Radar extended target detection method and device under composite Gaussian clutter

Abstract

The invention relates to the technical field of target detection, and provides a radar extended target detection method and device under composite Gaussian clutter. The method comprises the following steps: constructing a training sample matrix, a signal matrix, a to-be-detected data matrix and a clutter parameter vector; estimating target characteristic parameters according to the training sample matrix, the signal matrix, the to-be-detected data matrix and the clutter parameter vector; constructing test statistics according to the training sample matrix, the signal matrix and the estimated value of the target characteristic parameter; and determining a detection threshold according to the test statistic, and comparing the test statistic with the detection threshold to obtain a target state. According to the method, the problem that the real-time processing requirement of the radar cannot be met because the detection threshold is recalculated according to the clutter amplitude every time by a traditional detector is solved.

Description

Technical Field

[0001] This invention relates to the field of target detection technology, and in particular to a radar extended target detection method and apparatus under composite Gaussian clutter. Background Technology

[0002] In radar observation, targets with large size or complex structures are called extended targets. Their size is relatively large compared to the radar wavelength, or they possess complex shapes and scattering characteristics. Such targets exhibit large scattering cross-sections or complex scattering features in radar images or echoes. During radar signal processing, the statistical characteristics of observed clutter are generally considered to conform to a Gaussian distribution, allowing for target detection of extended targets based on the central limit theorem.

[0003] With the rapid development of modern radar technology towards high resolution and multimodal cooperative detection, the range resolution and spatial resolution of radar systems have achieved orders-of-magnitude improvements. When the radar operates at high resolution, the environment in which the observed clutter exists transforms into a complex Gaussian clutter environment, and the number of scatterers within a single resolution cell may drop to single digits. This renders the traditional Gaussian clutter assumption based on the central limit theorem invalid, and the actual measured data exhibits obvious peaks, tails, and other non-Gaussian characteristics; especially in scenarios such as low-friction angle observation and sea surface monitoring, the distribution of clutter amplitude often displays more complex statistical characteristics.

[0004] In this scenario, existing technologies use traditional detectors to detect received signals, determining whether to classify the received signal as indicating the presence of a target based on a detection threshold. However, traditional detectors need to recalculate the detection threshold based on clutter amplitude each time they detect data, which cannot meet the real-time processing requirements of radar and has poor practicality.

[0005] Therefore, overcoming the shortcomings of the existing technology is an urgent problem to be solved in this technical field. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a radar extended target detection method and device under composite Gaussian clutter. The purpose is that the detector directly calculates the test statistic when processing the received signal, and the detection threshold can be determined by the test statistic. There is no need to recalculate the detection threshold according to the clutter amplitude each time. This solves the problem that the traditional detector cannot meet the real-time processing requirements of radar because it recalculates the detection threshold according to the clutter amplitude each time.

[0007] The present invention adopts the following technical solution: In a first aspect, the present invention provides a radar extended target detection method under composite Gaussian clutter, comprising: Construct the training sample matrix, signal matrix, data matrix to be detected, and clutter parameter vector; The target feature parameters are estimated based on the training sample matrix, the signal matrix, the data matrix to be detected, and the clutter parameter vector. Based on the training sample matrix, the signal matrix, and the estimated values ​​of the target feature parameters, a test statistic is constructed. The detection threshold is determined based on the test statistic, and the test statistic is compared with the detection threshold to obtain the target state.

[0008] Furthermore, the target feature parameters include a signal coordinate matrix and a texture component reciprocal expectation vector; The step of estimating target feature parameters based on the training sample matrix, the signal matrix, the data matrix to be detected, and the clutter parameter vector includes: The covariance matrix is ​​estimated iteratively using the training sample matrix; Using the estimated value of the covariance matrix, the signal matrix, the data matrix to be detected, and the clutter parameter vector, the signal coordinate matrix and the reciprocal expectation vector of the texture components are estimated.

