Method and device for simulating amplitude data of sea clutters in positive Alpha stable distribution

Abstract

The invention provides a positive value Alpha stable distribution sea clutter amplitude data simulation method and device, and relates to the technical field of sea clutter data processing. The method comprises the following steps: determining a probability density function according to a characteristic function of positive value Alpha stable distribution, inverse Laplacian transformation, Mellin transformation properties and a G function; determining a cumulative distribution function according to the probability density function; establishing a corresponding relation table of a plurality of target characteristic index parameters, a plurality of target comprehensive scale parameters, a plurality of target sea clutter amplitude values and a value of a cumulative distribution function; and determining a target sea clutter amplitude data matrix according to a preset characteristic index parameter, a preset scale parameter, the random number matrix and the corresponding relation table. According to the characteristic function of the positive value Alpha stable distribution, the inverse Laplace transformation, the Mellin transformation property and the G function, closed representation of the probability density function of the positive value Alpha stable distribution is achieved, and the sea clutter amplitude of the positive value Alpha stable distribution can be directly generated in a simulation mode.

Description

Technical Field

[0001] The invention relates to the technical field of sea clutter data processing, and in particular to a method and a device for simulating positive alpha stable distribution sea clutter amplitude data. Background Art

[0002] Sea detection has always been one of the hot areas in the field of radar signal processing. Obtaining a variety of high-quality sea clutter data is the basis for the design of sea detection algorithms. The sea clutter data received by the radar is seriously affected by factors such as the radar working state, sea conditions, sea area, wind speed, wind direction, and wave height, resulting in variable statistical characteristics. A single statistical model cannot fully describe the characteristics of sea clutter, and the actual data acquisition process cannot obtain more comprehensive sea clutter data. Therefore, it is necessary to expand sea clutter data through sea clutter simulation. Among the Alpha stable distributions, the symmetric Alpha stable distribution is a model commonly used for sea clutter simulation. According to the sub-Gaussian theorem, the symmetric Alpha stable distribution can be expressed as the product of a complex Gaussian random process and a positive Alpha stable distribution random variable, which can more comprehensively describe the amplitude distribution characteristics and correlation of sea clutter, and can be regarded as a special form of the composite Gaussian distribution. However, since the probability density function of the Alpha stable distribution does not have a closed form expression for elementary functions, the simulation research on sea clutter data of this distribution family is very limited.

[0003] At present, the spherical invariant random process method can be used to simulate the symmetric Alpha stable distribution data. When the spherical invariant random process method is used to simulate the symmetric Alpha stable distribution data, its amplitude probability density function and autocorrelation function can be independently controlled. However, since the amplitude distribution of the symmetric Alpha stable distribution is still a special Alpha stable distribution, namely the positive Alpha stable distribution, the positive Alpha stable distribution does not have a closed form probability density function expression, so it is impossible to directly simulate and generate the positive Alpha stable distribution sea clutter amplitude data through the probability density function. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method and device for simulating positive Alpha stable distribution sea clutter amplitude data, so as to solve the problem that positive Alpha stable distribution sea clutter amplitude data cannot be directly simulated and generated by probability density function.

[0005] To solve the above technical problems, the embodiments of the present invention provide the following technical solutions:

[0006] A first aspect of the present invention provides a method for simulating positive alpha stable distribution sea clutter amplitude data, the method comprising:

[0007] Acquire simulation parameters of symmetrical Alpha stable distribution sea clutter data, the simulation parameters including the number of sea clutter range units, the number of pulses, a preset characteristic index parameter and a preset scale parameter;

[0008] Determine the characteristic function of the positive Alpha stable distribution according to the characteristic function of the preset Alpha stable distribution, the preset parameter range and the initial comprehensive scale parameter;

[0009] According to the characteristic function, inverse Laplace transform, Mellin transform property and G function of positive Alpha stable distribution, a probability density function is determined, and the probability density function is used to indicate the closed representation of positive Alpha stable distribution with respect to G function;

[0010] Determine the cumulative distribution function from the probability density function;

[0011] According to the cumulative distribution function and the preset interval, a corresponding relationship table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and corresponding cumulative distribution function values ​​is established;

[0012] Generate a random number matrix according to the number of sea clutter range units, the number of pulses and random distribution parameters;

[0013] According to the preset characteristic index parameters, the preset scale parameters, the random number matrix and the corresponding relationship table, the target sea clutter amplitude data matrix is ​​determined, and the target sea clutter amplitude data matrix is ​​used to simulate the sea clutter data.

[0014] A second aspect of the present application provides a positive Alpha stable distribution sea clutter amplitude data simulation device, the positive Alpha stable distribution sea clutter amplitude data simulation device comprising:

[0015] An acquisition module is used to acquire simulation parameters of symmetrical Alpha stable distribution sea clutter data, the simulation parameters including the number of sea clutter range units, the number of pulses, a preset characteristic index parameter and a preset scale parameter;

[0016] A first determination module is used to determine the characteristic function of the positive Alpha stable distribution according to the characteristic function of the preset Alpha stable distribution, the preset parameter range and the initial comprehensive scale parameter;

[0017] A second determination module is used to determine a probability density function according to a characteristic function of a positive Alpha stable distribution, an inverse Laplace transform, a Mellin transform property and a G function, wherein the probability density function is used to indicate a closed representation of a positive Alpha stable distribution with respect to a G function;

[0018] A third determination module is used to determine a cumulative distribution function according to a probability density function;

[0019] An establishment module is used to establish a correspondence table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and corresponding cumulative distribution function values ​​according to the cumulative distribution function and the preset interval;

[0020] A generation module, used for generating a random number matrix according to the number of sea clutter range units, the number of pulses and random distribution parameters;

[0021] The fourth determination module is used to determine the target sea clutter amplitude data matrix according to the preset characteristic index parameter, the preset scale parameter, the random number matrix and the corresponding relationship table, and the target sea clutter amplitude data matrix is ​​used to simulate the sea clutter data.

