Variable trailing Rician sea clutter amplitude distribution model and parameter estimation method thereof

Abstract

The invention discloses a variable trailing Rician sea clutter amplitude distribution model and a parameter estimation method thereof. Firstly, sea clutter data are obtained through a sea detection radar or a synthetic aperture radar, a bivariate truncated non-zero mean value stable distribution complex wave model is constructed, and a variable trailing Rice amplitude distribution model is deduced based on the model. The model comprises four key parameters which are used for accurately describing amplitude distribution characteristics of sea clutter signals. Thirdly, constructing a theoretical characteristic function and an empirical characteristic function of the variable trailing Rice amplitude distribution model, approaching the theoretical characteristic function through the empirical characteristic function, and estimating four uncritical parameters; and finally, calculating a probability density function of variable trailing Rice amplitude distribution by using the estimated parameters, and comparing the probability density function with an empirical histogram of actually measured data to evaluate a fitting effect. The method can effectively improve the precision of sea clutter amplitude modeling, and has wide practical application value, especially in the fields of ocean detection and radar signal processing.

Description

Technical Field

[0001] The present invention relates to the field of ocean detection technology, and in particular to a variable-tail Ricean sea clutter amplitude distribution model and a parameter estimation method thereof. Background Art

[0002] With the continuous development of ocean detection technology, the study of sea clutter signals has become one of the key issues in the application of sea detection radar and synthetic aperture radar (SAR). Sea clutter refers to the backscattered echo received from the sea surface when the radar electromagnetic wave irradiates the sea surface. Due to the influence of platform parameters, measurement conditions, marine environment and meteorological factors, the characteristics of sea clutter are usually complex and changeable. For ocean detection systems, accurate modeling of sea clutter amplitude distribution is the basis for improving radar performance, optimizing signal processing and enhancing target detection capabilities. Therefore, sea clutter amplitude modeling and parameter estimation are of great significance.

[0003] At present, sea clutter models can be mainly divided into three categories: empirical distribution model, compound Gaussian distribution model and mixed distribution model. The degree of tailing of empirical distribution model and compound Gaussian distribution model is relatively fixed. For example, Rayleigh distribution and Rice distribution have lighter tailing, lognormal distribution and heavy-tailed Rayleigh distribution have too heavy tailing, and K distribution has moderate tailing. This means that these models are only applicable to sea clutter data under specific conditions, lack of versatility, and easily lead to mismatch between measured data and models, thus affecting the performance of radar applications based on clutter amplitude distribution.

[0004] Mixed distribution models, such as KA distribution and KK distribution, introduce additional components to describe the "peak" phenomenon in sea clutter, so that they can fit the tail of the clutter distribution more flexibly in theory, which can effectively improve the versatility of modeling. However, KA distribution and KK distribution have many unknown parameters, and there is coupling between the parameters, which greatly increases the complexity of parameter estimation and is not conducive to practical applications.

[0005] Therefore, constructing a universal sea clutter amplitude distribution model that can be applied to different radar parameters and environmental conditions, and developing a simple and effective parameter estimation algorithm to better meet the needs of practical engineering applications are key technical issues that need to be urgently solved in this field. Summary of the invention

[0006] In view of the defects of the prior art, the present invention provides a variable-tail Ricean sea clutter amplitude distribution model and a parameter estimation method thereof.

[0007] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0008] A variable tail Ricean sea clutter amplitude distribution model and a parameter estimation method thereof, comprising the following steps:

[0009] S1. Obtain sea clutter data from sea detection radar or SAR.

[0010] S2. A two-variable truncated non-zero mean stable distribution complex wave model is constructed, and on this basis a variable tailing Rice amplitude distribution model is derived. The model includes four key parameters: characteristic index, scale parameter, location parameter and truncation depth parameter, which are used to accurately describe the amplitude distribution characteristics of sea clutter signals.

[0011] S3. Construct the calculation formula of the theoretical characteristic function and the empirical characteristic function of the variable tail Rice amplitude distribution model. Among them, the theoretical characteristic function is determined by the four key parameters of the model, and the empirical characteristic function is calculated based on the acquired sea clutter amplitude data.

[0012] S4. The empirical characteristic function is used to approximate the theoretical characteristic function to estimate the four key parameters of the variable tail Rice amplitude distribution model. Specifically, the location parameter is estimated based on the properties of the Bessel function; the characteristic index and the truncation depth parameter are estimated by constructing an optimization problem and solving it using the Gauss-Hermite quadrature method; the scale parameter is further estimated by using the estimated other parameters.