[0009] Furthermore, the expression for the iterative estimate of the covariance matrix is:

[0010] in, , , The number of system channels. Represents the trace of a matrix. Indicates the maximum number of iterations; Iteration The estimated value of the covariance matrix after the second iteration. , , for The middle matrix, This indicates the conjugate transpose. Indicates training samples, This represents a preset value indicating the number of training samples.

[0011] Further, constructing the test statistic based on the training sample matrix, the signal matrix, and the estimated values ​​of the target feature parameters includes: A test statistic is constructed using the training sample matrix, the signal matrix, the estimated value of the signal coordinate matrix, and the estimated value of the inverse expectation vector of the texture components.

[0012] Furthermore, the expression for the estimated value of the signal coordinate matrix is: ; in, Indicates the first The estimated value of the signal coordinate vector in the data to be detected. Represents the signal matrix, This indicates the conjugate transpose. , This represents the iterative estimate of the covariance matrix. Indicates the first One data point to be tested; The expression for the estimate of the reciprocal expectation vector of the texture component is: ; in, Indicates the first The estimated value of the inverse expectation vector of the texture components in each data to be detected; , indicating calculation Process parameters; , This represents the weighted generalized inverse Gaussian distribution. dimensional weight vector, , Let represent the right-scale parameter vector corresponding to the weighted generalized inverse Gaussian distribution. , Let represent the left-scale parameter vector corresponding to the weighted generalized inverse Gaussian distribution. , This represents the shape parameter vector corresponding to the weighted generalized inverse Gaussian distribution.

[0013] Furthermore, the expression for the test statistic is: ; in, Indicates the first The estimated value of the signal coordinate vector in the data to be detected. , Represents the signal matrix, This indicates the conjugate transpose. This represents the iterative estimate of the covariance matrix.

[0014] Further, the step of determining the detection threshold based on the test statistic and comparing the test statistic with the detection threshold to obtain the target state includes: The detection threshold is determined based on the test statistic and the preset false alarm probability. If the test statistic is greater than the detection threshold, the target state is that the target exists; otherwise, the target state is that the target does not exist.

[0015] Furthermore, the expression for the detection threshold is: ; in, , For the number of Monte Carlo simulations, To preset the false alarm probability, Indicates the rounding operation; For sequence Arrange from largest to smallest The maximum value, This indicates the conjugate transpose. , , This indicates the first data point of the test data containing only clutter components. The first experiment The estimated value of the signal coordinate matrix, This indicates the first data point of the test data containing only clutter components. The estimated value of the inverse expectation vector of the texture components in this experiment. This indicates the first data point of the test data containing only clutter components. The estimated value of the covariance matrix for this experiment.

[0016] Secondly, the present invention also provides a radar extended target detection device under composite Gaussian clutter, comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor for performing the radar extended target detection method under composite Gaussian clutter as described in the first aspect.

[0017] Thirdly, the present invention also provides a non-volatile computer storage medium storing computer-executable instructions, which are executed by one or more processors to perform the radar extended target detection method under composite Gaussian clutter as described in the first aspect.

[0018] Fourthly, a computer program product containing instructions is provided, which, when executed on a computer or processor, causes the computer or processor to perform the radar extended target detection method under composite Gaussian clutter as described in the first aspect.

[0019] Fifthly, the present invention also provides a radar extended target detection system under composite Gaussian clutter, including the radar extended target detection device under composite Gaussian clutter as described in the second aspect, and using the radar extended target detection method under composite Gaussian clutter as described in the first aspect to complete the interaction of the radar extended target detection device under composite Gaussian clutter as described in the second aspect.