[0022] Compared with the prior art, the method and device for simulating sea clutter amplitude data of positive Alpha stable distribution provided by the present invention obtain simulation parameters of symmetric Alpha stable distribution sea clutter data; determine the characteristic function of the positive Alpha stable distribution according to the characteristic function, preset parameter range and initial comprehensive scale parameter of the preset Alpha stable distribution; determine the probability density function according to the characteristic function, Laplace inverse transform, Merlin transform property and G function of the positive Alpha stable distribution, and the probability density function is used to indicate the closed representation of the positive Alpha stable distribution with respect to the G function; determine the cumulative distribution function according to the probability density function; establish a corresponding relationship table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and corresponding cumulative distribution function values ​​according to the cumulative distribution function and the preset interval; generate a random number matrix according to the number of sea clutter distance units, the number of pulses and the random distribution parameter; determine the target sea clutter amplitude data matrix according to the preset characteristic index parameter, the preset scale parameter, the random number matrix and the corresponding relationship table. In this way, according to the characteristic function, inverse Laplace transform, Mellin transform properties and G function of the positive Alpha stable distribution, the probability density function of the positive Alpha stable distribution can be expressed as a closed expression form about the G function, and the closed representation of the probability density function of the positive Alpha stable distribution can be achieved, so that the target sea clutter amplitude data matrix, that is, the positive Alpha stable distribution sea clutter amplitude, can be directly simulated and generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0024] Figure 1 Schematic diagram showing the process of the simulation method for positive alpha stable distribution sea clutter amplitude data Figure 1 ;

[0025] Figure 2 Schematic diagram showing the process of the simulation method for positive alpha stable distribution sea clutter amplitude data Figure 2 ;

[0026] Figure 3 A probability density comparison diagram is schematically shown;

[0027] Figure 4 The schematic diagram of sea clutter amplitude data under simulation parameters is shown schematically;

[0028] Figure 5 A comparison diagram of probability density function curves is schematically shown;

[0029] Figure 6 The structure diagram of the positive alpha stable distribution sea clutter amplitude data simulation device is schematically shown. DETAILED DESCRIPTION

[0030] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0031] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present invention should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0032] The method in the embodiment of the present invention is described in detail below.

[0033] Figure 1 The flowchart of the method for simulating positive alpha stable distribution sea clutter amplitude data in the embodiment of the present invention is schematically shown. Figure 1 As shown, the method may include:

[0034] S101, obtaining simulation parameters of symmetrical Alpha stable distribution sea clutter data.

[0035] The simulation parameters include the number of sea clutter distance units, the number of pulses, a preset characteristic index parameter and a preset scale parameter.

[0036] In the embodiment of the present invention, it is necessary to obtain the number parameters of sea clutter amplitude data to be simulated according to the sea clutter data volume simulation requirements, including the number of sea clutter distance units M and the number of pulses N. It is necessary to obtain the simulation parameter values ​​of the Alpha stable distribution according to the sea clutter amplitude statistical characteristics requirements, including the preset characteristic index parameter α′, the preset scale parameter σ′, the preset skewness parameter β′ and the preset position parameter μ′.

[0037] S102, determining a characteristic function of a positive Alpha stable distribution according to a preset characteristic function of the Alpha stable distribution, a preset parameter range and an initial comprehensive scale parameter.

[0038] The characteristic function of the preset Alpha stable distribution is the general form of the Alpha stable distribution characteristic function The preset parameter range may include (0,1], 1, and 0.

[0039] S103. Determine a probability density function according to the characteristic function of the positive Alpha stable distribution, the inverse Laplace transform, the Mellin transform property and the G function.

[0040] Among them, the probability density function is used to indicate the closed representation of the positive Alpha stable distribution with respect to the G function.

[0041] The probability density function is the probability density function of the closed representation of the positive Alpha stable distribution with respect to the G function. According to the characteristic function, inverse Laplace transform, Merlin transform properties and G function of the positive Alpha stable distribution, the probability density function of the closed representation of the positive Alpha stable distribution with respect to the G function is determined.

[0042] S104. Determine a cumulative distribution function according to the probability density function.

[0043] The cumulative distribution function is the cumulative distribution function of the positive Alpha stable distribution with respect to the closed representation of the G function. According to the probability density function of the positive Alpha stable distribution with respect to the closed representation of the G function, the cumulative distribution function of the positive Alpha stable distribution with respect to the closed representation of the G function is determined.

[0044] S105. According to the cumulative distribution function and the preset interval, a correspondence table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and corresponding cumulative distribution function values ​​is established.

[0045] According to the cumulative distribution function and preset interval of the positive Alpha stable distribution about the closed representation of the G function, a corresponding relationship table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and the corresponding cumulative distribution function values ​​is established.

[0046] The preset interval includes a first preset interval, a second preset interval and a third preset interval. The first preset interval is an interval corresponding to an initial characteristic index parameter, the second preset interval is an interval corresponding to an initial comprehensive scale parameter, and the third preset interval is an interval corresponding to an initial sea clutter amplitude value.

[0047] S106. Generate a random number matrix according to the number of sea clutter range units, the number of pulses and random distribution parameters.

[0048] The random distribution parameters are (0,1).

[0049] S107, determining a target sea clutter amplitude data matrix according to a preset characteristic index parameter, a preset scale parameter, a random number matrix and a corresponding relationship table.

[0050] Among them, the target sea clutter amplitude data matrix is ​​used to simulate sea clutter data.

[0051] Based on the above Figure 1 It can be seen from the implementation method that the embodiment of the present invention obtains simulation parameters of symmetric Alpha stable distribution sea clutter data; determines the characteristic function of positive Alpha stable distribution according to the characteristic function of preset Alpha stable distribution, preset parameter range and initial comprehensive scale parameter; determines the probability density function according to the characteristic function of positive Alpha stable distribution, Laplace inverse transform, Merlin transform property and G function, and the probability density function is used to indicate the closed representation of positive Alpha stable distribution with respect to G function; determines the cumulative distribution function according to the probability density function; establishes a correspondence relationship table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and corresponding cumulative distribution function values ​​according to the cumulative distribution function and the preset interval; generates a random number matrix according to the number of sea clutter distance units, the number of pulses and the random distribution parameter; determines the target sea clutter amplitude data matrix according to the preset characteristic index parameter, the preset scale parameter, the random number matrix and the correspondence relationship table. In this way, according to the characteristic function, inverse Laplace transform, Mellin transform properties and G function of the positive Alpha stable distribution, the probability density function of the positive Alpha stable distribution can be expressed as a closed expression form about the G function, and the closed representation of the probability density function of the positive Alpha stable distribution can be achieved, so that the target sea clutter amplitude data matrix, that is, the positive Alpha stable distribution sea clutter amplitude, can be directly simulated and generated.

[0052] As a refinement and expansion of the above embodiment, Figure 2 The process of the positive alpha stable distribution sea clutter amplitude data simulation method in the embodiment of the present invention is as follows Figure 2 , see Figure 2 As shown, a method for simulating positive alpha stable distribution sea clutter amplitude data provided by an embodiment of the present invention may include:

[0053] S201, obtaining simulation parameters of symmetrical Alpha stable distribution sea clutter data.

[0054] The simulation parameters include the number of sea clutter distance units, the number of pulses, a preset characteristic index parameter and a preset scale parameter.

[0055] Step S201 is the same as step S101 and will not be described again here.

[0056] S202, determining a characteristic function of a positive Alpha stable distribution according to a preset characteristic function of the Alpha stable distribution, a preset parameter range and an initial comprehensive scale parameter.

