detected; under the assumption of two targets and no targets, respectively utilizing the joint probability density function of the data Z to be detected and the training sample Y to derive and zero the clutter oblique symmetric covariance matrix structure M, and obtaining the maximum likelihood estimation of the unknown clutter oblique symmetric covariance matrix structure under the assumption of targets and the maximum likelihood estimation of the unknown clutter oblique symmetric covariance matrix structure under the assumption of no targets;
Step 2, deriving and zeroing a target transformation coordinate matrix by utilizing a joint probability density function of data Z to be detected and a training sample Y under the condition of target assumption, obtaining maximum likelihood estimation of an unknown target transformation coordinate matrix, and constructing radar target oblique symmetry subspace intelligent fusion detection method statistic lambda;
Step 3, setting a detection threshold T according to the preset false alarm probability; comparing the detection statistic lambda with a detection threshold T, if lambda is more than or equal to T, judging that a target exists in the current distance unit to be detected, wherein the data to be detected is not used as a training sample of other subsequent distance units to be detected; otherwise, if lambda is less than T, judging that the current distance unit to be detected does not have a target, and taking the data to be detected as training samples of other subsequent distance units to be detected.
Further, the step 1 specifically includes:
Under the assumption of targets, the clutter oblique symmetric covariance matrix structure is derived and zeroed by utilizing the joint probability density function of the data Z to be detected and the training sample Y to obtain the maximum likelihood estimation of M under the assumption of H 1 The method comprises the following steps:
Wherein,
Z k represents an n×1-dimensional to-be-detected data component corresponding to the kth to-be-detected distance unit;
b k represents an r×1-dimensional target subspace complex coordinate vector in the kth distance unit to be detected;
u represents a multi-rank subspace complex matrix of dimension N x r, r represents the rank of matrix U; Training sample Y is represented as an n×r-dimensional complex matrix, and y=c R＝[cK+1,cK+2,…,cK+R],cK+k represents training sample components corresponding to the k+kth reference distance unit; /(I) AndRespectively representing a real matrix set and a complex matrix set in m×n dimensions, (·) H represents a conjugate transpose, |·| represents a determinant of a square matrix, and Re (·) and Im (·) represent a real part and an imaginary part, respectively.
Further, the step 1 specifically further includes:
Under the condition of no target assumption, the clutter oblique symmetric covariance matrix structure is derived and zeroed by utilizing the joint probability density function of the data Z to be detected and the training sample Y to obtain the maximum likelihood estimation of M under the assumption of H 0 The method comprises the following steps:
further, the step 2 specifically includes:
The combined probability density function of the data Z to be detected and the training sample Y under the assumption of the target is utilized to derive and zero the target transformation coordinate matrix B p, and the maximum likelihood estimation of B p under the assumption of H 1 is obtained as follows:
further, the step 2 specifically further includes:
the intelligent fusion detection method for the radar target oblique symmetry subspace comprises the following steps of:
In the method, in the process of the invention, I 2K denotes a unit matrix of 2k×2K dimensions.
Compared with the prior art, the invention has the following technical effects:
1) The prior information of the oblique symmetrical structure of the clutter covariance matrix is fully utilized, the data to be detected and the training samples are jointly utilized, the estimation precision of the unknown clutter oblique symmetrical covariance matrix structure is improved, the requirement on the training samples is reduced, and a favorable support is provided for realizing intelligent fusion detection of the radar target oblique symmetrical subspace;
2) Constructing a subspace oblique symmetry generalized likelihood ratio test detector under uniform clutter, wherein the detector has an expression in a closed form, and the detection performance of the detector is superior to that of the existing unstructured distance expansion target subspace detector and the oblique symmetry distance expansion target subspace detector;
3) The subspace distance extension target signal model is constructed, the problem that the rank-one signal model is difficult to cope with target steering vector mismatch is solved, and the robustness of the broadband radar to the target steering vector mismatch condition is improved;
4) The detector of the method is applicable to subspace targets with linear independent coordinates, and the intelligent adaptability of the detector to the types of targets is improved;
5) The method is suitable for partial non-broadband radar detection situations, for example, detection of large targets or detection of space adjacent point target groups (conditions of naval vessel formation, airplane formation, vehicle formation and the like) moving at the same speed by using low/medium resolution radars, and has good application prospect.