[0020] Unlike existing technologies, the present invention has at least the following beneficial effects: After receiving a signal, this invention constructs a training sample matrix, a signal matrix, a data matrix to be detected, and a clutter parameter vector. Then, it constructs a test statistic by estimating the target feature parameters to determine the detection threshold. Since the test statistic can be directly obtained after the received signal is processed by the detector, there is no need to recalculate the detection threshold based on the clutter amplitude each time, as is done with traditional detectors. The detector efficiently extracts the features of extended targets through parallel processing, and can achieve clutter suppression, signal accumulation, and target detection in an integrated manner, meeting the real-time processing requirements of radar. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0022] Figure 1 This is a schematic flowchart of a radar extended target detection method under composite Gaussian clutter provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the principle of a radar extended target detection method under composite Gaussian clutter provided in an embodiment of the present invention; Figure 3 This is a flowchart illustrating step 10 provided in an embodiment of the present invention; Figure 4 This is a flowchart illustrating step 40 provided in an embodiment of the present invention; Figure 5 This is a comparison chart of the detection probability of the method of this embodiment of the invention and existing methods under different signal-to-noise ratios, provided by an embodiment of the invention. Figure 6 This is a structural framework diagram of a radar extended target detection system under composite Gaussian clutter provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the architecture of a radar extended target detection device under composite Gaussian clutter provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] Unless the context otherwise requires, throughout the specification and claims, the term "comprising" is interpreted as openly inclusive, meaning "including, but not limited to." In the description of the specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this disclosure. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics mentioned may be included in any suitable manner in any one or more embodiments or examples; that is, although they may be incorporated into embodiments or examples using the above terms for reasons such as order and position, it does not limit them to be incorporated in combination by a single embodiment or example.

[0025] In the description of this invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this disclosure.

[0026] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more. Furthermore, for example, the description may use the prefix "A" or "B" to describe the same type of nouns as two independent entities. In this case, the corresponding features defined with "A" and "B" are used only to distinguish between similar entities and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features.

[0027] In describing some embodiments, the terms "coupled," "coupled," and "connected," and their derivative expressions, may be used. For example, the term "connected" may be used in describing some embodiments to indicate that two or more components have direct physical or electrical contact with each other. Similarly, the term "coupled" may be used in describing some embodiments to indicate that two or more components have direct physical or electrical contact. However, the terms "connected" or "coupled" may also refer to two or more components that do not have direct contact with each other but still cooperate or interact with each other, such as "optical coupling," "wireless connection," etc. The embodiments disclosed herein are not necessarily limited to the scope of this invention.

[0028] In the description of this invention, the expression “A and / or B” (where A and B are used to formally represent specific features) will be used. The corresponding expression includes the following three combinations: only A, only B, and a combination of A and B.

[0029] As used in this invention, “about,” “approximately,” or “approximately” includes the stated value and the average value within an acceptable range of deviation from a particular value, wherein the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0030] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0031] Example 1: To solve the above problems, such as Figure 1 As shown, this embodiment of the invention provides a radar extended target detection method under composite Gaussian clutter, including: Step 10: Construct the training sample matrix, signal matrix, data matrix to be detected, and clutter parameter vector.

[0032] The radar extended target detection method under composite Gaussian clutter according to an embodiment of the present invention is described below based on specific examples: use 3D matrix Represents the data matrix to be detected. The number of system channels. To expand the number of distance cells occupied by the target (i.e., the target expansion dimension).

[0033] This invention uses hypothesis testing as a statistical method to determine whether the data to be detected contains the target signal. In hypothesis testing... Below, data to be detected Contains only clutter components In the hypothesis testing framework of radar signal processing, "the data to be detected contains only clutter components" means that, under the no-target assumption, the received signal data does not contain target echo signals, but only background clutter; this clutter may originate from various non-target signal sources such as ground clutter, sea clutter, and rain clutter. In this case, the null hypothesis... This indicates that the data to be detected contains only clutter, while the alternative hypothesis... This indicates that the data contains both clutter and target signals. By performing statistical tests on the data, it can be determined whether to accept the null hypothesis (i.e., determine that no target exists) or reject the null hypothesis (i.e., determine that a target exists).

[0034] Assuming clutter components Each column is independent and identically distributed, let the i-th column be denoted as . Listed as , It follows a pattern with a mean of zero and a covariance matrix of... The complex Gaussian distribution is denoted as . ,in, It is the first Data to be detected The clutter texture, corresponding to the probability density function is: . , representing the weighted generalized inverse Gaussian distribution. A weight vector of dimension; , represents the right-scale parameter vector corresponding to the weighted generalized inverse Gaussian distribution; , represents the left-scale parameter vector corresponding to the weighted generalized inverse Gaussian distribution; , represents the shape parameter vector corresponding to the weighted generalized inverse Gaussian distribution, and ,in Indicates the weighted number. This indicates transpose.

