Shipborne complex HRRP sparse estimation method and system matching K-distribution clutter characteristics

Abstract

The present invention discloses a method and system for sparse estimation of complex ship HRRP matching K - distribution clutter characteristics. The present invention uses the K - distribution to constrain the probability characteristics of sea clutter, uses a single - parameter random distribution to constrain the sparse characteristics of complex ship HRRP, and uses the A - D test to determine the sparse parameters of complex ship HRRP q , which solves the problem of clutter model mismatch when the existing sparse optimization methods are applied to the estimation of complex ship HRRP in the background of non - Gaussian sea clutter, and significantly reduces the performance loss of complex ship HRRP estimation caused by clutter model mismatch.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar signal processing, and particularly relates to a method and system for sparse estimation of complex HRRP of ships matching the characteristics of K-distributed clutter. Background Art

[0002] High Resolution Range Profile (HRRP) is obtained by summing all complex echo vectors of target scatterers within a single range cell, corresponding to the projection of the complex echo of the target scattering center on the radar line of sight. In the application of high-resolution sea radars, since complex HRRP contains rich information of the target, many target classification and recognition techniques have been developed based on complex HRRP.

[0003] For the complex HRRP of most ships, some strong scattering units mainly come from the masts, superstructures, cranes and weapons of the ships, and sparsity is also an inherent characteristic of the complex HRRP of ships. However, in the application of sea radars, due to the strong non-Gaussian characteristics of sea clutter, it poses a serious challenge to the estimation of the complex HRRP of ships. Therefore, the problem of estimating the complex HRRP of ships can be attributed to estimating the sparse complex HRRP of ships from the radar complex echoes containing non-Gaussian sea clutter.

[0004] So far, many sparse optimization methods for recovering target signals in noise have also been developed. The current methods take into account the existence of noise, but since most of them assume that the background noise is Gaussian noise, while in the sparse estimation scenario of high-resolution sea radars, sea clutter exhibits long-tailed non-Gaussian characteristics. Therefore, directly applying these sparse optimization methods to the estimation of complex HRRP of ships will cause serious performance loss. Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a method and system for sparse estimation of complex HRRP of ships matching the characteristics of K-distributed clutter.

[0006] In a first aspect, the present invention provides a method for sparse estimation of complex HRRP of ships matching the characteristics of K-distribution, including:

[0007] Obtain the original radar echo data;

[0008] Use the threshold detection method for the original radar echo data to obtain the range interval where the complex HRRP of the ship is located;

[0009] Represent the radar echo signal within the range interval as a vector matrix model;

[0010] Apply a sparse optimization method that matches the K - distribution characteristics to the vector matrix model of the radar echo signal to estimate the complex HRRP of the ship.

[0011] In a second aspect, the present invention provides a sparse estimation system for the complex HRRP of a ship that matches the K - distribution characteristics, including:

[0012] A data acquisition module for acquiring the original radar echo data;

[0013] A threshold detection module for performing threshold detection on the original radar echo data to determine the distance interval where the complex HRRP of the ship is located;

[0014] A model construction module for representing the radar echo signal within the distance interval as a vector matrix model;

[0015] An estimation module for applying a sparse optimization method that matches the K - distribution characteristics to estimate the complex HRRP of the ship.

[0016] In a third aspect, the present invention provides a computer - readable storage medium, on which program instructions are stored, and when the program instructions are executed, the above - mentioned method is implemented.

[0017] In a fourth aspect, the present invention provides a computer program product, including computer programs / instructions, and when the computer programs / instructions are executed by a processor, the above - mentioned method is implemented.