Drawings
FIG. 1 is a functional block diagram of a radar target oblique symmetry subspace intelligent fusion detection method of the invention;
FIG. 2 is a graph comparing the detection performance of the method of the present invention with that of an existing unstructured distance-extended target subspace detector;
FIG. 3 is a graph comparing the detection performance of the inventive method with that of the prior art oblique symmetric distance extended target subspace detector.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the examples are illustrated for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
In a broadband radar detection scene, aiming at the problem of degradation of target detection performance of a subspace radar, how to fully utilize clutter oblique symmetric structure information, reduce the actual demand on a training sample, improve the estimation precision of an unknown clutter covariance matrix, construct a distance expansion target intelligent detector with a closed form under a subspace target coordinate vector model with linearity independence, reduce the demand on the training sample while ensuring the CFAR characteristic of the detector, further improve the adaptability of the broadband radar to the target type, and improve the intelligent fusion detection performance of the radar target oblique symmetric subspace.
The embodiment provides an intelligent fusion detection method for a radar target oblique symmetry subspace, which specifically comprises the following steps:
Step 1, acquiring data Z to be detected from K distance units to be detected, and acquiring training samples Y from R distance units adjacent to the distance units to be detected; under the assumption of a target and the assumption of no target, respectively utilizing a joint probability density function of the data Z to be detected and the training sample Y to derive a clutter oblique symmetry covariance matrix structure M and setting zero to obtain maximum likelihood estimation of an unknown clutter oblique symmetry covariance matrix structure under the assumption of the target, and simultaneously obtaining the maximum likelihood estimation of the unknown clutter oblique symmetry covariance matrix structure under the assumption of no target; and then constructing subspace oblique symmetry generalized likelihood ratio test intermediate statistics.
The method comprises the following specific steps:
For a coherent radar system with the space-time joint channel number of N, assuming that a target possibly occupies K continuous distance units to be detected, the data to be detected corresponding to the echo signals of the coherent radar system can be represented as an NxK-dimensional complex matrix Z= [ Z 1,z2,…,zK],zk ] representing an Nx1-dimensional data component to be detected corresponding to a kth distance unit to be detected.
Under the assumption H 0 that no target exists, the data to be detected Z contains only an n×k-dimensional complex matrix of clutter components c= [ C 1,c2,…,cK ], where n×1-dimensional complex vector C k (k=1, 2..once., K) represents clutter components in the kth distance unit to be detected, which obeys a complex circular gaussian distribution with zero-mean covariance matrix of n×n-dimensional complex matrix M, and clutter vectors between different distance units are independently and equidistributed.
Under the assumption H 1 that a target exists, the data Z to be detected consists of a signal component matrix S and a clutter component matrix C in N multiplied by K dimensions; the signal component matrix S may be expressed as a product of a known n×r multi-rank subspace complex matrix U and an r×k unknown complex coordinate matrix B, where b= [ B 1,b2,…,bK],bk ] represents an r×1-dimensional target subspace complex coordinate vector in the kth distance unit to be detected, and r represents the rank of the matrix U.
In order to estimate the unknown M, R observation data are obtained from R pure clutter reference distance units adjacent to the distance unit to be detected, and the training sample Y corresponding to the echo signal may be represented as an n×r-dimensional complex matrix y=c R＝[cK+1,cK+2,…,cK+R],cK+k representing the training sample component corresponding to the k+kth reference distance unit. Wherein the n×1-dimensional complex vector c t (t=k+1, k+2, …, k+r) is subjected to complex round gaussian distribution with zero-mean covariance matrix being n×n-dimensional complex matrix M, and training sample components between different distance units are independently and uniformly distributed.
When the radar receiver adopts a symmetric interval linear array or a symmetric interval pulse sequence, the clutter covariance matrix M and the signal multi-rank subspace matrix U have an oblique symmetric structure, namely, m=jm *J,U＝JU* is satisfied. Wherein (-) * represents the conjugate, J represents an N-dimensional permutation matrix in which the diagonal element is 1 and the other elements are 0. The introduction of the oblique symmetric structure can further improve the estimation precision of the unknown clutter oblique symmetric covariance matrix structure, further reduce the requirement for training samples, and provide favorable conditions for realizing intelligent fusion detection of the radar target oblique symmetric subspace.