[0035] In hypothesis testing Down Includes clutter components and signal components , signal component Can be written as .in, 3D matrix Represents the signal subspace. 3D matrix This represents the signal coordinate matrix. In a real-world environment, the clutter covariance matrix... It is usually unknown. For estimation Assuming there exists There are training samples, denoted as _n_. , . Contains only clutter components Therefore, the problem to be detected can be represented by the following binary hypothesis test:

[0036] in, , .

[0037] This invention constructs a signal matrix. (Dimension is) ), data matrix to be detected (Dimension is) ), training sample matrix (Dimension is) ), clutter parameter matrix (Dimension is) ).in, Indicates the first One data point to be detected. , Indicates the first One training sample data, .

[0038] Step 20: Estimate the target feature parameters based on the training sample matrix, the signal matrix, the data matrix to be detected, and the clutter parameter vector.

[0039] In step 20, the target feature parameters obtained are estimated values. This process will be explained below.

[0040] Step 30: Construct a test statistic based on the training sample matrix, the signal matrix, and the estimated values ​​of the target feature parameters.

[0041] The process of constructing the test statistic will be explained below.

[0042] Step 40: Determine the detection threshold based on the test statistic, and compare the test statistic with the detection threshold to obtain the target state.

[0043] After receiving a signal, this invention constructs a training sample matrix, a signal matrix, a data matrix to be detected, and a clutter parameter vector. Then, it constructs a test statistic by estimating the target feature parameters to determine the detection threshold. Since the test statistic can be directly obtained after the received signal is processed by the detector, there is no need to recalculate the detection threshold based on the clutter amplitude each time, as is done with traditional detectors. The detector efficiently extracts the features of extended targets through parallel processing, and can achieve clutter suppression, signal accumulation, and target detection in an integrated manner, meeting the real-time processing requirements of radar.

[0044] like Figure 2The diagram shown is a schematic representation of the radar extended target detection method under composite Gaussian clutter according to an embodiment of the present invention. The following is a detailed description of the radar extended target detection method under composite Gaussian clutter according to an embodiment of the present invention: The composite Gaussian model is a statistical modeling framework for describing non-Gaussian clutter. This model decomposes clutter into a product of texture and speckle components. The choice of texture component distribution directly affects model accuracy. In traditional research, models such as the K-distribution, inverse Gamma distribution, and generalized inverse Gaussian distribution are widely used for texture component modeling; however, these models still have limitations in describing complex clutter environments. This invention's embodiment fits the clutter distribution based on a weighted generalized inverse Gaussian distribution model, effectively improving the accuracy of clutter characteristic descriptions in complex environments.

[0045] In one embodiment, the target feature parameters include a signal coordinate matrix and a texture component reciprocal expectation vector; such as Figure 3 As shown, step 20 includes: Step 201: Iteratively estimate the covariance matrix using the training sample matrix.

[0046] Wherein, the covariance matrix is ​​expressed as Its iterative estimate is expressed as All dimensions are superscript Indicates an estimate.

[0047] In one embodiment, the expression for the iterative estimate of the covariance matrix is:

[0048] in, , , Number of system channels Represents the trace of a matrix. Indicates the maximum number of iterations; Iteration The estimated value of the covariance matrix after the second iteration. , , for The initial values ​​of the intermediate matrix and the covariance matrix are: , This indicates the conjugate transpose. Indicates training samples, This represents a preset value indicating the number of training samples.

[0049] Step 202: Using the estimated value of the covariance matrix, the signal matrix, the data matrix to be detected, and the clutter parameter vector, estimate the signal coordinate matrix and the reciprocal expectation vector of the texture components.

[0050] The signal coordinate vector is represented as The corresponding estimated value is expressed as The reciprocal expectation vector of the texture components is represented as: The corresponding estimated value is expressed as ,in, Indicates the first The estimated value of the signal coordinate vector in the data to be detected. Indicates the first The estimated value of the reciprocal expectation of the texture components in the data to be detected. .