[0013] S5. Substitute the estimated model parameters into the amplitude model and calculate the probability density function of the variable tail Rice amplitude distribution. Evaluate the fitting effect by comparing with the empirical histogram of the measured data and output the model fitting results.

[0014] Furthermore, for the sea detection radar, in S1, the amplitude sequence corresponding to the fixed distance unit or the fixed pulse unit is analyzed, which is recorded as r = [r1, r2, ..., r L ], where L is the number of pulses in the sample sequence data;

[0015] For SAR sea clutter data, the two-dimensional image is analyzed and the two-dimensional data is rearranged into one-dimensional data, which is also recorded as r = [r1, r2, ..., r L ].

[0016] Furthermore, the characteristic function of the complex wave model in S2 is:

[0017]

[0018] Among them, ξ1, ξ2 represent the two components of the frequency vector, is the frequency vector amplitude, α∈(0,2] is the characteristic exponent, γ>0 is the scale parameter, δ1∈(-∞,+∞) and δ2∈(-∞,+∞) are the edge distribution position parameters of the real and imaginary parts, and η>0 is the truncation depth parameter; j(δ1ξ1+δ2ξ2) reflects the mean information of the complex wave signal in the time domain.

[0019] Furthermore, in S2, a two-dimensional Fourier transform is performed on the characteristic function to obtain a joint probability density function of the real part and the imaginary part of the clutter:

[0020]

[0021] in, represents double integration of two directions ξ1 and ξ2 in the frequency space;

[0022] x re , x im represents the real and imaginary parts of the clutter;

[0023] Furthermore, the corresponding variable tail amplitude distribution probability density function is derived based on the joint probability density function in S2:

[0024]

[0025] in, is the clutter amplitude, is the location parameter amplitude, J0(·) is the first kind of 0th order Bessel function, and s is a variable in frequency space.

[0026] Furthermore, according to the characteristic function of the complex wave model of S2 The frequency domain amplitude variables are derived in S3 Theoretical characteristic function of The calculation formula is

[0027]

[0028] Furthermore, according to the measured data of S1, r = [r1, r2, ..., r L ], and the empirical characteristic function is calculated in S3 Defined as:

[0029]

[0030] Among them, r i represents the i-th clutter amplitude in the measured data, and L is the number of data samples.

[0031] Furthermore, S4 uses And the property of J0(2.405)=0, the location parameter δ is estimated, and its estimated value is:

[0032]

[0033] Among them, s′ is the minimum s value obtained by numerical calculation, satisfying

[0034] Furthermore, in order to estimate the characteristic index α and the truncation depth parameter ηη in S4, firstly based on the theoretical characteristic function and the empirical characteristic function Construct the following formula:

[0035]

[0036]

[0037] where s ref =1 / C, is the amplitude signal power.

[0038] Furthermore, in S4, the characteristic index α and the truncation depth parameter ηη are estimated by solving the following optimization problem:

[0039]

[0040] Among them, {s k ; k = 1, 2, ..., K} is the positive zero of the 2Kth order Hermite polynomial, {w k ; k=1, 2, ..., K} are the corresponding weighted values.

[0041] Furthermore, the scale parameter γ is estimated in S4, and its estimated value is:

[0042]

[0043] in, represents the estimated value of the scale parameter; represents the estimated value of the characteristic index; Represents the estimated cutoff depth parameter of Power; is the characteristic index A factor related to ref Indicates the reference frequency; is the product of the cutoff depth and the reference frequency; represents the estimated value of the location parameter; ln(.) represents the natural logarithm.

[0044] The present invention also discloses a variable tail Rice Sea clutter amplitude distribution model and a parameter estimation system thereof, which can be used to implement the above-mentioned variable tail Rice Sea clutter amplitude distribution model and a parameter estimation method thereof, specifically including:

[0045] A sea clutter data acquisition module is used to acquire sea clutter data from a sea detection radar or a synthetic aperture radar SAR;

[0046] Sea clutter model building module, used to build a bivariate truncated non-zero mean stable distribution complex wave model and the corresponding variable tail Rice amplitude distribution model, and initialize the relevant model parameters, including characteristic index, scale parameter, location parameter and truncation depth parameter;

[0047] Amplitude model characteristic function construction module, used to construct the calculation formula of the theoretical characteristic function and empirical characteristic function of the variable tail Rice amplitude distribution model, where the theoretical characteristic function is determined by the four key parameters of the model, and the empirical characteristic function can be calculated based on the acquired sea clutter amplitude data;