Modeling distance extended target detection as a binary hypothesis testing problem:
The generalized likelihood ratio test criterion may be expressed as:
Where T is the detection threshold, f 1 (Z, y|b, M) and f 0 (Z, y|m) represent the joint probability density function of the data Z to be detected and the training sample Y under the assumption of H 1 and H 0, respectively.
Using the oblique symmetry information in the clutter oblique symmetry covariance matrix structure M, the joint probability density function f 1 (Z, y|b, M) of the data Z to be detected and the training sample Y under the target hypothesis H 1 can be expressed as:
Wherein,
Where j is an imaginary unit, |·| represents the determinant of the square matrix, tr (·) represents the trace of the matrix, re (·) and Im (·) represent the real and imaginary parts respectively,AndRespectively representing a set of real matrices and a set of complex matrices in m x n dimensions, (·) H represents the conjugate transpose.
Using the oblique symmetry information in the clutter oblique symmetry covariance matrix structure M, the joint probability density function f 0 (Z, y|m) of the data Z to be detected and the training sample Y under the no-target hypothesis H 0 can be expressed as:
Wherein,
Under the target assumption H 1, the clutter oblique symmetric covariance matrix structure is derived and set to zero by utilizing the joint probability density function of the data Z to be detected and the training sample Y, namelyObtaining the maximum likelihood estimate/>, of M under the assumption of H 1 The method comprises the following steps:
the above-mentioned combined utilization of the data to be detected and the training sample improves the estimation accuracy of the unknown clutter oblique symmetry covariance matrix structure, and reduces the requirement for the training sample.
Under the no-target assumption H 0, the clutter oblique symmetric covariance matrix structure is derived and set to zero by utilizing the joint probability density function of the data Z to be detected and the training sample Y, namelyObtaining a maximum likelihood estimate of M under the H 0 hypothesisThe method comprises the following steps:
the above-mentioned combined utilization of the data to be detected and the training sample improves the estimation accuracy of the unknown clutter oblique symmetry covariance matrix structure, and reduces the requirement for the training sample.
Substituting equation (6) and equation (7) into equation (2), the detection statistic λ can be equivalently expressed as:
step 2, deriving and setting zero for the target transformation coordinate matrix by utilizing the joint probability density function of the data Z to be detected and the training sample Y under the condition of target assumption, and obtaining the maximum likelihood estimation of the unknown target transformation coordinate matrix; constructing statistics lambda of an intelligent fusion detection method of the radar target oblique symmetry subspace;
The method comprises the following specific steps:
Deriving and zeroing the target transformation coordinate matrix B p by utilizing a joint probability density function of the data Z to be detected and the training sample Y under the target hypothesis H 1, namely The maximum likelihood estimate for B p under the H 1 hypothesis is obtained as:
Substituting the formula (9) into the formula (8) to obtain the intelligent fusion detection method statistic of the radar target oblique symmetry subspace, wherein the statistic is as follows:
In the method, in the process of the invention, I 2K denotes a unit matrix of 2k×2k dimensions.
The detector of the method of the invention is also called a subspace oblique symmetry generalized likelihood ratio test detector under uniform clutter (called Psub-GLRT for short), has an expression in a closed form, does not need iterative operation, and has CFAR characteristics for M.
Step 3, setting a detection threshold T according to preset false alarm probability in order to maintain the CFAR characteristic of the detection method; comparing the detection statistic lambda with a detection threshold T, if lambda is more than or equal to T, judging that a target exists in the current distance unit to be detected, wherein the data to be detected is not used as a training sample of other subsequent distance units to be detected; otherwise, if lambda is less than T, judging that the current distance unit to be detected does not have a target, and taking the data to be detected as training samples of other subsequent distance units to be detected.
To verify the effectiveness of the method of the present invention, this embodiment provides two examples, the first example being directed to a sea detection environment and the second example being directed to a ground detection environment.
Example 1
Referring to fig. 1 of the specification, the embodiment of example 1 is divided into the following steps:
Step A1, carrying out radar irradiation on a sea area to be detected by using a sea detection radar to obtain to-be-detected data Z of K to-be-detected distance units; and carrying out radar irradiation on a non-target range around the sea area to be detected to obtain training samples Y of R reference distance units only containing pure sea clutter.
