A radar extended target adaptive detection method and system in a non-uniform environment

Abstract

The invention discloses a radar extended target adaptive detection method and system in a non-uniform environment, belonging to the field of radar target detection. Firstly, it is assumed that there is an unknown power mismatch between each training sample and a unit to be detected, and then the power mismatch and a signal coordinate matrix are obtained by iterative estimation. Then, the clutter covariance matrix estimation value is constructed by using the data to be detected, the training sample, the signal coordinate matrix and the power mismatch, and then the test statistic is calculated, and the detection threshold is determined according to the false alarm probability. Finally, the size between the test statistic and the detection threshold is compared. If the test statistic is greater than the detection threshold, it is determined that the target exists, otherwise it is determined that the target does not exist. The detection method and system of the invention take into account the detection problem of extended targets under power heterogeneous clutter in a non-uniform environment, obtain the clutter covariance matrix by iterative estimation, and realize clutter suppression, signal accumulation and target judgment in an integrated manner, simplify the process and improve the detection performance.

Description

Technical Field

[0001] The present invention belongs to the field of radar target detection, and more specifically, relates to a radar extended target adaptive detection method and system in a non-uniform environment. Background Art

[0002] With the development of radar technology, the performance of radar is constantly improving, and accordingly, the resolution of radar is constantly improving. For high-resolution radars, targets often extend in the distance dimension, that is, they occupy multiple radar distance resolution units, and the unknown parameters involved are more complex. At the same time, due to factors such as clutter fluctuations, the environment often exhibits non-uniform characteristics, and the traditional Gaussian model cannot well characterize this type of clutter. In recent years, the adaptive detection problem of extended targets has been widely studied, and scholars have successively proposed partial uniform environment models, composite Gaussian models, etc. to model clutter in non-uniform environments. For example, Kraut et al. proposed a generalized likelihood ratio detector in a partially uniform environment in 1999, namely an adaptive coherence estimator, and the generalized form of its corresponding extended target can be derived from the two-step generalized likelihood ratio criterion; Xu Shuwen et al. proposed an adaptive optimal detector that matches the clutter texture distribution in 2016, but in a non-uniform environment, clutter does not necessarily satisfy the given texture distribution, and the detector designed according to the prior distribution of texture is bound to bring certain performance losses. In a complex and changeable real environment, clutter returned by different distance units often exhibits heterogeneous characteristics, that is, there is also a power mismatch between training samples.

[0003] Therefore, how to improve the detection performance of radar extended targets in a non-uniform environment is a problem that technical personnel in this field need to solve urgently. Summary of the invention

[0004] In view of the above problems or improvement needs of the prior art, the present invention provides a method and system for adaptive detection of radar extended targets in a non-uniform environment, which aims to improve the detection performance of radar extended targets in a non-uniform environment.

[0005] To achieve the above object, according to one aspect of the present invention, a method for adaptively detecting radar extended targets in a non-uniform environment is provided, comprising the following steps:

[0006] Step 1: Assume that the signal is in a known subspace , the data matrix to be detected , the radar receives near the unit to be detected training samples , ,in, is the number of radar system channels, is the expanded dimension of the target, represents the dimension of the signal subspace, symbol represents a complex domain;

[0007] Step 2: Based on the data to be tested and training samples , , the joint probability density function and the signal matrix , get the signal coordinate matrix and the power mismatch between the training sample and the unit to be detected , , the iterative estimation expression and , , where the superscript Indicates Iteration estimate, superscript Indicates an estimate;

[0008] Step 3: Given an initial value of the power mismatch, denoted as , , perform iterative estimation and obtain the final and , ,in Indicates the maximum number of iterations;

[0009] Step 4: Utilize the data to be tested , training samples , , the final estimate of the signal coordinate matrix The final estimate of the power mismatch is , , construct an iterative estimate of the clutter covariance matrix ;

[0010] Step 5: Use the iterative estimate of the covariance matrix 、Data to be tested Sum signal matrix Constructing the test statistic ;

[0011] Step 6: Using the test statistic and false alarm probability to determine the detection threshold ;

[0012] Step 7: Compare the test statistics With detection threshold The size between them is used to determine whether the target exists.