[0051] In one embodiment, the expression for the estimated value of the signal coordinate matrix is: ; in, Indicates the first The estimated value of the signal coordinate vector in the data to be detected. Represents the signal matrix, This indicates the conjugate transpose. , This represents the iterative estimate of the covariance matrix. Indicates the first One data point to be tested.

[0052] In one embodiment, the expression for the estimate of the inverse expectation vector of the texture components is: ; in, Indicates the first The estimated value of the inverse expectation vector of the texture components in each data to be detected; , indicating calculation Process parameters; , This represents the weighted generalized inverse Gaussian distribution. dimensional weight vector, , Let represent the right-scale parameter vector corresponding to the weighted generalized inverse Gaussian distribution. , Let represent the left-scale parameter vector corresponding to the weighted generalized inverse Gaussian distribution. , This represents the shape parameter vector corresponding to the weighted generalized inverse Gaussian distribution. Indicates the order is The second type of modified Bessel function is expressed as: .

[0053] The embodiments of the present invention achieve maximum a posteriori estimation of the inverse expectation of clutter texture components through clutter parameter vectors.

[0054] Based on this, step 30 includes: A test statistic is constructed using the training sample matrix, the signal matrix, the estimated value of the signal coordinate matrix, and the estimated value of the inverse expectation vector of the texture components.

[0055] In one embodiment, the expression for the test statistic is: ; in, Indicates the first The estimated value of the signal coordinate vector in the data to be detected. , Represents the signal matrix, This indicates the conjugate transpose. This represents the iterative estimate of the covariance matrix.

[0056] After determining the test statistic, such as Figure 4 As shown, step 40 includes: Step 401: Determine the detection threshold according to the test statistic and the preset false alarm probability.

[0057] The preset false alarm probability, or the preset value of the false alarm probability, is selected by those skilled in the art based on the specific application scenario. The false alarm probability refers to the probability that a radar system mistakenly identifies a target as present when there is no target. The higher the detection threshold, the lower the false alarm probability. The method of this embodiment can achieve constant false alarm rate (CFAR) detection.

[0058] In one embodiment, the expression for the detection threshold is: ; in, , For the number of Monte Carlo simulations, To preset the false alarm probability, Indicates the rounding operation; For sequence Arrange from largest to smallest The maximum value, This indicates the conjugate transpose. , , This indicates the first data to be detected that contains only clutter components. The first experiment The estimated value of the signal coordinate matrix, This indicates the first data to be detected that contains only clutter components. The estimated value of the inverse expectation vector of the texture components in this experiment. This indicates the first data point of the test data containing only clutter components. The estimated value of the covariance matrix for this experiment.

[0059] Step 402: If the test statistic is greater than the detection threshold, the target state is that the target exists; otherwise, the target state is that the target does not exist.

[0060] Finally, compare the test statistic with the detection threshold. If the test statistic is greater than the detection threshold, the target is determined to exist; otherwise, the target is determined to not exist.

[0061] The effects of this invention will be further illustrated below with simulation experiments: like Figure 5 The diagram shows a comparison of the detection probabilities of the method of this invention and existing methods under different signal-to-noise ratios. To simplify calculations, let the weighting factor... Number of radar system channels Target expanded dimension Dimension of signal subspace Number of training samples The number of iterations for estimation is 4, and the signal matrix is... With signal coordinate matrix Once randomly generated, the clutter parameters are set to remain unchanged. , , , .

[0062] Speckle components of clutter in simulation experiments , For a zero-mean complex Gaussian random process, the covariance matrix can be modeled as follows: , ,in .

[0063] False alarm probability The signal-to-noise ratio is defined as follows: ;in, .

[0064] To generate a specific signal-to-noise ratio, the corresponding target amplitude adopts a uniform energy distribution model, i.e. ,in This represents a random quantity that follows a standard complex normal distribution. The corresponding expression for the prior art method used in comparison is as follows: ; in, , .