The data Z to be detected and the training samples Y are sent to a decorrelation processing module, wherein in the decorrelation processing module, clutter oblique symmetry covariance matrix structure estimation for transforming the data Z p to be detected and based on the training samples is obtained according to the method (4)
In one aspect, Z p andSending the data to be detected and training samples under the assumption of H 0 to a joint probability density function module according to Z p andObtaining a joint probability density function formula (5) of the data to be detected and the training sample under the assumption of H 0; transmitting the joint probability density function of the data to be detected and the training sample under the assumption of H 0 to a maximum likelihood estimation module of a clutter oblique symmetry covariance matrix structure under the assumption of H 0, deriving and setting zero for the clutter oblique symmetry covariance matrix structure M by utilizing the joint probability density function of the data to be detected and the training sample under the assumption of no targets, and obtaining the maximum likelihood estimation/>, of M under the assumption of H 0 according to a formula (7)
On the other hand, Z p andSending the data to be detected and training samples under the assumption of H 1 to a joint probability density function module according to Z p andObtaining a joint probability density function formula (3) of the data to be detected and the training sample under the assumption of H 1; transmitting the joint probability density function of the data to be detected and the training sample under the assumption of H 1 to a maximum likelihood estimation module of a clutter oblique symmetry covariance matrix structure under the assumption of H 1, deriving and setting zero for the clutter oblique symmetry covariance matrix structure M by utilizing the joint probability density function of the data to be detected and the training sample under the assumption of a target, and obtaining the maximum likelihood estimation/>, of M under the assumption of H 1 according to a formula (6)
Notably, in the step A1, the subspace distance extension target signal model is constructed, the problem that the rank-one signal model is difficult to cope with target steering vector mismatch is avoided, and the robustness of the broadband radar to the offshore target steering vector mismatch condition is improved; in addition, the method fully utilizes the prior information of the oblique symmetrical structure of the sea clutter covariance matrix, and the data to be detected and the training samples are jointly utilized, so that the estimation precision of the unknown sea clutter oblique symmetrical covariance matrix structure is improved, the requirement on the training samples is reduced, and a favorable support is provided for realizing intelligent fusion detection of radar target oblique symmetrical subspaces.
Step A2. WillSending to a maximum likelihood estimation module of a coordinate matrix under the assumption of H 1, deriving and zeroing the target transformation coordinate matrix by utilizing a joint probability density function of data to be detected and training samples under the assumption of a target, and obtaining the maximum likelihood estimation/>' of the unknown target transformation coordinate matrix B p according to the condition (9)Finally, statistics lambda of the intelligent fusion detection method of the radar target oblique symmetry subspace is constructed.
It is worth noting that the method of the invention constructs the subspace oblique symmetry generalized likelihood ratio test detector under the uniform clutter, the detector has the closed expression, the structure of the detection statistic is simple, and the engineering realization is convenient. Meanwhile, the detector of the method is applicable to subspace targets with linear independent coordinates, and the intelligent adaptability of the detector to the target type is improved.
Step A3, setting a detection threshold T according to a preset false alarm probability:
Specifically, the false alarm probability is set to be P fa, and the detection threshold T is calculated according to 100/P fa actually measured sea clutter data accumulated in the earlier stage according to the Monte Carlo method. Considering that the sea clutter acquisition difficulty is high, if the actually obtained pure sea clutter measured data quantity R is less than 100/Pfa, the missing 100/Pfa-R clutter data can be obtained through simulation by utilizing a sea clutter simulation model, and model parameters are reasonably estimated and set according to the obtained pure sea clutter measured data.
Further, comparing the detection statistic lambda with a detection threshold T, if lambda is more than or equal to T, judging that targets exist in the current K distance units to be detected, wherein the data to be detected are not used as training samples of other subsequent distance units to be detected; otherwise, if lambda < T, judging that targets do not exist in the current K distance units to be detected, and taking the data to be detected as training samples of other subsequent distance units to be detected.