[0013] Furthermore, the signal coordinate matrix in step 2 and the power mismatch between the training sample and the data to be detected , , the iterative estimation expressions are:

[0014] ;

[0015] and

[0016] , ,

[0017] in, , , superscript represents the conjugate transpose.

[0018] Furthermore, the initial value expression of the power mismatch amount in step 3 is:

[0019] , ,

[0020] in, Represents the generalized inverse, and the final and , , respectively:

[0021] ;

[0022] and

[0023] , ,

[0024] in, ;

[0025] ;

[0026] Furthermore, the iterative estimate of the clutter covariance matrix in step 4 is The expression is:

[0027] ;

[0028] Furthermore, the test statistic in step 5 is , whose expression is:

[0029] ;

[0030] in, Represents the trace of the matrix.

[0031] Furthermore, the detection threshold in step 6 The method to determine is:

[0032] ;

[0033] in, , is the number of Monte Carlo simulations, is the set false alarm probability value, For rounding operation, For sequence Arrange from largest to smallest The maximum value, is the first data to be detected that contains only clutter components Experiments, in which .

[0034] Furthermore, in step 7, the determination of whether the target exists is performed according to the following results: if the test statistic Greater than the detection threshold , then the target is judged to exist, otherwise the target is judged not to exist.

[0035] On the other hand, the present invention provides a radar extended target adaptive detection system in a non-uniform environment, comprising:

[0036] Parameter estimation algorithm module: used to calculate the signal coordinate matrix and the iterative estimation value of the power mismatch between the training sample and the data to be detected by using the joint probability density function of the data to be detected and the training sample;

[0037] Covariance matrix construction module: used to construct the clutter covariance matrix using the data to be detected, the training samples, the signal coordinate matrix and the power mismatch between the training samples and the data to be detected;

[0038] Test statistic calculation module: used to construct test statistics using the data to be detected, signal subspace and clutter covariance matrix;

[0039] Detection threshold determination module: used to determine the detection threshold according to the false alarm probability;

[0040] Target judgment module: used to compare the test statistic and the detection threshold and judge whether the target exists. If the test statistic is greater than the detection threshold, the target is judged to exist, otherwise the target is judged not to exist.

[0041] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0042] (1) Taking into account the non-uniform characteristics of the environment, the power mismatch between each training sample and the clutter covariance matrix of the unit to be detected is obtained by iterative estimation, which provides a guarantee for improving the estimation of the covariance matrix of the unit to be detected.

[0043] (2) By constructing an iterative covariance matrix estimate, an effective estimation of the covariance matrix of the detection unit is achieved without relying on the statistical distribution of clutter.

[0044] (3) By constructing a test statistic, clutter suppression, signal accumulation, and target detection are integrated.

[0045] (4) The present invention can solve the problem of radar extended target detection in a non-uniform environment and improve the radar target detection performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the principle of a radar extended target adaptive detection method in a non-uniform environment according to the present invention;

[0047] Figure 2 This is a structural framework diagram of a radar extended target adaptive detection system in a non-uniform environment according to the present invention;

[0048] Figure 3 This is a comparison chart of detection probabilities between the method of the present invention and existing methods under different signal-to-noise ratios. DETAILED DESCRIPTION

[0049] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0051] use Dimensional Matrix Indicates the data to be detected. is the number of system channels, is the number of distance units occupied by the extended target. Next, data to be tested Contains only clutter components , assuming Each column is independent and identically distributed, record Listed as , Subject to zero mean and covariance matrix is the complex Gaussian distribution of In hypothesis testing Down Contains clutter components Sum signal component , It can be written as ,in, dimensional column full rank matrix Span the signal subspace, Dimensional Matrix Represents the coordinates of the signal in the subspace. In a practical environment, the clutter covariance matrix is usually unknown. , assuming there is training samples, denoted as , . Contains only clutter components Therefore, the problem to be tested can be expressed as follows:

[0052] ;

[0053] in, , Assumptions Subject to zero mean and covariance matrix is the complex Gaussian distribution of . Then in a non-uniform environment:

[0054] , ,

[0055] in , , is an unknown parameter, representing the different power mismatches between each training sample and the data to be detected.