[0018] The beneficial effects of the present invention: The present invention uses the K - distribution to constrain the probability characteristics of sea clutter, uses a single - parameter random distribution to constrain the sparse characteristics of the complex HRRP of the ship, and uses the A - D test to determine the sparse parameter q of the complex HRRP of the ship, solving the problem of clutter model mismatch when the existing sparse optimization method is applied to the estimation of the complex HRRP of the ship in a non - Gaussian sea clutter background, and significantly reducing the performance loss of the complex HRRP estimation of the ship caused by clutter model mismatch. Description of the Drawings

[0019] Figure 1 is a schematic flowchart of a sparse estimation method for the complex HRRP of a ship that matches the K - distribution characteristics provided by an embodiment of the present invention;

[0020] Figure 2 is a framework diagram for estimating the complex HRRP of a ship by applying a sparse estimation method that matches the K - distribution characteristics to the vector matrix model of the radar echo signal provided by an embodiment of the present invention;

[0021] Figures 3(a) and 3(b) are curves showing the variation of the estimation error and variance of two methods with the signal - to - clutter ratio provided by an embodiment of the present invention;

[0022] Figures 3(c) and 3(d) are the curves of the estimation error and variance of the two methods provided by the embodiments of the present invention varying with the shape parameter;

[0023] Figures 3(e) and 3(f) are the curves of the estimation error and variance of the two methods provided by the embodiments of the present invention varying with the sparse parameter;

[0024] Figure 4 is the experimental diagram of the measured data provided by the embodiments of the present invention. Specific Embodiments

[0025] The following further describes the present invention in detail with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0026] Embodiment 1

[0027] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a shipborne complex HRRP sparse estimation method for matching the K-distribution characteristics provided by the embodiments of the present invention, and it includes:

[0028] Step 1: Obtain the original radar echo data.

[0029] In this embodiment, assume that the radar broadband pulse has a bandwidth of and a carrier frequency of . The down-converted signal of the received echo is . Sampling and at intervals of can generate discrete sequences and a reference signal , where is the width of the range cell, and is the speed of light. In order to reduce the range sidelobe, a window function is used in the frequency domain, and pulse compression is achieved in the frequency domain through the Discrete Fourier Transform (DFT);

[0030]

[0031] where represents the inverse , represents taking the conjugate complex number after performing DFT on the reference signal , represents taking the square of the modulus after performing DFT on the reference signal , is the window function, and is the length of. The point spread function is used to represent pulse compression.

[0032] When the high-resolution sea surveillance radar operates in the scanning mode, the radar echo of the ship is expressed as:

[0033]

[0034] Where is the complex HRRP of the ship along the range cell, is the time delay of the strong scatterer in the th range cell. For a high-range-resolution radar with a range cell of several meters, can be assumed to be an integer . , are respectively the starting position and the ending position of the range interval where the complex HRRP of the ship is located.

[0035] Step 2: Use the threshold detection method for the original ship radar echo data to obtain the range interval where the complex HRRP of the ship is located.

[0036] In the application of target size feature extraction based on HRRP, in order to adapt to the range resolution ability of the radar and the scale feature of the target, the window pane of the radar received echo is usually discretized in units of the radar range resolution cell. The length of the window pane of the intercepted radar received echo needs to be greater than the length of the target echo. Therefore, in the one-dimensional range image of the radar, in addition to the echo signal of the target, it also includes information such as noise and sea clutter. The threshold detection can be used to obtain the range interval where the complex HRRP of the ship is located, that is, if the radar echo power of a certain range cell is greater than a certain specific value, it can be considered that this range cell may be located in the range interval where the complex HRRP of the ship is located.

[0037] In some embodiments, the specific content of step 2 is:

[0038] Step 2-1: The threshold used is the average power of the high-resolution sea clutter, specifically:

[0039] Where, , are respectively the shape parameter and the scale parameter of the K-distribution fitting sea clutter determined by the moment estimation method.

[0040] Step 2-2: Denote as the radar echo power on the kth range cell, and record all continuous intervals that satisfy .

[0041] The range interval where the complex HRRP of the ship is located is expressed as:

[0042]

[0043] Among them, represents the length of the interval .

[0044] Step 3: Represent the radar echo data within the said distance interval in the form of a vector matrix.

[0045] Represent the radar echo data in the form of a vector matrix, that is, represent the radar echo data as in the form of a vector matrix, with the aim of representing the problem of estimating the complex HRRP of a ship from radar echoes as a sparse reconstruction problem that can be solved using sparse optimization methods, where is the observation vector, is the sensing matrix, is the sparse vector to be estimated, is the clutter vector.