[0065] from Figure 5 As can be seen, the method of this embodiment of the invention has a performance improvement of approximately 0.06 compared to the two-step generalized likelihood ratio test detector (GLRT) used in the prior art when the signal-to-noise ratio is 19dB. That is, the detection probability is increased by 0.06 under the premise that the signal-to-noise ratio remains unchanged.

[0066] like Figure 6 As shown, this embodiment of the invention also provides a radar extended target detection system under composite Gaussian clutter, used to implement the radar extended target detection method under composite Gaussian clutter of this embodiment of the invention. The radar extended target detection system under composite Gaussian clutter includes: Data construction module: Constructs the signal matrix, the data matrix to be detected, the training sample matrix, and the clutter parameter vector.

[0067] Covariance matrix estimation module: Used to iteratively estimate the covariance matrix using training samples.

[0068] The parameter estimation algorithm module is used to calculate the estimated values ​​of the signal coordinate matrix and the inverse expectation of the texture components using the data to be detected, the estimated value of the covariance matrix, the signal matrix, and the clutter parameter vector.

[0069] Test statistic calculation module: Used to construct test statistics using the estimated values ​​of the covariance matrix, signal matrix, coordinate matrix, and the inverse expectation of clutter texture components.

[0070] Detection threshold determination module: used to determine the detection threshold based on the false alarm probability.

[0071] The target decision module compares the test statistic and the detection threshold to determine whether the target exists. If the test statistic is greater than the detection threshold, the target is determined to exist; otherwise, the target is determined not to exist.

[0072] Example 2: like Figure 7 The diagram shown is a schematic representation of an extended target detection device for radar under composite Gaussian clutter according to an embodiment of the present invention. This extended target detection device for radar under composite Gaussian clutter includes one or more processors 21 and a memory 22. Figure 7 Take a processor 21 as an example.

[0073] Processor 21 and memory 22 can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0074] The memory 22, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs and non-volatile computer-executable programs, such as the radar extended target detection method under composite Gaussian clutter in this embodiment. The processor 21 executes the radar extended target detection method under composite Gaussian clutter by running the non-volatile software programs and instructions stored in the memory 22.

[0075] Memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 22 may optionally include memory remotely located relative to processor 21, which can be connected to processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0076] The program instructions / modules are stored in the memory 22. When executed by one or more processors 21, they execute the radar extended target detection method under composite Gaussian clutter in the above embodiments, for example, executing each step of the radar extended target detection method under composite Gaussian clutter in the embodiments of the present invention described above.

[0077] This invention also provides a non-volatile computer storage medium storing computer-executable instructions that are executed by one or more processors, for example... Figure 7 A processor 21 can enable one or more of the processors to execute the radar extended target detection method under composite Gaussian clutter in the specific embodiments of the present invention, for example, to execute the various steps of the radar extended target detection method under composite Gaussian clutter in the embodiments of the present invention described above; it can also implement Figure 7 The various modules and units described above; or the radar extended target detection method under composite Gaussian clutter as described in the specific embodiments of the present invention, for example, executing the various steps of the radar extended target detection method under composite Gaussian clutter described in the embodiments of the present invention above; can also achieve Figure 7 The aforementioned modules and units.

[0078] It is worth noting that the information interaction and execution process between the modules and units in the above-mentioned device and system are based on the same concept as the processing method embodiment of the present invention. For details, please refer to the description in the method embodiment of the present invention, and will not be repeated here.

[0079] Those skilled in the art will understand that all or part of the steps in the various methods of the embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A radar extended target detection method under composite Gaussian clutter, characterized in that, include: Construct the training sample matrix, signal matrix, data matrix to be detected, and clutter parameter vector; The target feature parameters are estimated based on the training sample matrix, the signal matrix, the data matrix to be detected, and the clutter parameter vector. Based on the training sample matrix, the signal matrix, and the estimated values ​​of the target feature parameters, a test statistic is constructed. The detection threshold is determined based on the test statistic, and the test statistic is compared with the detection threshold to obtain the target state.