[0056] The purpose of the present invention is to improve the radar extended target detection performance in a non-uniform environment. To achieve the above purpose, please refer to Figure 1 As shown, this embodiment provides a radar extended target adaptive detection method in a non-uniform environment, including:

[0057] Step 1: Assume that the signal is in a known subspace , the data matrix to be detected , the radar receives near the unit to be detected training samples , ,in, is the number of radar system channels, is the expanded dimension of the target, represents the dimension of the signal subspace, symbol represents a complex domain;

[0058] Step 2: Based on the data to be tested and training samples , , the joint probability density function and the signal matrix , and get the signal coordinate matrix and the power mismatch between the training sample and the unit to be detected , , the iterative estimation expression and , , where the superscript Indicates Iteration estimate, superscript Indicates an estimate;

[0059] Step 3: Given an initial value of the power mismatch, denoted as , , perform iterative estimation and obtain the final and , ,in Indicates the maximum number of iterations;

[0060] Step 4: Utilize the data to be tested , training samples , , the final estimate of the signal coordinate matrix The final estimate of the power mismatch is , , construct an iterative estimate of the clutter covariance matrix ;

[0061] Step 5: Use the iterative estimate of the covariance matrix 、Data to be tested Sum signal matrix Constructing the test statistic ;

[0062] Step 6: Using the test statistic and false alarm probability to determine the detection threshold ;

[0063] Step 7: Compare the test statistics With detection threshold The size between them is used to determine whether the target exists.

[0064] The signal coordinate matrix in step 2 and the power mismatch between the training sample and the data to be detected , , the iterative estimation expressions are:

[0065] ;

[0066] and

[0067] , ,

[0068] in, , , superscript represents the conjugate transpose.

[0069] The initial value expression of the power mismatch in step 3 is:

[0070] , ,

[0071] in, Represents the generalized inverse, and the final and , , respectively:

[0072] ;

[0073] and

[0074] , ,

[0075] in, ;

[0076] ;

[0077] The iterative estimate of the clutter covariance matrix in step 4 is The expression is:

[0078] ;

[0079] The test statistic in step 5 , whose expression is:

[0080] ;

[0081] in, Represents the trace of the matrix.

[0082] The detection threshold in step 6 The method for determining is:

[0083] ;

[0084] in, , is the number of Monte Carlo simulations, is the set false alarm probability value, For rounding operation, For sequence Arrange from largest to smallest The maximum value, is the first data to be detected that contains only clutter components Experiments, in which .

[0085] In step 7, the determination of whether the target exists is performed according to the following results: if the test statistic Greater than the detection threshold , then the target is judged to exist, otherwise the target is judged not to exist.

[0086] See also Figure 2 As shown, the present invention provides a radar extended target adaptive detection system in a non-uniform environment, comprising:

[0087] Parameter estimation algorithm module: used to calculate the signal coordinate matrix and the iterative estimation value of the power mismatch between the training sample and the data to be detected by using the joint probability density function of the data to be detected and the training sample;

[0088] Covariance matrix construction module: used to construct the clutter covariance matrix using the data to be detected, the training samples, the signal coordinate matrix and the power mismatch between the training samples and the data to be detected;

[0089] Test statistic calculation module: used to construct test statistics using the data to be detected, signal subspace and clutter covariance matrix;

[0090] Detection threshold determination module: used to determine the detection threshold according to the false alarm probability;

[0091] Target judgment module: used to compare the test statistic and the detection threshold and judge whether the target exists. If the test statistic is greater than the detection threshold, the target is judged to exist; otherwise, the target is judged not to exist.

[0092] The effect of the present invention is further described below in conjunction with simulation experiments.

[0093] See also Figure 3 As shown in FIG. , it is a comparison diagram of the detection probability of the method of the present invention and the existing method under different signal-to-noise ratios. To simplify the calculation, let the number of radar system channels be , target expansion dimension , the signal subspace dimension , the number of training samples , the number of iterations is 15. The K-distributed clutter whose texture component obeys the gamma distribution is used in the simulation experiment. Its speckle component is a zero-mean complex Gaussian random process, and the covariance matrix can be modeled as , ,in False Alarm Probability , the signal-to-noise ratio is defined as .