[0057] Before determining the characteristic function of the positive Alpha stable distribution according to the characteristic function of the preset Alpha stable distribution, the preset parameter range and the initial comprehensive scale parameter, it is necessary to obtain the characteristic function of the preset Alpha stable distribution.

[0058] Specifically, the characteristic function of the preset Alpha stable distribution is the general form of the Alpha stable distribution characteristic function The expression is:

[0059]

[0060] in, is the characteristic function of the preset Alpha stable distribution, t is the time variable, i is the imaginary unit, α is the initial characteristic exponential parameter and α∈(0,2], σ is the initial scale parameter and σ>0, β is the initial skewness parameter and β∈[-1,1], μ is the initial position parameter and Represents the set of real numbers.

[0061] Specifically, according to the characteristic function of the preset Alpha stable distribution, the preset parameter range and the initial comprehensive scale parameter, the characteristic function of the positive Alpha stable distribution is determined, including:

[0062] Step A1: According to a preset characteristic function of the Alpha stable distribution and a preset parameter range, a characteristic function of a first positive Alpha stable distribution is obtained.

[0063] The characteristic function of the preset Alpha stable distribution is the general form of the Alpha stable distribution characteristic function The preset parameter range may include (0,1], 1, and 0.

[0064] According to the definition of positive Alpha stable distribution, the general form of Alpha stable distribution characteristic function The preset parameter range of the initial characteristic index parameter α in is set to α∈(0,1], the initial skewness parameter β is set to β=1, and the initial position parameter μ is set to μ=0, and these parameter values ​​are further substituted into the characteristic function of the above preset Alpha stable distribution In the formula, we get the characteristic function φ of the first positive Alpha stable distribution PαS 1(t):

[0065]

[0066] Among them, φ PαS 1(t) is the characteristic function of the first positive Alpha stable distribution, t is the time variable, i is the imaginary unit, σ is the initial scale parameter, and α is the initial characteristic exponential parameter.

[0067] Step A2: Determine the characteristic function of the positive alpha stable distribution according to the characteristic function of the first positive alpha stable distribution and the initial comprehensive scale parameter.

[0068] Specifically, according to the characteristic function of the first positive alpha stable distribution and the initial comprehensive scale parameter, determining the characteristic function of the positive alpha stable distribution includes:

[0069] The initial comprehensive scale parameter, i.e. the initial comprehensive scale parameter expression of the positive Alpha stable distribution, is γ = σ α / cos(πα / 2), and we can get σ by transposing the term α =γ*cos(πα / 2), and σ α =γ*cos(πα / 2)Substitute into the characteristic function φ of the first positive Alpha stable distribution obtained in step A1 PαS 1(t), we can get:

[0070]

[0071] Among them, φ PαS 2(t) is the characteristic function of the intermediate positive Alpha stable distribution, γ is the initial comprehensive scale parameter, α is the initial characteristic exponential parameter, σ is the initial scale parameter, t is the time variable, and i is the imaginary unit;

[0072] By using the relationship between Laplace transform and characteristic function definition, the formula of the characteristic function of the intermediate positive Alpha stable distribution can be simplified to obtain the simplified positive Alpha stable distribution characteristic function, that is, the characteristic function φ of the positive Alpha stable distribution P ' αS (t):

[0073] φ P ' αS(t) = exp(-γt α );

[0074] Among them, φ P ' αS (t) is the characteristic function of the positive Alpha stable distribution, γ is the initial comprehensive scale parameter, α is the initial characteristic exponential parameter, and t is the time variable.

[0075] The following steps S203 to S206 are specific operations for determining the probability density function based on the characteristic function of the positive Alpha stable distribution, the Laplace inverse transform, the Mellin transform properties and the G function.

[0076] S203: Convert the initial characteristic index parameter into an intermediate characteristic index parameter.

[0077] Among them, the intermediate characteristic exponent parameter is a fractional expression of the initial characteristic exponent parameter, and the characteristic function of the positive Alpha stable distribution includes the initial characteristic exponent parameter.

[0078] Converting the initial characteristic index parameter to an intermediate characteristic index parameter includes: Converting the initial characteristic index parameter to an intermediate characteristic index parameter in a fractional expression:

[0079] α=v / u,v <u;

[0080] Wherein, α is the initial characteristic index parameter, v is the first intermediate parameter, u is the second intermediate parameter, and v and u are positive integers.

[0081] S204, performing an inverse Laplace transform on the characteristic function of the positive Alpha stable distribution to obtain a first probability density function.

[0082] The first probability density function is an integral probability density function.

[0083] Perform an inverse Laplace transform on the characteristic function of the positive Alpha stable distribution to obtain the first probability density function in integral form:

[0084]

[0085] Among them, f1(z) is the first probability density function, is the inverse Laplace transform, φ P ' αS (t) is the characteristic function of the positive Alpha stable distribution, γ is the initial comprehensive scale parameter, α is the initial characteristic index parameter, z is the initial sea clutter amplitude value, t is the time variable, exp(-γt α ) is e's -γt α Power.

[0086] S205. Obtain a second probability density function according to the Mellin transformation property and the first probability density function.

[0087] Specifically, according to the properties of Mellin transformation, we can get: Substituting this formula into the formula of the first probability density function above, we can get:

[0088]

[0089] Among them, f2(z) is the second probability density function, z is the initial sea clutter amplitude value, γ is the initial comprehensive scale parameter, α is the initial characteristic index parameter, s is the seventh intermediate variable, i is the imaginary unit, and Γ(·) is the Gamma function.

[0090] S206. Determine a probability density function according to the intermediate characteristic index parameter, the second probability density function, the properties of the Gamma function and the G function.

[0091] The probability density function is used to indicate that the positive alpha stable distribution is closed with respect to the G function.

[0092] Specifically, according to the intermediate characteristic index parameter, the second probability density function, the Gamma function property and the G function, the probability density function is determined, including:

[0093] Step B1: Substitute the intermediate characteristic index parameter into the second probability density function and perform variable replacement to obtain a third probability density function.

[0094] Substituting the intermediate characteristic index parameter α=v / u into the formula of the second probability density function f2(z) and performing variable substitution, that is, s / u=t, the third probability density function can be obtained:

[0095]

[0096] Among them, f3(z) is the third probability density function, z is the initial sea clutter amplitude value, u is the second intermediate parameter, i is the imaginary unit, t is the time variable, v is the first intermediate parameter, L1 is the integration path, Γ(·) is the Gamma function, and γ is the initial comprehensive scale parameter.