The comparison of the detection performance of the method of the invention on the matching signal with the existing detection method is shown in figure 2. The results show that compared with the existing unstructured distance extension target subspace detectors (S-GLRT, S-Rao, S-2SD and the like), the method has better detection performance on the broadband radar weak target under uniform sea clutter.
Example 2
Referring to fig. 1 of the specification, the embodiment of example 2 is divided into the following steps:
B1, carrying out radar irradiation on a region to be detected by using a sea detection radar to obtain data Z to be detected of K distance units to be detected; and carrying out radar irradiation on the non-target range around the region to be detected to obtain training samples Y of R reference distance units only containing pure ground clutter. The data Z to be detected and the training samples Y are sent to a decorrelation processing module, wherein in the decorrelation processing module, clutter oblique symmetry covariance matrix structure estimation for transforming the data Z p to be detected and based on the training samples is obtained according to the method (4)
In one aspect, Z p andSending the data to be detected and training samples under the assumption of H 0 to a joint probability density function module according to Z p andObtaining a joint probability density function formula (5) of the data to be detected and the training sample under the assumption of H 0; transmitting the joint probability density function of the data to be detected and the training sample under the assumption of H 0 to a maximum likelihood estimation module of a clutter oblique symmetry covariance matrix structure under the assumption of H 0, deriving and setting zero for the clutter oblique symmetry covariance matrix structure M by utilizing the joint probability density function of the data to be detected and the training sample under the assumption of no targets, and obtaining the maximum likelihood estimation/>, of M under the assumption of H 0 according to a formula (7)
On the other hand, Z p andSending the data to be detected and training samples under the assumption of H 1 to a joint probability density function module according to Z p andObtaining a joint probability density function formula (3) of the data to be detected and the training sample under the assumption of H 1; transmitting the joint probability density function of the data to be detected and the training sample under the assumption of H 1 to a maximum likelihood estimation module of a clutter oblique symmetry covariance matrix structure under the assumption of H 1, deriving and setting zero for the clutter oblique symmetry covariance matrix structure M by utilizing the joint probability density function of the data to be detected and the training sample under the assumption of a target, and obtaining the maximum likelihood estimation/>, of M under the assumption of H 1 according to a formula (6)
Notably, in the step B1, the method constructs a subspace distance extension target signal model, avoids the problem that the rank-one signal model is difficult to cope with target steering vector mismatch, and improves the robustness of the broadband radar to the situation of target steering vector mismatch on the ground; in addition, the method fully utilizes the prior information of the oblique symmetrical structure of the sea clutter covariance matrix, and the data to be detected and the training samples are jointly utilized, so that the estimation precision of the unknown ground clutter oblique symmetrical covariance matrix structure is improved, the requirement on the training samples is reduced, and a favorable support is provided for realizing intelligent fusion detection of radar target oblique symmetrical subspaces.
Step B2. WillSending to a maximum likelihood estimation module of a coordinate matrix under the assumption of H 1, deriving and zeroing the target transformation coordinate matrix by utilizing a joint probability density function of data to be detected and training samples under the assumption of a target, and obtaining the maximum likelihood estimation/>' of the unknown target transformation coordinate matrix B p according to the condition (9)Finally, statistics lambda of the intelligent fusion detection method of the radar target oblique symmetry subspace is constructed.
It is worth noting that the method of the invention constructs the subspace oblique symmetry generalized likelihood ratio test detector under the uniform clutter, the detector has the closed expression, the structure of the detection statistic is simple, and the engineering realization is convenient. Meanwhile, the detector of the method is applicable to subspace targets with linear independent coordinates, and the intelligent adaptability of the detector to the target type is improved.
Step B3, setting a detection threshold T according to a preset false alarm probability:
Specifically, the false alarm probability is set to be P fa, and the detection threshold T is calculated according to 100/P fa actually measured ground clutter data accumulated in the earlier stage according to the Monte Carlo method. Considering that the ground clutter acquisition difficulty is high, if the actually obtained pure ground clutter measured data quantity R is less than 100/Pfa, the missing 100/Pfa-R clutter data can be obtained through simulation by using a ground clutter simulation model, and model parameters are reasonably estimated and set according to the obtained pure ground clutter measured data.