[0048] A parameter estimation module is used to estimate the position parameter, characteristic index, truncation depth parameter and scale parameter in the amplitude model by using the empirical characteristic function of the amplitude model to approximate the theoretical characteristic function, wherein the position parameter is estimated by the properties of the Bessel function, the characteristic index and the truncation depth parameter are solved by constructing an optimization problem and using the Gauss-Hermite quadrature method, and the scale parameter is estimated by using other estimated parameters;

[0049] The fitting result evaluation module is used to substitute the estimated model parameters into the amplitude model, calculate the probability density function of the variable tail Rice amplitude distribution, and compare it with the empirical histogram of the measured data to evaluate the fitting effect of the model.

[0050] The display module is used to display the fitting results and evaluation effects to the user in the form of graphics, tables or other visual forms, so as to facilitate intuitive viewing of parameter estimation results and model fitting effects.

[0051] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the variable-tail Ricey Sea clutter amplitude distribution model construction and parameter estimation method are implemented.

[0052] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the variable-tail Ricey Sea clutter amplitude distribution model construction and parameter estimation method are implemented.

[0053] Compared with the prior art, the advantages of the present invention are:

[0054] Improve the accuracy of sea clutter amplitude distribution modeling: The variable tail Rice amplitude distribution model proposed in the present invention simultaneously considers the diversity of the measured clutter amplitude distribution tail and the non-zero mean of the clutter real and imaginary part signals, which can effectively improve the accuracy and versatility of amplitude modeling under different radar parameters and environmental conditions.

[0055] Optimizing parameter estimation process: The present invention constructs the theoretical characteristic function and empirical function of the variable tailing Rice amplitude distribution model, and uses the latter to approximate the former, effectively estimating various parameters in the sea clutter amplitude model. In particular, the characteristic index and truncation depth parameter are accurately estimated by the Gauss-Hermite quadrature method, thereby obtaining a more accurate estimation of the scale parameter, thereby avoiding the estimation error that may exist in the traditional method.

[0056] Applicable to various radar systems and sea clutter types: The present invention is applicable to sea clutter data acquired by different types of radar systems (such as sea detection radar, SAR, etc.), has strong versatility, and can adapt to the sea clutter characteristics under different sea conditions and different frequency bands.

[0057] Improve the accuracy and operability of sea clutter signal analysis: The variable tailing Rice amplitude distribution model and the characteristic function-based estimation method constructed by the present invention can not only accurately describe the statistical characteristics of sea clutter signals, but also provide reliable numerical support for subsequent radar data processing, image analysis and ocean detection, thereby improving the overall performance of ocean detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 The present invention provides a flow chart of a radar sea clutter amplitude distribution analysis method with a variable trailing Ricean distribution.

[0059] Figure 2 These are two 300*300 sea clutter images of COSMO-SkyMed SAR in a certain area according to an embodiment of the present invention; (a) is Example 1, and (b) is Example 2.

[0060] Figure 3 The variable tailing Rice distribution constructed by the present invention is Figure 2 The probability density function fitting results of the two measured sea clutter data images in the figure; (a) is example 1, and (b) is example 2. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0062] A method for estimating parameters of a sea clutter amplitude model comprises the following steps:

[0063] S1. Sea clutter data acquisition and processing

[0064] Sea clutter data can usually be obtained by sea detection radar and synthetic aperture radar (SAR). For sea detection radar, the amplitude sequence corresponding to a fixed distance unit or a fixed pulse unit is usually analyzed, denoted as r = [r1, r2, L, rL ], L is the number of pulses in the sample sequence data. For SAR sea clutter data, two-dimensional images are usually analyzed. In this case, the two-dimensional data can be rearranged into one-dimensional data, which is also recorded as r = [r1, r2, L, r L ].

[0065] Two 300*300 sea clutter images of COSMO-SkyMed SAR in a certain area are used as examples to verify the model and illustrate the parameter estimation method. COSMO-SkyMed SAR is carried by a satellite with an orbit altitude of 619.6km and a frequency of 9.65GHz (X-band). The two sea clutter images are selected from the Venezuela-Maracaibo file (row index 3049-3348, column index 201:500) and the Italy-DeltaPo file (row index 221-520, column index 1012:1311). Figure 2 Their magnitude grayscale graphs are plotted.

[0066] Rearrange the two images into a 1*90000 sequence r=[r1, r2, L, r L ], where L = 90000.