[0046] In some embodiments, the specific content of Step 3 is:

[0047] Step 3-1: Represent the echo data of the radar within the said distance interval as:

[0048]

[0049]

[0050] Among them, represents the monopulse radar echo, which is the superposition of the ship echo and the high-resolution sea clutter ; , . is the high-resolution sea clutter, and it is modeled using the K distribution, that is:

[0051]

[0052] Among them, are respectively the shape parameter and the scale parameter of the K distribution, is the gamma function, represents the modified Bessel function of the second kind of order

[0053] Step 3-2: Convert the complex vector sequence into a one-dimensional complex-valued sequence:

[0054]

[0055] Among them, is the monopulse radar echo.

[0056] Step 3-3: Represent the one-dimensional complex-valued sequence in the form of a matrix vector:

[0057]

[0058] where, is the radar echo signal vector; represents the vector corresponding to the complex HRRP of the ship, represents the interference vector, whose probability density function is p(d(n)), and its nth value is ; is the extended Point Spread Function (PSF) vector, and its expression is:

[0059]

[0060] where, is a vector of all zeros with length ; is the length of;

[0061] is the range cell shift matrix, and its expression is:

[0062]

[0063] where; is the center of, and respectively represent a row vector of all zeros with length N-1 and a column vector of all zeros with length N-1; represents an identity matrix of size N-1 rows and N-1 columns; A is a sensing matrix of size , L is the length of the range interval where the complex HRRP of the ship is located ;

[0064] Step 4: Apply a sparse estimation method that matches the K-distribution characteristics to the vector matrix model of the radar echo signal to estimate the complex HRRP of the ship.

[0065] Please refer to Figure 2 , Figure 2 which is the framework diagram of the sparse estimation method that matches the K-distribution characteristics to the vector matrix model of the radar echo signal provided by the embodiment of the present invention to estimate the complex HRRP of the ship.

[0066] In the sparse estimation method that matches the K-distribution characteristics, since the sparse parameter q values corresponding to different ships are different, when performing sparse estimation on the vector matrix model, it is necessary to select an appropriate sparse parameter q value, and then use the sparse estimation result corresponding to this value as the ship complex HRRP estimation result. The value range of the sparse parameter q is [0.02 0.05:0.05:1].

[0067] In some embodiments, step 4 is specifically:

[0068] Step 4-1: Calculate the ship complex HRRP estimation results corresponding to each value within the q value range:

[0069] In this embodiment, the estimation method is as follows: The sparse optimization method that matches the K-distribution characteristics uses the Maximum a Posteriori (MAP) to estimate the ship complex HRRP, that is:

[0070]

[0071] where, is the ship complex HRRP obtained by using the maximum a posteriori probability estimation; is the ship complex HRRP vector The joint probability density function of, which uses a single-parameter random distribution, that is

[0072] , is the sparse parameter, is the l-th value of the ship complex HRRP vector , ; , N is the length of the radar echo signal vector , is The n-th value of, is the row vector corresponding to the n-th row of the sensing matrix A.

[0073] Substituting the above formula gives the following optimization problem:

[0074]

[0075] where, is the estimation result of the ship complex HRRP when the sparse parameter is q; is The l-th value of.

[0076] Use the method of gradient decomposition to solve the above optimization problem. Specifically, the steps include:

[0077] 4-1-1: For the ship complex HRRP Initialization:

[0078] In this embodiment, a matched filter is applied to initialize the algorithm, that is

[0079]

[0080] where is the l-th value of the initialization vector of the ship complex HRRP vector ; is the column vector corresponding to the l-th column of the sensing matrix

[0081] 4-1-2: Iteratively update the ship complex HRRP :

[0082] In this embodiment, the iterative formula of the ship complex HRRP is:

[0083]

[0084] where , are respectively the vectors of the (k + 1)-th and k-th iterations of the ship complex HRRP ;

[0085] , is the l-th value of

[0086] ;

[0087] .

[0088] 4-1-3: Judge the iteration termination condition:

[0089] In this embodiment, when the ship complex HRRP iterates 20 times or satisfies , stop the iteration, and select the at this time as the estimated value of the ship complex HRRP corresponding to the sparse parameter q , where is a very small positive number

[0090] Step 4-2: Calculate the statistic of the A-D test corresponding to each sparse parameter q. Specifically, the steps include:

[0091] Step 4-2-1: Determine the residual sequence of the ship complex HRRP estimation .