Further, the detection statistic lambda is compared with a detection threshold T, if lambda is more than or equal to T, the fact that targets do not exist in the current K distance units to be detected is judged, and the data to be detected are used as training samples of other subsequent distance units to be detected.
The detection performance of the method of the invention is compared with that of the existing oblique symmetrical distance extension target subspace detector, and is shown in figure 3. The results show that compared with the existing oblique symmetrical distance extended target subspace detectors (1S-PGLRT, PS-Rao, 1S-PWald and 2S-PGLRT), the method has better detection performance on broadband radar weak targets under uniform clutter.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (5)
1. The intelligent fusion detection method for the oblique symmetrical subspace of the radar target is characterized by comprising the following steps of:
Step 1, acquiring data Z to be detected from K distance units to be detected, and acquiring training samples Y from R distance units adjacent to the distance units to be detected; under the assumption of two targets and no targets, respectively utilizing the joint probability density function of the data Z to be detected and the training sample Y to derive and zero the clutter oblique symmetric covariance matrix structure M, and obtaining the maximum likelihood estimation of the unknown clutter oblique symmetric covariance matrix structure under the assumption of targets and the maximum likelihood estimation of the unknown clutter oblique symmetric covariance matrix structure under the assumption of no targets;
Step 2, deriving and zeroing a target transformation coordinate matrix by utilizing a joint probability density function of data Z to be detected and a training sample Y under the condition of target assumption, obtaining maximum likelihood estimation of an unknown target transformation coordinate matrix, and constructing radar target oblique symmetry subspace intelligent fusion detection method statistic lambda;
step 3, setting a detection threshold T according to the preset false alarm probability; comparing the detection statistic lambda with a detection threshold T, if lambda is more than or equal to T, judging that a target exists in the current distance unit to be detected, wherein the data to be detected is not used as a training sample of other subsequent distance units to be detected; otherwise, if lambda < T, judging that the current distance unit to be detected does not have a target, and taking the data to be detected as training samples of other subsequent distance units to be detected.
2. The intelligent fusion detection method for the oblique symmetry subspace of the radar target according to claim 1, wherein the step 1 specifically comprises:
Under the assumption of targets, the clutter oblique symmetric covariance matrix structure is derived and zeroed by utilizing the joint probability density function of the data Z to be detected and the training sample Y to obtain the maximum likelihood estimation of M under the assumption of H 1 The method comprises the following steps:
Wherein,
Z k represents an n×1-dimensional to-be-detected data component corresponding to the kth to-be-detected distance unit;
b k represents an r×1-dimensional target subspace complex coordinate vector in the kth distance unit to be detected;
u represents a multi-rank subspace complex matrix of dimension N x r, r represents the rank of matrix U; Training sample Y is represented as an n×r-dimensional complex matrix, and y=c R＝[cK+1,cK+2,…,cK+R],cK+k represents training sample components corresponding to the k+kth reference distance unit; /(I) AndRespectively representing a real matrix set and a complex matrix set in m multiplied by n dimensions, (. Cndot.) H represents a conjugate transpose, and Re (. Cndot.) and Im (. Cndot.) respectively represent a real part and an imaginary part; j represents an n×n-dimensional permutation matrix in which diagonal elements are 1 and other elements are 0.
3. The intelligent fusion detection method of the oblique symmetry subspace of the radar target according to claim 2, wherein the step 1 specifically further comprises:
Under the condition of no target assumption, the clutter oblique symmetric covariance matrix structure is derived and zeroed by utilizing the joint probability density function of the data Z to be detected and the training sample Y to obtain the maximum likelihood estimation of M under the assumption of H 0 The method comprises the following steps:
4. the intelligent fusion detection method for the oblique symmetry subspace of the radar target according to claim 2, wherein the step 2 specifically comprises:
The combined probability density function of the data Z to be detected and the training sample Y under the assumption of the target is utilized to derive and zero the target transformation coordinate matrix B p, and the maximum likelihood estimation of B p under the assumption of H 1 is obtained as follows:
5. The intelligent fusion detection method for the oblique symmetry subspace of the radar target according to claim 4, wherein the step 2 specifically further comprises:
the intelligent fusion detection method for the radar target oblique symmetry subspace comprises the following steps of:
In the method, in the process of the invention, I 2K denotes a unit matrix of 2k×2K dimensions.
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