[0067] S2. Constructing a bivariate truncated non-zero mean stable distribution complex wave model and its variable tail amplitude model. Constructing a bivariate truncated non-zero mean stable distribution complex wave model, whose characteristic function is

[0068]

[0069] in is the frequency vector, is the frequency vector amplitude, α∈(0,2] is the characteristic exponent, γ>0 is the scale parameter, δ1∈(-∞,+∞) and δ2∈(-∞,+∞) are the position parameters corresponding to the two edge distributions of the real and imaginary parts respectively, and η>0 is the cutoff depth parameter.

[0070] Performing a two-dimensional Fourier transform on the characteristic function of the bivariate truncated non-zero mean stable distribution, we can obtain the joint probability density function of the real and imaginary parts.

[0071]

[0072] where x re and x im are the real and imaginary parts of the clutter respectively.

[0073] According to the joint probability density function of the real and imaginary parts, the corresponding variable tail amplitude distribution probability density function is derived as follows:

[0074]

[0075] in is the clutter amplitude, is the position parameter amplitude, and J0(·) is the first kind 0th order Bessel function.

[0076] The amplitude model constructed by the present invention is named as variable tail Ricean distribution to reflect its adjustable tail characteristics and the consideration of the non-zero mean of the real and imaginary parts of the clutter. When η→+∞, that is, there is no truncation, the constructed variable tail Ricean distribution degenerates into a heavy tail Ricean distribution. When η→+∞ and δ=0, the constructed variable tail Ricean distribution degenerates into a heavy tail Rayleigh distribution. When η→+∞, δ=0 and α=2, the constructed variable tail Ricean distribution degenerates into a Rayleigh distribution.

[0077] S3. Construct the theoretical characteristic function and empirical characteristic function calculation formula of variable tailed Rice distribution with respect to frequency vector amplitude variable, which are:

[0078]

[0079] in ξ1=s cos(θ ξ ), ξ2=s ​​sin(θ ξ ), R and Φ r are the random variables of clutter amplitude and phase, respectively, re =R cosΦ r and X im =R sinΦ r are the real and imaginary random variables of the noise, is an empirical approximation to the theoretical expectation.

[0080] S4. Construct a parameter estimation method for the variable tail amplitude model based on characteristic function to fit the measured data

[0081] Estimate the location parameter δ: Use And using the property of J0(2.405)=0, the estimated value of δ is Where s′ is defined by numerical calculation formula (5) The minimum s value.

[0082] Estimate the characteristic exponent α and the cutoff depth parameter η. Theoretical characteristic function based on variable tail Ricean distribution and the empirical characteristic function Construct the following formula

[0083]

[0084] where s ref =1 / C, is the amplitude signal power. Then the estimated values ​​of α and η can be obtained by solving the following Gauss-Hermitian quadrature optimization problem:

[0085]

[0086] where {s k ; k = 1, L, K} is the positive zero of the 2Kth order Hermite polynomial, {w k ; k = 1, L, K} are corresponding weighted values, and the present invention takes K = 10. The Gauss-Hermite quadrature optimization problem is solved using the classic simplex algorithm, and the initial point of the algorithm is set to [α0, η0] = [1, 1].

[0087] Estimate the scale parameter γ: Use approximate The estimated value of γ is

[0088]

[0089] Table 1 lists the variable tail Rice amplitude model Figure 2 Parameter estimation results of the two SAR images in .

[0090] S5. Output model fitting results

[0091] The estimated parameters Substituting into equation (3) to calculate the fitting result of the probability density function of the variable tailing Ricean amplitude distribution, the fitting performance can be evaluated by comparing it with the empirical histogram of the measured data.

[0092] The probability density function fitting result of the measured data in this embodiment is as follows Figure 3 shown.

[0093] It can be seen that the fitting performance of the present invention is significantly improved compared with the prior art (K distribution and heavy-tailed Ricean distribution). Specifically, compared with the K distribution, the overall fitting performance of the present invention is significantly improved; and compared with the heavy-tailed Ricean distribution, the fitting performance of the tail is significantly enhanced. This advantage is mainly due to the fact that the present invention takes into account the non-zero mean of the real and imaginary parts of the clutter and can adapt to clutter of various tailing degrees.

[0094] In order to highlight the advantages of the model constructed by the present invention, the following comparison between two existing technologies is carried out.

[0095] ①Comparison model 1: Typical K distribution Where K v-1 (·) represents the modified Bessel function of the second kind of order v-1, where v and λ are the shape parameter and scale parameter, respectively.