[0097] Step B2: According to the properties of the Gamma function, the G function and the third probability density function, the probability density function is determined using the following first formula:

[0098]

[0099] Among them, a h =(u-h+1) / u,b j=(v-j+1) / v, h = 1, 2, ..., u, j = 1, 2, ..., v, f(z) is the probability density function, z is the initial sea clutter amplitude value, γ is the initial comprehensive scale parameter, v is the first intermediate parameter, u is the second intermediate parameter, a u is the third intermediate parameter, b v is the fourth intermediate parameter, is the Meijer G function with parameters (0, u, u, v), a h is the hth third intermediate parameter, b j is the jth fourth intermediate parameter.

[0100] Specifically, according to the properties of the Gamma function, As well as the G function, simplify the formula of the probability density function and obtain the probability density function, that is, the probability density function of the positive Alpha stable distribution with respect to the closed representation of the G function:

[0101]

[0102] S207. Determine a cumulative distribution function according to the probability density function.

[0103] Specifically, the cumulative distribution function is determined according to the probability density function, including:

[0104] Step C1: Determine a first cumulative distribution function according to a probability density function and an integral relationship between the probability density function and the cumulative distribution function.

[0105] The formula for the first cumulative distribution function is:

[0106]

[0107] Wherein, F1(z) is the first cumulative distribution function, f(x) is the probability density function, and in order to distinguish it from z in F1(z), f(z) in step B2 is replaced by f(x), x is the function independent variable, v is the first intermediate parameter, u is the second intermediate parameter, and a u is the third intermediate parameter, b v is the fourth intermediate parameter, is the Meijer G function with parameters (0, u, u, v), and γ is the initial comprehensive scale parameter.

[0108] Step C2: Based on the first cumulative distribution function, the following second formula is used to obtain the cumulative distribution function, that is, the cumulative distribution function of the positive Alpha stable distribution with respect to the closed characterization of the G function:

[0109]

[0110] Among them, a′ h=(u-h+1) / u,b j ′=(vj) / v,h=1,2,...,u,j=1,2,...,v,F(z) is the cumulative distribution function, z is the initial sea clutter amplitude value, γ is the initial comprehensive scale parameter, v is the first intermediate parameter, u is the second intermediate parameter, a u ' is the fifth intermediate parameter, b v ' is the sixth intermediate parameter, is the Meijer G function with parameters (0, u, u, v), a′ h is the hth fifth intermediate parameter, b j ′ is the jth sixth intermediate parameter.

[0111] S208. According to the cumulative distribution function and the preset interval, a correspondence table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and corresponding cumulative distribution function values ​​is established.

[0112] The preset intervals include a first preset interval, a second preset interval and a third preset interval, and the cumulative distribution function includes an initial characteristic index parameter, an initial sea clutter amplitude value and an initial comprehensive scale parameter.

[0113] Specifically, according to the cumulative distribution function and the preset interval, a corresponding relationship table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and corresponding cumulative distribution function values ​​is established, including:

[0114] Step D1: According to the initial characteristic index parameter and the first preset interval, a plurality of target characteristic index parameters are obtained at preset time intervals.

[0115] The first preset interval is [0,1].

[0116] Specifically, the value of the initial characteristic index parameter α is set in the first preset interval [0,1], and the value is taken every 0.01 from the preset time 0.01 to obtain multiple target characteristic index parameter values ​​α p That is, the initial characteristic index parameter α takes values ​​of 0.01, 0.02, 0.03, ... 0.99, 1, a total of 100 values, denoted as α p , where p = 1, 2, …, 100, and p is the number of initial characteristic exponential parameters α.

[0117] The preset time may also be 0.02, and there is no limitation on the preset time here.

[0118] Step D2: According to the initial comprehensive scale parameter and the second preset interval, a plurality of target comprehensive scale parameters are obtained at preset time intervals.

[0119] The second preset interval may be [0, 10] or [0, 20], and the second preset interval is not limited here.

[0120] Specifically, the value of the initial comprehensive scale parameter γ is set in the second preset interval [0,10], and the value is taken every 0.01 from the preset time 0.01 to obtain multiple target comprehensive scale parameter values ​​γ q That is, the initial comprehensive scale parameter γ takes values ​​of 0.01, 0.02, 0.03, ... 9.99, 10, a total of 1000 values, denoted as γ q , where q = 1, 2, ..., 1000, q is the number of initial comprehensive scale parameters γ.

[0121] Step D3: acquiring a plurality of target sea clutter amplitude values ​​at preset time intervals according to the initial sea clutter amplitude value and the third preset interval.

[0122] The third preset interval may be [0, 1000] or [0, 2000], and the third preset interval is not limited here.

[0123] Specifically, the initial simulation clutter amplitude value z is set in the interval [0,1000], and the value is taken every 0.01 from the preset time 0.01 to obtain multiple target clutter amplitude values ​​z r That is, the initial clutter amplitude value z takes the values ​​of 0.01, 0.02, 0.03...999.99, 1000, a total of 100,000 values, recorded as z r , where r = 1, 2, ... 100000, r is the number of initial simulated clutter amplitude values ​​z.

[0124] Step D4: Establish a three-dimensional parameter matrix based on multiple target characteristic index parameters, multiple target comprehensive scale parameters, and multiple target sea clutter amplitude values.

[0125] Among them, each element in the three-dimensional parameter matrix is ​​an element containing a target characteristic index parameter, a target comprehensive scale parameter, and a target sea clutter amplitude value.

[0126] Specifically, using different multiple target feature index parameter values ​​α p , different multi-target comprehensive scale parameters γ q , different multiple target sea clutter amplitude values ​​z r , establish a 100×1000×100000 three-dimensional parameter matrix, each element of which is (α p ,γ q ,z r ).

[0127] Step D5: Substitute each element in the three-dimensional parameter matrix into the cumulative distribution function to obtain the value of the cumulative distribution function corresponding to each element.

[0128] Specifically, traverse each element in the three-dimensional parameter matrix and convert each element (α p ,γ q ,z r ) is substituted into the formula of the cumulative distribution function to calculate the value of the cumulative distribution function corresponding to each element F(z r ; α=α p ,γ=γ q ), the values ​​of these cumulative distribution functions are different.

[0129] Step D6: Associating each element with the value of the corresponding cumulative distribution function to establish a corresponding relationship table.

[0130] Specifically, each element is associated with the value of the corresponding cumulative distribution function so that each element (α p ,γ q ,z r ) and the corresponding cumulative distribution function value F(z r ; α=α p ,γ=γ q ) is a one-to-one correspondence, thereby establishing a correspondence table between each element in the three-dimensional parameter matrix and the corresponding cumulative distribution function value.

[0131] S209, generating a random number matrix according to the number of sea clutter range units, the number of pulses and random distribution parameters.

[0132] Specifically, according to the number of sea clutter range units, the number of pulses and random distribution parameters, a random number matrix is ​​generated, including:

[0133] Step E1: Generate a zero matrix of a preset length according to the number of sea clutter range units, the number of pulses and the zero matrix.