[0092] In this embodiment, let be the residual of the estimated complex HRRP of the ship when the sparse parameter takes the value of q, that is ; sort the absolute values of the numbers in the vector to obtain the sequence , is the n th value of this sequence.

[0093] Step 4-2-2: Calculate the statistic q of the A-D test corresponding to each sparse parameter .

[0094] In this embodiment, the statistic of the A-D test corresponding to the sparse parameter q is:

[0095]

[0096] where is the amplitude cumulative density function of the K distribution.

[0097] Step 4-3: The sparse parameter q of the complex HRRP of the ship is determined as:

[0098] .

[0099] Step 4-4: The corresponding to the sparse parameter is the final estimated vector of the complex HRRP of the ship.

[0100] Embodiment 2

[0101] Corresponding to the above-mentioned method for sparse estimation of the complex HRRP of a ship matching the K distribution characteristics, the embodiment of the present application also provides a system for sparse estimation of the complex HRRP of a ship matching the K distribution characteristics. The above-mentioned system for sparse estimation of the complex HRRP of a ship matching the K distribution characteristics includes:

[0102] A data acquisition module for acquiring original radar echo data;

[0103] A threshold detection module for performing threshold detection on the original radar echo data to determine the distance interval where the complex HRRP of the ship is located;

[0104] A model construction module for representing the radar echo signal in the distance interval as a vector matrix model;

[0105] An estimation module for applying a sparse optimization method matching the K distribution characteristics to implement the estimation of the complex HRRP of the ship.

[0106] Embodiment 3

[0107] Corresponding to the above-mentioned ship complex HRRP sparse estimation method that matches the K-distribution characteristics, an embodiment of the present application also provides a computer-readable storage medium, on which program instructions are stored, and when the program instructions are executed, a ship complex HRRP sparse estimation method that matches the K-distribution characteristics is implemented.

[0108] Embodiment 4

[0109] Corresponding to the above-mentioned ship complex HRRP sparse estimation method that matches the K-distribution characteristics, an embodiment of the present application also provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, a ship complex HRRP sparse estimation method that matches the K-distribution characteristics is implemented.

[0110] In addition, although the present application is described in the context of functional modules, it should be understood that unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It can also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. More precisely, considering the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Therefore, those skilled in the art can implement the present application as set forth in the claims without undue experimentation using ordinary skills. It can also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, and the scope of the present application is determined by the full scope of the appended claims and their equivalents.

[0111] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0112] More specific examples (nonexhaustive list) of computer-readable media include the following: electrical connections (electronic devices) having one or more wirings, portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0113] Verification example:

[0114] The beneficial effects of the present application are further illustrated below through simulation experiments.

[0115] 1. Simulation conditions:

[0116] The simulation experiment of this embodiment was carried out in an environment where the computer was configured with core i7 2.6GHZ, 8G of memory, and the WINDOWS 10 system, and the computer software was configured with Matlab R2022b.

[0117] 2. Simulation content and result analysis:

[0118] The simulation experiment of this embodiment respectively carried out ship complex HRRP estimation experiments using the sparse optimization method proposed by the present invention and the existing sparse optimization method in simulation data and measured data.

[0119] In this simulation experiment, it is assumed that the radar is an X-band high-resolution radar, which emits a linear frequency modulated (LFM) pulse with a pulse width of 43.2 microseconds and a bandwidth of 200 MHz. The sampling frequency of the baseband signal is 200 MHz, and the pulse repetition frequency (PRF) of the radar is 1800 Hz. The emitted LFM waveform is positive frequency modulation. When pulse compression is performed, a Hamming window is added in the time domain, and pulse compression is achieved in the frequency domain. The farther away from the main lobe position, the gradually decreasing distance side lobe level, and the first distance side lobe is about -31.27 dB. The scale parameter of the sea clutter is set to =5, and the sparse parameter q takes values in the range of [0.02 0.05:0.05:1]. 10,000 independent experiments are carried out in each specified parameter set of the average signal-to-clutter ratio (SCR), sea clutter shape parameter, and sparse parameter to calculate the average value and variance of the relative error of estimating 10,000 ship complex HRRPs for each method.