[0096] ② Comparative model 2: heavy-tailed Ricean distribution (a special case of the variable-tailed Ricean distribution constructed in the present invention when the cutoff depth parameter η→+∞).

[0097] The following table gives the parameter estimation results of different models.

[0098]

[0099] In another embodiment of the present invention, a variable tail Rice Sea clutter amplitude distribution model and a parameter estimation system thereof are provided. The system can be used to implement the above-mentioned variable tail Rice Sea clutter amplitude distribution model and a parameter estimation method thereof, specifically including:

[0100] A sea clutter data acquisition module is used to acquire sea clutter data from a sea detection radar or a synthetic aperture radar SAR;

[0101] Sea clutter model building module, used to build a bivariate truncated non-zero mean stable distribution complex wave model and the corresponding variable tail Rice amplitude distribution model, and initialize the relevant model parameters, including characteristic index, scale parameter, location parameter and truncation depth parameter;

[0102] Amplitude model characteristic function construction module, used to construct the calculation formula of the theoretical characteristic function and empirical characteristic function of the variable tail Rice amplitude distribution model, where the theoretical characteristic function is determined by the four key parameters of the model, and the empirical characteristic function can be calculated based on the acquired sea clutter amplitude data;

[0103] A parameter estimation module is used to estimate the position parameter, characteristic index, truncation depth parameter and scale parameter in the amplitude model by using the empirical characteristic function of the amplitude model to approximate the theoretical characteristic function, wherein the position parameter is estimated by the properties of the Bessel function, the characteristic index and the truncation depth parameter are solved by constructing an optimization problem and using the Gauss-Hermite quadrature method, and the scale parameter is estimated by using other estimated parameters;

[0104] The fitting result evaluation module is used to substitute the estimated model parameters into the amplitude model, calculate the probability density function of the variable tail Rice amplitude distribution, and compare it with the empirical histogram of the measured data to evaluate the fitting effect of the model.

[0105] The display module is used to display the fitting results and evaluation effects to the user in the form of graphics, tables or other visual forms, so as to facilitate intuitive viewing of parameter estimation results and model fitting effects.

[0106] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the variable tailing Rice Sea clutter amplitude distribution model and its parameter estimation method.

[0107] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0108] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the variable-tail Ricey Sea clutter amplitude distribution model and its parameter estimation method in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0109] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0113] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A variable tail Ricean sea clutter amplitude distribution model and its parameter estimation method, characterized in that: The following steps are involved: S1. Obtain sea clutter data from sea detection radar or synthetic aperture radar SAR. The measured data is recorded as r = [r1, r2, ..., r L ], where L is the number of pulses in the sample sequence data; S2. Construct a bivariate truncated non-zero mean stable distribution complex wave model, and derive a variable tail Rice amplitude distribution model based on it. The model includes four key parameters: characteristic index, scale parameter, location parameter and truncation depth parameter. The characteristic function of the complex wave model is: Among them, ξ1,ξ2 represent the two components of the frequency vector, is the frequency vector amplitude, α∈(0,2] is the characteristic index, γ>0 is the scale parameter, δ1∈(-∞,+∞) and δ2∈(-∞,+∞) are the edge distribution position parameters of the real and imaginary parts, and η>0 is the truncation depth parameter; j(δ1ξ1+δ2ξ2) reflects the mean information of the complex wave signal in the time domain; S3. Construct the calculation formula of the theoretical characteristic function and the empirical characteristic function of the variable tailing Rice amplitude distribution model; wherein the theoretical characteristic function is determined by the four key parameters of the model, and the empirical characteristic function is calculated based on the acquired sea clutter amplitude data, specifically: Characteristic functions of complex wave models according to S2 The frequency domain amplitude variables are derived in S3 Theoretical characteristic function of The calculation formula is According to the measured data of S1, r=[r1,r2,…,r L ], and the empirical characteristic function is calculated in S3 Defined as: Among them, r i represents the i-th clutter amplitude in the measured data, and L is the number of data samples; S4. The empirical characteristic function is used to approximate the theoretical characteristic function to estimate the four key parameters of the variable tail Rice amplitude distribution model. Specifically, the estimation of the position parameter is based on the properties of the Bessel function; the characteristic index and the truncation depth parameter are estimated by constructing an optimization problem and solving it using the Gauss-Hermite quadrature method; the scale parameter is further estimated by using the estimated other parameters; S5. Substitute the estimated model parameters into the amplitude model and calculate the probability density function of the variable tailing Ricean amplitude distribution; evaluate the fitting effect by comparing with the empirical histogram of the measured data, and output the fitting results of the model.