2. The radar extended target detection method under composite Gaussian clutter according to claim 1, characterized in that, The target feature parameters include a signal coordinate matrix and a texture component reciprocal expectation vector; The step of estimating target feature parameters based on the training sample matrix, the signal matrix, the data matrix to be detected, and the clutter parameter vector includes: The covariance matrix is ​​estimated iteratively using the training sample matrix; Using the estimated value of the covariance matrix, the signal matrix, the data matrix to be detected, and the clutter parameter vector, the signal coordinate matrix and the reciprocal expectation vector of the texture components are estimated.

3. The radar extended target detection method under composite Gaussian clutter according to claim 2, characterized in that, The expression for the iterative estimate of the covariance matrix is: in, , , Number of system channels Represents the trace of a matrix. Indicates the maximum number of iterations; Iteration The estimated value of the covariance matrix after the second iteration. , , for The intermediate matrix, This indicates the conjugate transpose. Indicates training samples, This represents a preset value indicating the number of training samples.

4. The radar extended target detection method under composite Gaussian clutter according to claim 2, characterized in that, The step of constructing the test statistic based on the training sample matrix, the signal matrix, and the estimated values ​​of the target feature parameters includes: A test statistic is constructed using the training sample matrix, the signal matrix, the estimated value of the signal coordinate matrix, and the estimated value of the inverse expectation vector of the texture components.

5. The radar extended target detection method under composite Gaussian clutter according to claim 4, characterized in that, The expression for the estimated value of the signal coordinate matrix is: ； in, Indicates the first The estimated value of the signal coordinate vector in the data to be detected. Represents the signal matrix, This indicates the conjugate transpose. , This represents the iterative estimate of the covariance matrix. Indicates the first One data point to be tested; The expression for the estimate of the reciprocal expectation vector of the texture component is: ； in, Indicates the first The estimated value of the inverse expectation vector of the texture components in each data to be detected; , indicating calculation Process parameters; , This represents the weighted generalized inverse Gaussian distribution. dimensional weight vector, , Let represent the right-scale parameter vector corresponding to the weighted generalized inverse Gaussian distribution. , Let represent the left-scale parameter vector corresponding to the weighted generalized inverse Gaussian distribution. , This represents the shape parameter vector corresponding to the weighted generalized inverse Gaussian distribution.

6. The radar extended target detection method under composite Gaussian clutter according to claim 4, characterized in that, The expression for the test statistic is: ； in, Indicates the first The estimated value of the signal coordinate vector in the data to be detected. , Represents the signal matrix, This indicates the conjugate transpose. This represents the iterative estimate of the covariance matrix.

7. The radar extended target detection method under composite Gaussian clutter according to claim 1, characterized in that, The step of determining the detection threshold based on the test statistic and comparing the test statistic with the detection threshold to obtain the target state includes: The detection threshold is determined based on the test statistic and the preset false alarm probability. If the test statistic is greater than the detection threshold, the target state is that the target exists; otherwise, the target state is that the target does not exist.

8. The radar extended target detection method under composite Gaussian clutter according to claim 7, characterized in that, The expression for the detection threshold is: ； in, , For the number of Monte Carlo simulations, To preset the false alarm probability, Indicates the rounding operation; For sequence Arrange from largest to smallest The maximum value, This indicates the conjugate transpose. , , This indicates the first data point of the test data containing only clutter components. The first experiment The estimated value of the signal coordinate matrix, This indicates the first data point of the test data containing only clutter components. The estimated value of the inverse expectation vector of the texture components in this experiment. This indicates the first data point of the test data containing only clutter components. The estimated value of the covariance matrix for this experiment.

9. A radar extended target detection device under composite Gaussian clutter, characterized in that, The radar extended target detection device under composite Gaussian clutter includes at least one processor and a memory, which are connected via a data bus. The memory stores instructions that can be executed by the at least one processor. After being executed by the processor, the instructions are used to implement the radar extended target detection method under composite Gaussian clutter as described in any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which are executed by one or more processors to perform the radar extended target detection method under composite Gaussian clutter as described in any one of claims 1-8.

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