[0120] Figures 3(a) and 3(b) are respectively the shape parameters set to 10, the sparsity parameter q set to 0.2, and the average relative error and variance of the relative error of the ship complex HRPP estimation when the signal-to-clutter ratio takes values in the interval [10:2:50]. The abscissa is the change range of the signal-to-clutter ratio, with the unit of dB, and the ordinates are the average relative error and the variance of the relative error respectively. Figures 3(c) and 3(d) are respectively the average relative error and variance of the relative error of the ship complex HRPP estimation when the signal-to-clutter ratio is set to 20 dB, the sparsity parameter is set to 0.2, and the shape parameter takes values in the interval [2:2:10]. The abscissa is the change range of the shape parameter, and the ordinates are the average relative error and the variance of the relative error respectively. Figures 3(e) and 3(f) are respectively the signal-to-clutter ratio set to 20 dB, the shape parameter set to 10, and the average relative error and variance of the relative error of the ship complex HRPP estimation when the sparsity parameter q takes values in the interval [0.02 0.05:0.05:1]. The abscissa is the change range of the sparsity parameter, and the ordinates are the average relative error and the variance of the relative error respectively. The green straight lines in all the pictures in the figure are the change curves of the average relative error and variance of the relative error of the ship complex HRRP estimated by the sparse optimization method that matches the K-distribution characteristics in this application. The red triangles are the change curves of the average relative error and variance of the relative error of the ship complex HRRP estimated by the existing SLIM method.

[0121] It can be seen from Figures 3(a) to 3(e) that: First, in all cases, the sparse optimization method for ship complex HRRP estimation proposed in this application has better estimation performance than the SLIM method. In fact, since most current sparse optimization methods use the Gaussian distribution as the interference background, it will inevitably result in performance loss when applied to ship complex HRRP estimation. However, the proposed sparse optimization method can significantly reduce the influence brought by the non-Gaussian characteristics of sea clutter. Second, the estimation performance of the sparse optimization method for ship complex HRRP estimation proposed in this application will improve with the increase of the signal-to-clutter ratio. Third, the sparse optimization method for ship complex HRRP estimation proposed in this application is little affected by the non-Gaussian characteristics of sea clutter, and the estimation performance remains basically unchanged with the increase of the sea clutter shape parameter. Fourth, the estimation performance of the sparse optimization method for ship complex HRRP estimation proposed in this application continuously improves with the decrease of the sparsity parameter q, which indicates that the sparser the complex HRRP of the ship, the better the estimation performance of the sparse optimization method for ship complex HRRP estimation proposed in this application.

[0122] Furthermore, please refer to Figure 4 , Figure 4 which is the experimental graph of the measured data provided by the embodiment of the present invention. Among them, Figure 4In (a) is the amplitude diagram of radar clutter data (unit: dB). Figure 4 In (b) is the amplitude diagram of the estimated result of the complex HRRP of the ship using the sparse optimization method proposed in this application (unit: dB). Figure 4 In (c) is the amplitude diagram of the estimated result of the complex HRRP of the ship using the SLIM method (unit: dB).

[0123] From Figure 4 It can be seen that when the method is applied to measured data, the SLIM method almost completely fails and cannot estimate the complex HRRP of the ship. However, the sparse optimization method for estimating the complex HRRP of the ship proposed in this application can effectively estimate both the strong scattering points and weak scattering points of the ship.

[0124] In summary, compared with the SLIM method, this application can effectively estimate the complex HRRP of the ship from radar echoes, significantly reducing the influence of the non-Gaussian characteristics of sea clutter on the estimation of the complex HRRP of the ship. This result is of great significance for high-resolution sea surveillance radars.