[0134] The zero matrix of the preset length includes a plurality of zero elements. The preset length is M×N.

[0135] Specifically, according to the number M of sea clutter distance units, the number N of pulses and the zero matrix, a zero matrix D of a preset length M×N is generated:

[0136]

[0137] Wherein, D is a zero matrix of preset length, M is the number of sea clutter range units, and N is the number of pulses.

[0138] Step E2: Generate multiple random numbers according to the random distribution parameters.

[0139] The number of random numbers is the same as the number of zero elements. The random distribution parameter is (0,1), and the generated random numbers are uniformly distributed.

[0140] Generates M×N random numbers with uniform distribution parameters (0,1).

[0141] Step E3: Replace multiple zero elements row by row with multiple random numbers to obtain a random number matrix.

[0142] Multiple zero elements are replaced row by row with multiple random numbers to obtain a random number matrix. In other words, following the principle of from left to right and from top to bottom, multiple random numbers are used to replace the zero elements in the zero matrix of preset length in sequence to obtain a uniformly distributed random number matrix D with a random distribution parameter of (0,1). 1 :

[0143]

[0144] Among them, D 1 is the random number matrix, Rnd 1 is the first random number, M is the number of sea clutter range units, and N is the number of pulses.

[0145] The following steps S209 to S210 are specific operations for determining the target sea clutter amplitude data matrix according to the preset characteristic index parameter, the preset scale parameter, the random number matrix and the corresponding relationship table.

[0146] S210: Determine a first characteristic index parameter and a first comprehensive scale parameter according to a preset characteristic index parameter and a preset scale parameter.

[0147] Specifically, based on the preset characteristic index parameter α′, the corresponding first characteristic index parameter, i.e., the characteristic index parameter of the positive Alpha stable distribution, is determined

[0148]

[0149] in, is the first characteristic index parameter, and α′ is the preset characteristic index parameter.

[0150] According to the first characteristic index parameter And the preset scale parameter σ′ determines the corresponding first comprehensive scale parameter, that is, the comprehensive scale parameter of the positive Alpha stable distribution

[0151]

[0152] in, is the first comprehensive scale parameter, σ′ is the preset scale parameter, is the first characteristic index parameter.

[0153] S211. Determine a target sea clutter amplitude data matrix according to the first characteristic index parameter, the first comprehensive scale parameter, the random number matrix and the corresponding relationship table.

[0154] Among them, the target sea clutter amplitude data matrix is ​​used to simulate sea clutter data.

[0155] Specifically, according to the first characteristic index parameter, the first comprehensive scale parameter, the random number matrix and the corresponding relationship table, the target sea clutter amplitude data matrix is ​​determined, including:

[0156] Step F1: Compare the first characteristic index parameter with a plurality of target characteristic index parameters in the corresponding relationship table to determine the second characteristic index parameter.

[0157] Among them, the second characteristic index parameter is the first characteristic index parameter The parameter with the smallest difference.

[0158] The first characteristic index parameter Multiple target feature index parameters α in the corresponding relationship table p Compare and find the corresponding relationship table The closest α p Recorded as

[0159] Step F2: Compare the first comprehensive scale parameter with a plurality of target comprehensive scale parameters in the corresponding relationship table to determine a second comprehensive scale parameter.

[0160] Among them, the second comprehensive scale parameter is the first comprehensive scale parameter The parameter with the smallest difference.

[0161] The first comprehensive scale parameter Multiple target comprehensive scale parameters γ of the corresponding relationship table q Compare and find the corresponding relationship table Closest γ q Recorded as

[0162] Step F3: replace all the target characteristic index parameters in the corresponding relationship table with the second characteristic index parameter, and replace all the target comprehensive scale parameters in the corresponding relationship table with the second comprehensive scale parameter.

[0163] The corresponding relationship table as well as

[0164] Step F4: updating the values ​​of all cumulative distribution functions in the corresponding relationship table according to the second characteristic index parameter, the second comprehensive scale parameter and the corresponding multiple target sea clutter amplitude values, and obtaining all updated cumulative distribution function values.

[0165] According to the second characteristic index parameter The second comprehensive scale parameter and the corresponding multiple target sea clutter amplitude values, update the value of the cumulative distribution function in the corresponding relationship table, and obtain the value of all updated cumulative distribution functions

[0166] Step F5: Compare each element in the random number matrix with all updated cumulative distribution function values ​​in the corresponding relationship table to determine the final sea clutter amplitude value corresponding to each element, so as to determine the target sea clutter amplitude data matrix.

[0167] The final sea clutter amplitude value is the sea clutter amplitude value corresponding to the cumulative distribution function value with the smallest difference from each element.

[0168] The random number matrix D 1 Each element in the corresponding relationship table corresponds to the value of the cumulative distribution function of all updates Compare and determine the value of the cumulative distribution function that is closest to each element in the random number matrix Determine the corresponding final sea clutter amplitude value z from the value of the closest cumulative distribution function r , the final sea clutter amplitude value z r Denoted as z mn , where m = 1, 2, ..., M, n = 1, 2, ..., N, z mn are the elements in the target sea clutter amplitude data matrix, M is the number of sea clutter range units, and N is the number of pulses.

[0169] The target sea clutter amplitude data matrix Z is constructed based on the final sea clutter amplitude value corresponding to each element:

[0170] Z=[z mn ] M×N ;

[0171] Where Z is the target sea clutter amplitude data matrix.

[0172] Furthermore, after obtaining the target sea clutter amplitude data matrix, the sea clutter data of the symmetrical Alpha stable distribution can be simulated more accurately according to the target sea clutter amplitude data matrix, thereby achieving the purpose of accurate simulation of sea clutter, and the sea clutter data can be expanded so that the radar can identify the target according to the sea clutter data.

[0173] Figure 3The probability density comparison diagram is schematically shown. According to the simulation parameters (number of sea clutter distance units, number of pulses, preset characteristic index parameters and preset scale parameters), the simulation parameters of the positive Alpha stable distribution are obtained, and further substituted into the probability density function of the positive Alpha stable distribution with respect to the closed expression of the G function. The probability density function independent variable interval is taken as [1:1000], the interval is 1, and the probability density function image is drawn. It is compared with the ideal positive Alpha stable distribution probability density function image. The results are shown as follows: Figure 3 As shown. Figure 3 It can be seen that the probability density function value calculated by the probability density function of the positive alpha stable distribution represented by the G function closure in the present invention is very close to the probability density value of the ideal positive alpha stable distribution, and the relative entropy (Kullback-Leibler Divergence, KL distance) is 5.81×10 -4 This shows that the method of the present invention and related calculations can accurately characterize the probability density value of the positive Alpha stable distribution.