[0094] As can be seen from the figure, compared with the adaptive normalized matched filter (ANMF) that uses recursive estimation of the clutter covariance matrix, the method proposed in the present invention has a performance improvement of about 3dB when the detection probability is 0.8, that is, under the premise of unchanged detection probability, the required signal-to-noise ratio is reduced by 3dB.

[0095] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to the specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A radar extended target adaptive detection method in a non-uniform environment, characterized in that: include: Step 1: Assume that the signal is in a known subspace Data matrix to be tested The radar receives L training samples z near the unit to be detected l , l = 1, 2, ..., L, where N is the number of radar system channels, K is the expansion dimension of the target, p represents the dimension of the signal subspace, and the symbol represents a complex domain; Step 2: Based on the data to be tested Z and the training sample z l , l=1,2,…,L, and the signal matrix H, we can get the signal coordinate matrix Ψ and the power mismatch γ between the training sample and the unit to be detected l , l=1,2,…,L, the iterative estimation expression and Among them, the superscript t+1 represents the estimated value of the t+1th iteration, and the superscript ^ represents the estimate; Step 3: Given an initial value of the power mismatch, denoted as Perform iterative estimation to obtain the final and where t max Indicates the maximum number of iterations; Step 4: Use the data to be tested Z and training samples z l , l = 1, 2, ..., L, the final estimate of the signal coordinate matrix The final estimate of the power mismatch is Constructing iterative estimates of the clutter covariance matrix Step 5: Use the iterative estimate of the covariance matrix The test data Z and signal matrix H are used to construct the test statistic t iter ; Step 6: Use the test statistic t iter The detection threshold η is determined by the false alarm probability iter ; Step 7: Compare the test statistic t iter and the detection threshold η iter The size between them and determines whether the target exists; The signal coordinate matrix Ψ in step 2 and the power mismatch γ between the training sample and the data to be detected l , l=1,2,…,L, the iterative estimation expressions are: and in, Superscript (·) H represents conjugate transpose; The initial value expression of the power mismatch in step 3 is: in,(·) - Represents the generalized inverse, and the final and They are: and in, The iterative estimate of the clutter covariance matrix in step 4 is The expression is: The test statistic t in step 5 iter , whose expression is: Where tr(·) represents the trace of the matrix; The detection threshold η in step 6 iter The method to determine is: or iter =t(n * ); in, M is the number of Monte Carlo simulations, ρ is the set false alarm probability value, For rounding operation, t(n * ) is a sequence Sort n from largest to smallest * maximum values, Z(k) is the kth experiment of the data to be detected containing only clutter components, where k = 1, 2, …M.

2. The method for adaptive detection of radar extended targets in a non-uniform environment according to claim 1, characterized in that: In step 7, the determination of whether the target exists is performed according to the following results: if the test statistic t iter Greater than the detection threshold η iter , then the target is judged to exist, otherwise the target is judged not to exist.

3. A radar extended target adaptive detection system in a non-uniform environment, characterized in that: The method is implemented by a radar extended target adaptive detection method in a non-uniform environment according to any one of claims 1 to 2. include: Parameter estimation algorithm module: used to calculate the signal coordinate matrix and the iterative estimation value of the power mismatch between the training sample and the data to be detected by using the joint probability density function of the data to be detected and the training sample; Covariance matrix construction module: used to construct the clutter covariance matrix using the data to be detected, the training samples, the signal coordinate matrix and the power mismatch between the training samples and the data to be detected; Test statistic calculation module: used to construct test statistics using the data to be detected, signal subspace and clutter covariance matrix; Detection threshold determination module: used to determine the detection threshold according to the false alarm probability; Target judgment module: used to compare the test statistic and the detection threshold and judge whether the target exists. If the test statistic is greater than the detection threshold, the target is judged to exist, otherwise the target is judged not to exist.

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