[0125] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0126] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A sparse estimation method for the complex HRRP of ships matching the K-distribution characteristics, characterized in that Including: Obtain the original radar echo data; Use the threshold detection method for the original radar echo data to obtain the distance interval where the complex ship HRRP is located; Express the radar echo signals within the distance interval as a vector matrix model; Apply a sparse optimization method that matches the K-distribution characteristics to the vector matrix model of the radar echo signals to estimate the complex ship HRRP; The sparse optimization method that matches the K-distribution characteristics uses maximum a posteriori probability estimation to estimate the complex ship HRRP, and obtains the estimation result by maximizing the product of the conditional probability density function of the radar echo signals when the complex ship HRRP vector is known and the prior probability density function of the complex ship HRRP vector; The prior probability density function uses a single-parameter random distribution, which constrains the sparsity of the complex ship HRRP vector in the form of an exponential function, where the sparse parameter is used to control the degree of sparsity of the distribution; The determination method of the sparse parameter is: find the parameter value that minimizes the A-D test statistic within a preset value range, where the A-D test statistic is used to measure the matching degree between the residual of the estimation result and the K-distribution characteristics. Specifically: Calculate the A-D test statistic corresponding to each sparse parameter q, including: Determine the residual sequence of ship complex HRRP estimation Let d q be the residual of the estimated complex HRRP of the ship when the sparse parameter takes the value q; sort the absolute values of the numbers in the vector d q , and obtain the sequence which is the n-th value of this sequence; Calculate the A-D test statistic W corresponding to each sparse parameter q q 2 : where CDF is the amplitude cumulative density function of the K-distribution; The sparse parameter q of the complex ship HRRP is determined as: Sparse parameter corresponding to is the final shipborne complex HRRP estimation vector.

2. The sparse estimation method of the complex ship HRRP that matches the K-distribution characteristics according to claim 1, characterized in that: Using the threshold detection method for the original radar echo data includes: Set the threshold to the average power of the sea clutter; Compare the intervals in the radar echo data where the power is greater than the threshold; Determine the longest interval in the intervals as the distance interval where the complex ship HRRP is located.

3. The method for sparse estimation of complex HRRP of ships matching K-distribution characteristics according to claim 2, characterized in that: The vector matrix model of the radar echo signals is expressed as the radar echo signal vector being equal to the product of the sensing matrix and the complex ship HRRP vector plus the interference vector, where the statistical characteristics of the interference vector are modeled using the K-distribution.

4. The method for sparse estimation of complex HRRP of ships matching K distribution characteristics according to claim 1, characterized in that: The method further includes iteratively updating the estimation result until a preset termination condition is met.

5. Shipborne complex HRRP sparse estimation system matching K distribution characteristics, characterized in that Including: A data acquisition module for obtaining the original radar echo data; A threshold detection module for performing threshold detection on the original radar echo data to determine the distance interval where the complex ship HRRP is located; A model construction module for expressing the radar echo signals within the distance interval as a vector matrix model; An estimation module for applying a sparse optimization method that matches the K-distribution characteristics to estimate the complex ship HRRP; The sparse optimization method that matches the K-distribution characteristics uses maximum a posteriori probability estimation to estimate the complex ship HRRP, and obtains the estimation result by maximizing the product of the conditional probability density function of the radar echo signals when the complex ship HRRP vector is known and the prior probability density function of the complex ship HRRP vector; The prior probability density function uses a single-parameter random distribution, which constrains the sparsity of the complex ship HRRP vector in the form of an exponential function, where the sparse parameter is used to control the degree of sparsity of the distribution; The determination method of the sparse parameter is as follows: find the parameter value that minimizes the A-D test statistic within a preset value range, where the A-D test statistic is used to measure the matching degree between the residual of the estimation result and the K-distribution characteristics. Specifically: Calculate the A-D test statistic corresponding to each sparse parameter q, including: Determine the residual sequence of ship complex HRRP estimation Let d q be the residual of the estimated complex HRRP of the ship when the sparse parameter takes the value q; Sort the absolute values of the numbers in the vector d q to obtain a sequence which is the nth value of this sequence; Calculate the A-D test statistic W corresponding to each sparse parameter q q 2 : where CDF is the amplitude cumulative density function of the K-distribution; The sparse parameter q of the shipborne complex HRRP is determined as: Sparse parameter corresponding to is the final shipborne complex HRRP estimation vector.

6. A computer-readable storage medium, characterized in that, It stores program instructions, and when the program instructions are executed, the method described in any one of claims 1 to 4 is implemented.

7. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by a processor, the method described in any one of claims 1 to 4 is implemented.

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