[0174] Figure 4 The schematic diagram of sea clutter amplitude data under simulation parameters is schematically shown. The sea clutter amplitude data under simulation parameters is generated by using the embodiment of the present invention, and the data is further schematically drawn. The horizontal axis represents different distance unit numbers, the vertical axis represents different echo pulses, and the color represents the amplitude of the generated sample. The sea clutter amplitudes under different distance units and different echo pulses are compared. The results are as follows: Figure 4 As shown. Figure 4 It can be seen that the amplitude of sea clutter generated in different range units and echo pulses is different.

[0175] Figure 5 The schematic diagram of the comparison of the probability density function curve is schematically shown. The sea clutter amplitude data under the simulation parameters are generated by using the embodiment of the present invention, and the data is further histogrammed to obtain the empirical probability density function of the simulation data, and the empirical probability density function curve is drawn and compared with the ideal probability density function curve. The results are shown in FIG. Figure 5 As shown. Figure 5 It can be seen that the sea clutter amplitude empirical probability density curve generated by the embodiment of the present invention is consistent with the ideal probability density function curve, indicating that the method of simulating positive Alpha stable distribution sea clutter amplitude data in the embodiment of the present invention is effective.

[0176] The positive Alpha stable distribution sea clutter amplitude data simulation method of the present invention uses Laplace inverse transform, Merlin transform and G function definition to express the probability density function of the positive Alpha stable distribution as a closed expression form about the G function, which overcomes the disadvantage that the probability density function of the positive Alpha stable distribution does not have a closed expression form about the elementary function. At the same time, the introduction of the G function provides feasibility and convenience for the calculation of the probability density function of the positive Alpha stable distribution and the simulation of symmetric Alpha stable distribution sea clutter data by the spherical invariant random process method. The Monte Carlo method is used to directly generate sea clutter amplitude data. Compared with the indirect method to generate clutter data, the steps are simpler and no cumbersome parameter conversion is required.

[0177] Based on the same inventive concept, as an implementation of the above-mentioned method for simulating sea clutter amplitude data with positive alpha stable distribution, an embodiment of the present invention further provides a device for simulating sea clutter amplitude data with positive alpha stable distribution. Figure 6 is a structural diagram of the device in the embodiment of the present invention, see Figure 6 As shown, the positive Alpha stable distribution sea clutter amplitude data simulation device may include:

[0178] An acquisition module 601 is used to acquire simulation parameters of symmetrical Alpha stable distribution sea clutter data, the simulation parameters including the number of sea clutter range units, the number of pulses, a preset characteristic index parameter and a preset scale parameter;

[0179] A first determination module 602 is used to determine a characteristic function of a positive Alpha stable distribution according to a preset characteristic function of an Alpha stable distribution, a preset parameter range and an initial comprehensive scale parameter;

[0180] A second determination module 603 is used to determine a probability density function according to a characteristic function of a positive Alpha stable distribution, an inverse Laplace transform, a Mellin transform property and a G function, wherein the probability density function is used to indicate a closed representation of the positive Alpha stable distribution with respect to the G function;

[0181] A third determination module 604 is used to determine a cumulative distribution function according to the probability density function;

[0182] Establishing module 605, for establishing a correspondence table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and corresponding cumulative distribution function values ​​according to the cumulative distribution function and the preset interval;

[0183] A generating module 606, for generating a random number matrix according to the number of sea clutter range units, the number of pulses and random distribution parameters;

[0184] The fourth determination module 607 is used to determine the target sea clutter amplitude data matrix according to the preset characteristic index parameter, the preset scale parameter, the random number matrix and the corresponding relationship table, and the target sea clutter amplitude data matrix is ​​used to simulate the sea clutter data.

[0185] The first determination module 602 is specifically used to obtain the characteristic function of the first positive Alpha stable distribution according to the characteristic function of the preset Alpha stable distribution and the preset parameter range; determine the characteristic function of the positive Alpha stable distribution according to the characteristic function of the first positive Alpha stable distribution and the initial comprehensive scale parameter.

[0186] The second determination module 603 is specifically used to convert the initial characteristic exponent parameter into an intermediate characteristic exponent parameter, which is a fractional expression of the initial characteristic exponent parameter; perform an inverse Laplace transform on the characteristic function of the positive Alpha stable distribution to obtain a first probability density function, which is a probability density function in integral form; obtain a second probability density function based on the Mellin transformation properties and the first probability density function; determine the probability density function based on the intermediate characteristic exponent parameter, the second probability density function, the properties of the Gamma function and the G function.

[0187] The second determination module 603 determines the probability density function according to the intermediate characteristic index parameter, the second probability density function, the Gamma function property and the G function, including: substituting the intermediate characteristic index parameter into the second probability density function and performing variable substitution to obtain a third probability density function; and determining the probability density function according to the Gamma function property, the G function and the third probability density function using the following first formula:

[0188]

[0189] Among them, a h =(u-h+1) / u,b j =(v-j+1) / v, h = 1, 2, ..., u, j = 1, 2, ..., v, f(z) is the probability density function, z is the initial sea clutter amplitude value, γ is the initial comprehensive scale parameter, v is the first intermediate parameter, u is the second intermediate parameter, a u is the third intermediate parameter, b v is the fourth intermediate parameter, is the Meijer G function with parameters (0, u, u, v), a h is the hth third intermediate parameter, b j is the jth fourth intermediate parameter.

[0190] The third determination module 604 is specifically used to determine the first cumulative distribution function according to the probability density function and the integral relationship between the probability density function and the cumulative distribution function; and obtain the cumulative distribution function according to the first cumulative distribution function using the following second formula:

[0191]

[0192] Among them, a′ h =(u-h+1) / u,b j ′=(vj) / v,h=1,2,...,u,j=1,2,...,v,F(z) is the cumulative distribution function, z is the initial sea clutter amplitude value, γ is the initial comprehensive scale parameter, v is the first intermediate parameter, u is the second intermediate parameter, a u ' is the fifth intermediate parameter, b v ' is the sixth intermediate parameter, is the Meijer G function with parameters (0, u, u, v), a′ h is the hth fifth intermediate parameter, b j ′ is the jth sixth intermediate parameter.