2. The method for estimating sea clutter amplitude model parameters according to claim 1, characterized in that: In S1, for the sea detection radar, the amplitude sequence corresponding to the fixed distance unit or fixed pulse unit is analyzed, which is recorded as r = [r1, r2, ..., r L ], where L is the number of pulses in the sample sequence data; For SAR sea clutter data, the two-dimensional image is analyzed and the two-dimensional data is rearranged into one-dimensional data, which is also recorded as r = [r1, r2, …, r L ].

3. The method for estimating sea clutter amplitude model parameters according to claim 2, characterized in that: In S2, the characteristic function is subjected to a two-dimensional Fourier transform to obtain the joint probability density function of the real part and the imaginary part of the clutter: in, represents double integration of two directions ξ1 and ξ2 in the frequency space; x re ,x im Represents the real and imaginary parts of the clutter.

4. The method for estimating sea clutter amplitude model parameters according to claim 3, characterized in that: In S2, the corresponding variable tail amplitude distribution probability density function is derived based on the joint probability density function: in, is the clutter amplitude, is the location parameter amplitude, J0(·) is the first kind of 0th order Bessel function, and s is a variable in frequency space.

5. The method for estimating sea clutter amplitude model parameters according to claim 4, characterized in that: Utilization in S4 And the property of J0(2.405)=0, the location parameter δ is estimated, and its estimated value is: Among them, s ′ is the minimum s value obtained by numerical calculation, satisfying 6. The method for estimating sea clutter amplitude model parameters according to claim 5, characterized in that: In order to estimate the characteristic index α and the truncation depth parameter η in S4, we first use the theoretical characteristic function and the empirical characteristic function Construct the following formula: where s ref =1 / C, is the amplitude signal power.

7. The method for estimating sea clutter amplitude model parameters according to claim 6, characterized in that: In S4, the characteristic index α and the cutoff depth parameter η are estimated by solving the following optimization problem: Among them, {s k ; k = 1, 2, ..., K} is the positive zero of the 2Kth order Hermite polynomial, {w k ; k=1,2,...,K} is the corresponding weighted value.

8. The method for estimating sea clutter amplitude model parameters according to claim 7, characterized in that: The estimated scale parameter γ in S4 is: in, represents the estimated value of the scale parameter; represents the estimated value of the characteristic index; Represents the estimated cutoff depth parameter of Power; is the characteristic index A factor related to ref Indicates the reference frequency; is the product of the cutoff depth and the reference frequency; represents the estimated value of the location parameter; ln(.) represents the natural logarithm.

9. A variable tail Ricey Sea clutter amplitude distribution model and parameter estimation system thereof, which can be used to implement the variable tail Ricey Sea clutter amplitude distribution model and parameter estimation method thereof according to any one of claims 1 to 8, specifically comprising: A sea clutter data acquisition module is used to acquire sea clutter data from a sea detection radar or a synthetic aperture radar SAR; Sea clutter model building module, used to build a bivariate truncated non-zero mean stable distribution complex wave model and the corresponding variable tail Rice amplitude distribution model, and initialize the relevant model parameters, including characteristic index, scale parameter, location parameter and truncation depth parameter; Amplitude model characteristic function construction module, used to construct the calculation formula of the theoretical characteristic function and empirical characteristic function of the variable tail Rice amplitude distribution model, where the theoretical characteristic function is determined by the four key parameters of the model, and the empirical characteristic function can be calculated based on the acquired sea clutter amplitude data; A parameter estimation module is used to estimate the position parameter, characteristic index, truncation depth parameter and scale parameter in the amplitude model by using the empirical characteristic function of the amplitude model to approximate the theoretical characteristic function, wherein the position parameter is estimated by the properties of the Bessel function, the characteristic index and the truncation depth parameter are solved by constructing an optimization problem and using the Gauss-Hermite quadrature method, and the scale parameter is estimated by using other estimated parameters; The fitting result evaluation module is used to substitute the estimated model parameters into the amplitude model, calculate the probability density function of the variable tail Rice amplitude distribution, and compare it with the empirical histogram of the measured data to evaluate the fitting effect of the model; The display module is used to display the fitting results and evaluation effects to the user in the form of graphics, tables or other visual forms, so as to facilitate intuitive viewing of parameter estimation results and model fitting effects.

10. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the variable tail Ricean Sea clutter amplitude distribution model construction and parameter estimation method as described in one of claims 1 to 8 are implemented.

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