[0193] Establishing module 605 is specifically used to obtain multiple target characteristic index parameters according to the initial characteristic index parameter and the first preset interval at the preset time interval; obtain multiple target comprehensive scale parameters according to the preset time interval according to the initial comprehensive scale parameter and the second preset interval; obtain multiple target sea clutter amplitude values ​​according to the preset time interval according to the initial sea clutter amplitude value and the third preset interval; establish a three-dimensional parameter matrix according to the multiple target characteristic index parameters, the multiple target comprehensive scale parameters, and the multiple target sea clutter amplitude values, wherein each element in the three-dimensional parameter matrix is ​​an element including a target characteristic index parameter, a target comprehensive scale parameter, and a target sea clutter amplitude value; substitute each element in the three-dimensional parameter matrix into the cumulative distribution function to obtain the value of the cumulative distribution function corresponding to each element; associate each element with the corresponding cumulative distribution function value to establish a corresponding relationship table, wherein the preset interval includes the first preset interval, the second preset interval, and the third preset interval, and the cumulative distribution function includes the initial characteristic index parameter, the initial sea clutter amplitude value, and the initial comprehensive scale parameter.

[0194] The generating module 606 is specifically used to generate a zero matrix of a preset length according to the number of sea clutter range units, the number of pulses and the zero matrix, wherein the zero matrix of the preset length includes a plurality of zero elements; generate a plurality of random numbers according to a random distribution parameter, wherein the number of the plurality of random numbers is the same as the number of the plurality of zero elements; and replace the plurality of zero elements row by row with the plurality of random numbers to obtain a random number matrix.

[0195] The fourth determination module 607 is specifically used to determine the first characteristic index parameter and the first comprehensive scale parameter according to the preset characteristic index parameter and the preset scale parameter; and determine the target sea clutter amplitude data matrix according to the first characteristic index parameter, the first comprehensive scale parameter, the random number matrix and the corresponding relationship table.

[0196] The fourth determination module 607 determines the target sea clutter amplitude data matrix according to the first characteristic index parameter, the first comprehensive scale parameter, the random number matrix and the correspondence table, including: comparing the first characteristic index parameter with multiple target characteristic index parameters in the correspondence table to determine a second characteristic index parameter, the second characteristic index parameter being the parameter with the smallest difference from the first characteristic index parameter; comparing the first comprehensive scale parameter with multiple target comprehensive scale parameters in the correspondence table to determine a second comprehensive scale parameter, the second comprehensive scale parameter being the parameter with the smallest difference from the first comprehensive scale parameter; replacing all multiple target characteristic index parameters in the correspondence table with the first comprehensive scale parameter. The second characteristic index parameter is used, and the multiple target comprehensive scale parameters in the corresponding relationship table are replaced by the second comprehensive scale parameter; according to the second characteristic index parameter, the second comprehensive scale parameter and the corresponding multiple target sea clutter amplitude values, the values ​​of all cumulative distribution functions in the corresponding relationship table are updated to obtain all updated cumulative distribution function values; each element in the random number matrix is ​​compared with the values ​​of all updated cumulative distribution functions in the corresponding relationship table to determine the final sea clutter amplitude value corresponding to each element, so as to determine the target sea clutter amplitude data matrix, and the final sea clutter amplitude value is the sea clutter amplitude value corresponding to the cumulative distribution function value with the smallest difference from each element.

[0197] It should be pointed out here that: the above description of the embodiment of the positive Alpha stable distribution sea clutter amplitude data simulation device is similar to the description of the embodiment of the positive Alpha stable distribution sea clutter amplitude data simulation method, and has similar beneficial effects as the embodiment of the positive Alpha stable distribution sea clutter amplitude data simulation method. For technical details not disclosed in the embodiment of the positive Alpha stable distribution sea clutter amplitude data simulation device of the embodiment of the present invention, please refer to the description of the embodiment of the positive Alpha stable distribution sea clutter amplitude data simulation method of the present invention for understanding.

[0198] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for simulating positive alpha stable distribution sea clutter amplitude data. It is characterized in that The positive alpha stable distribution sea clutter amplitude data simulation method comprises: Acquiring simulation parameters of symmetrical Alpha stable distribution sea clutter data, wherein the simulation parameters include the number of sea clutter range units, the number of pulses, a preset characteristic index parameter, and a preset scale parameter; Determine the characteristic function of the positive Alpha stable distribution according to the characteristic function of the preset Alpha stable distribution, the preset parameter range and the initial comprehensive scale parameter; Determine a probability density function according to the characteristic function, inverse Laplace transform, Mellin transform property and G function of the positive Alpha stable distribution, wherein the probability density function is used to indicate a closed representation of the positive Alpha stable distribution with respect to the G function; determining a cumulative distribution function based on the probability density function; According to the cumulative distribution function and the preset interval, a corresponding relationship table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and corresponding values ​​of the cumulative distribution function is established; generating a random number matrix according to the number of sea clutter range units, the number of pulses and a random distribution parameter; According to the preset characteristic index parameter, the preset scale parameter, the random number matrix and the corresponding relationship table, a target sea clutter amplitude data matrix is ​​determined, and the target sea clutter amplitude data matrix is ​​used to simulate sea clutter data.

2. The method for simulating positive alpha stable distribution sea clutter amplitude data according to claim 1, It is characterized in that The step of determining the characteristic function of the positive Alpha stable distribution according to the characteristic function of the preset Alpha stable distribution, the preset parameter range and the initial comprehensive scale parameter includes: According to the characteristic function of the preset Alpha stable distribution and the preset parameter range, a characteristic function of a first positive Alpha stable distribution is obtained; The characteristic function of the positive alpha stable distribution is determined according to the characteristic function of the first positive alpha stable distribution and the initial comprehensive scale parameter.

3. The method for simulating positive alpha stable distribution sea clutter amplitude data according to claim 1, It is characterized in that The characteristic function of the positive Alpha stable distribution includes an initial characteristic exponential parameter, and the probability density function is determined according to the characteristic function of the positive Alpha stable distribution, the Laplace inverse transform, the Mellin transform property and the G function, including: Converting the initial characteristic index parameter into an intermediate characteristic index parameter, wherein the intermediate characteristic index parameter is a fractional expression of the initial characteristic index parameter; Performing an inverse Laplace transform on the characteristic function of the positive alpha stable distribution to obtain a first probability density function, where the first probability density function is a probability density function in an integral form; Obtaining a second probability density function according to the Mellin transformation property and the first probability density function; The probability density function is determined according to the intermediate characteristic index parameter, the second probability density function, the Gamma function property and the G function.

4. The method for simulating positive alpha stable distribution sea clutter amplitude data according to claim 3, It is characterized in that The determining the probability density function according to the intermediate characteristic index parameter, the second probability density function, the Gamma function property and the G function comprises: Substituting the intermediate characteristic index parameter into the second probability density function and performing variable substitution to obtain a third probability density function; According to the properties of the Gamma function, the G function and the third probability density function, the probability density function is determined using the following first formula: Among them, a h =(u-h+1) / u,b j =(v-j+1) / v, h=1,2,...,u, j=1,2,...,v, f(z) is the probability density function, z is the initial sea clutter amplitude value, γ is the initial comprehensive scale parameter, v is the first intermediate parameter, u is the second intermediate parameter, a u is the third intermediate parameter, b v is the fourth intermediate parameter, is the Meijer G function with parameters (0, u, u, v), a h is the hth third intermediate parameter, b j is the j-th fourth intermediate parameter.

5. The method for simulating positive alpha stable distribution sea clutter amplitude data according to claim 4, It is characterized in that Determining the cumulative distribution function according to the probability density function comprises: Determining a first cumulative distribution function according to the probability density function and the integral relationship between the probability density function and the cumulative distribution function; According to the first cumulative distribution function, the cumulative distribution function is obtained using the following second formula: Among them, a′ h =(u-h+1) / u,b j ′=(vj) / v,h=1,2,...,u,j=1,2,...,v,F(z) is the cumulative distribution function, z is the initial sea clutter amplitude value, γ is the initial comprehensive scale parameter, v is the first intermediate parameter, u is the second intermediate parameter, a u ' is the fifth intermediate parameter, b v ' is the sixth intermediate parameter, is the Meijer G function with parameters (0, u, u, v), a′ h is the fifth intermediate parameter of the hth order, b j ' is the jth sixth intermediate parameter.

6. The method for simulating positive alpha stable distribution sea clutter amplitude data according to claim 1, It is characterized in that The preset interval includes a first preset interval, a second preset interval and a third preset interval, the cumulative distribution function includes an initial characteristic index parameter, an initial sea clutter amplitude value and the initial comprehensive scale parameter, and establishing a correspondence table between a plurality of target characteristic index parameters, a plurality of target comprehensive scale parameters, a plurality of target sea clutter amplitude values ​​and corresponding values ​​of the cumulative distribution function according to the cumulative distribution function and the preset intervals, including: According to the initial characteristic index parameter and the first preset interval, acquiring the plurality of target characteristic index parameters at preset time intervals; According to the initial comprehensive scale parameter and the second preset interval, acquiring the plurality of target comprehensive scale parameters at the preset time interval; acquiring the plurality of target sea clutter amplitude values ​​according to the initial sea clutter amplitude value and the third preset interval at the preset time interval; A three-dimensional parameter matrix is ​​established according to the multiple target characteristic index parameters, the multiple target comprehensive scale parameters, and the multiple target sea clutter amplitude values, wherein each element in the three-dimensional parameter matrix is ​​an element including a target characteristic index parameter, a target comprehensive scale parameter, and a target sea clutter amplitude value; Substituting each element in the three-dimensional parameter matrix into the cumulative distribution function to obtain the value of the cumulative distribution function corresponding to each element; Each of the elements is associated with the corresponding value of the cumulative distribution function to establish the corresponding relationship table.

7. The method for simulating positive alpha stable distribution sea clutter amplitude data according to claim 1, It is characterized in that The generating a random number matrix according to the number of sea clutter range units, the number of pulses and random distribution parameters comprises: generating a zero matrix of a preset length according to the number of sea clutter range units, the number of pulses and a zero matrix, wherein the zero matrix of the preset length includes a plurality of zero elements; Generate a plurality of random numbers according to the random distribution parameter, wherein the number of the plurality of random numbers is the same as the number of the plurality of zero elements; The multiple zero elements are replaced row by row with the multiple random numbers to obtain the random number matrix.

8. The method for simulating positive alpha stable distribution sea clutter amplitude data according to claim 1, It is characterized in that The step of determining the target sea clutter amplitude data matrix according to the preset characteristic index parameter, the preset scale parameter, the random number matrix and the corresponding relationship table includes: Determining a first characteristic index parameter and a first comprehensive scale parameter according to the preset characteristic index parameter and the preset scale parameter; The target sea clutter amplitude data matrix is ​​determined according to the first characteristic index parameter, the first comprehensive scale parameter, the random number matrix and the corresponding relationship table.

9. The method for simulating positive alpha stable distribution sea clutter amplitude data according to claim 8, It is characterized in that Determining the target sea clutter amplitude data matrix according to the first characteristic index parameter, the first comprehensive scale parameter, the random number matrix and the corresponding relationship table includes: Comparing the first characteristic index parameter with the plurality of target characteristic index parameters in the corresponding relationship table to determine a second characteristic index parameter, wherein the second characteristic index parameter is the parameter having the smallest difference from the first characteristic index parameter; Comparing the first comprehensive scale parameter with the plurality of target comprehensive scale parameters in the correspondence table to determine a second comprehensive scale parameter, wherein the second comprehensive scale parameter is the parameter having the smallest difference from the first comprehensive scale parameter; The plurality of target characteristic index parameters in the correspondence table are all replaced by the second characteristic index parameter, and the plurality of target comprehensive scale parameters in the correspondence table are all replaced by the second comprehensive scale parameter; updating the values ​​of all the cumulative distribution functions in the corresponding relationship table according to the second characteristic index parameter, the second comprehensive scale parameter and the corresponding multiple target sea clutter amplitude values, to obtain all updated cumulative distribution function values; Each element in the random number matrix is ​​compared with all updated cumulative distribution function values ​​in the corresponding relationship table to determine a final sea clutter amplitude value corresponding to each element, so as to determine the target sea clutter amplitude data matrix, wherein the final sea clutter amplitude value is the sea clutter amplitude value corresponding to the cumulative distribution function value having the smallest difference from each element.

10. A positive alpha stable distribution sea clutter amplitude data simulation device, It is characterized in that The positive Alpha stable distribution sea clutter amplitude data simulation device comprises: An acquisition module, used for acquiring simulation parameters of symmetrical Alpha stable distribution sea clutter data, wherein the simulation parameters include the number of sea clutter range units, the number of pulses, a preset characteristic index parameter and a preset scale parameter; A first determination module is used to determine the characteristic function of the positive Alpha stable distribution according to the characteristic function of the preset Alpha stable distribution, the preset parameter range and the initial comprehensive scale parameter; A second determination module is used to determine a probability density function according to the characteristic function, inverse Laplace transform, Mellin transform property and G function of the positive Alpha stable distribution, wherein the probability density function is used to indicate a closed representation of the positive Alpha stable distribution with respect to the G function; A third determination module, used to determine a cumulative distribution function according to the probability density function; An establishment module is used to establish a correspondence table of multiple target characteristic index parameters, multiple target comprehensive scale parameters, multiple target sea clutter amplitude values ​​and corresponding values ​​of the cumulative distribution function according to the cumulative distribution function and a preset interval; A generating module, used for generating a random number matrix according to the number of sea clutter range units, the number of pulses and random distribution parameters; The fourth determination module is used to determine a target sea clutter amplitude data matrix according to the preset characteristic index parameter, the preset scale parameter, the random number matrix and the corresponding relationship table, wherein the target sea clutter amplitude data matrix is ​​used to simulate sea clutter data.

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