Evaporation waveguide height prediction method and device for radar sea clutter, detection equipment and storage medium

Abstract

The invention relates to the technical field of radar signal processing, and discloses an evaporation waveguide height prediction method for radar sea clutter, and the method comprises the steps: optimizing an initial population based on an elite reverse learning strategy to construct an initial solution population; wherein the initial solution population is composed of estimated angle values; classifying the initial solution population according to the fitness values of the king butterfly individuals in the initial solution population to obtain a high-quality population and a suboptimal population; updating individual positions of the empire butterfly of the high-quality population and the suboptimal population based on an optimized MBO algorithm; iterating the combined solution population based on a compound mutation operator to obtain a target solution population; wherein the combined solution population is generated by combining the updated high-quality population and the suboptimal population. According to the method, the evaporation waveguide height prediction accuracy and stability can be improved. The invention also discloses an evaporation waveguide height prediction device for radar sea clutter, detection equipment and a storage medium.

Description

Technical Field

[0001] The present application relates to the field of radar signal processing technology, for example, to a method and device for predicting the evaporation waveguide height for radar sea clutter, a detection device, and a storage medium. Background Art

[0002] Tropospheric ducting, an anomalous radio wave propagation environment that frequently occurs over the ocean, can severely impact electronic systems such as radar and communications. Predicting the altitude of evaporation ducts based on radar sea clutter is a crucial research area, with widespread applications in radar, maritime target early warning, and detection, among other fields.

[0003] Maximum likelihood estimation, a classic method for predicting the height of evaporation ducts from radar sea clutter, offers advantages such as high resolution and high estimation accuracy. However, it requires a multi-dimensional, nonlinear spectral peak search, resulting in high computational complexity and low estimation efficiency. In recent years, researchers have applied intelligent optimization algorithms to maximum likelihood estimation, aiming to reduce computational complexity and improve estimation accuracy. While promising results have been achieved, these algorithms also suffer from issues such as low search efficiency and a tendency to fall into local optimality, leading to low angle estimation accuracy. Summary of the Invention

[0004] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical elements or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0005] The embodiments of the present disclosure provide a method, apparatus, detection equipment, and storage medium for predicting the evaporation duct height for radar sea clutter, so as to improve the accuracy and stability of the evaporation duct height prediction.

[0006] In some embodiments, the method includes: optimizing the initial population based on an elite reverse learning strategy to construct an initial solution population; wherein the initial solution population is composed of estimated angle values; classifying the initial solution population according to the fitness values ​​of monarch butterfly individuals in the initial solution population to obtain a high-quality population and a suboptimal population; updating the positions of monarch butterfly individuals in the high-quality population and the suboptimal population based on an optimized MBO algorithm; iterating the merged solution population based on a composite mutation operator to obtain a target solution population to obtain a target estimated angle value based on the target solution population; wherein the merged solution population is generated by merging the updated high-quality population and the suboptimal population.

[0007] In some embodiments, updating the position of monarch butterfly individuals in the high-quality population based on the optimized MBO algorithm includes: obtaining the monarch butterfly individuals with the best fitness values ​​in the high-quality population and the suboptimal population. Randomly select an individual from the superior population and the suboptimal population and Randomly select a monarch butterfly individual from the superior population and the suboptimal population and Where q1, q2∈[1,Q] and q1≠q2≠r1≠r2; according to the local search rule of the MBO algorithm, the individual positions of the monarch butterflies of the high-quality population are updated; where ∝ d Represents the mutation parameter, and the local search rules include:

[0008]

[0009] In some embodiments, updating the individual positions of monarch butterflies of the suboptimal population based on the optimized MBO algorithm includes: obtaining an upper limit value of the adjustment rate BAR max and the lower limit value of the adjustment rate BAR min , maximum number of iterations t m The adjustment rate is adjusted according to the adjustment rate formula of the MBO algorithm, and the suboptimal population is iterated based on the adjusted adjustment rate to update the individual positions of monarch butterflies in the suboptimal population; where t represents the current number of iterations, and the adjustment rate formula is: BAR = η + μt;

[0010] In some embodiments, the merged solution population is iterated based on a composite mutation operator to obtain a target solution population to obtain a target estimated angle value based on the target solution population, including: obtaining a Cauchy random number C that obeys a Cauchy distribution and a Gaussian random number N that obeys a Gaussian distribution; obtaining a first variable step length ∝1 and a second variable step length ∝2 associated with an adaptive strategy; wherein ∝1+∝2=1, the first variable step length ∝1 and the second variable step length ∝2 are both determined by the current number of iterations, and the first variable step length ∝1 is negatively correlated with the current number of iterations, and the second variable step length ∝2 is positively correlated with the current number of iterations; iterating the merged solution population according to a combined mutation operator, updating the positions of monarch butterfly individuals in the merged solution population to obtain a target solution population to obtain a target estimated angle value based on the target solution population; wherein the combined mutation operator is ∝1C+∝2N, and the new monarch butterfly individual is determined by the following formula:

[0011]

[0012] In some embodiments, optimizing the initial population based on the elite reverse learning strategy to construct the initial solution population includes: randomly generating the initial population s in the search space i,j ; Among them, the initial population is represented by P, P=[X1,X2,...,X Q ], X i =[x i,1 ,x i,2 ,...,x i,D ], X irepresents the position of the i-th monarch butterfly individual, Q represents the population size, and D represents the dimension of the solution space; according to the initial population s i,j , generate reverse population Merge initial population s i,j With reverse population Generate an initial solution population.

[0013] In some embodiments, the initial solution population is classified according to the fitness values ​​of the monarch butterfly individuals in the initial solution population to obtain a high-quality population and a suboptimal population, including: sorting the fitness values ​​of all monarch butterfly individuals in the initial solution population in descending order to obtain a sorting result; selecting the first N monarch butterfly individuals from the sorting result to constitute a high-quality population; and selecting other monarch butterfly individuals from the sorting result to constitute a suboptimal population.

[0014] In some embodiments, it also includes: after obtaining the target solution population, obtaining the target function value after each iteration; when the target function value meets the termination condition, determining the optimal solution after the end of this iteration as the target estimated angle value; or, when the target function value does not meet the termination condition, performing the next iteration and determining the optimal solution under the maximum number of iterations as the target estimated angle value.

[0015] In some embodiments, the apparatus includes a processor and a memory storing program instructions, and the processor is configured to execute the aforementioned evaporation duct height prediction method for radar sea clutter when running the program instructions.

[0016] In some embodiments, the detection device includes: a detection device body; and the aforementioned evaporation duct height prediction device for radar sea clutter, which is installed on the detection device body.

[0017] In some embodiments, the storage medium stores program instructions, which, when executed, enable a computer to execute the aforementioned evaporation duct height prediction method for radar sea clutter.

[0018] The method, apparatus, detection device, and storage medium for predicting the evaporation duct height of radar sea clutter provided by the embodiments of the present disclosure can achieve the following technical effects:

[0019] The disclosed embodiments improve the MBO algorithm from multiple aspects, such as optimizing the initial population and improving the optimization method, so that the optimized MBO algorithm can show better search ability and higher computational efficiency in evaporation duct height prediction, thereby improving the accuracy and stability of evaporation duct height prediction.

[0020] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] One or more embodiments are exemplarily described by corresponding drawings. These exemplary descriptions and drawings do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a scale limitation. In addition,

[0022] Figure 1 is a schematic diagram of a method for predicting the evaporation duct height for radar sea clutter provided in a disclosed embodiment;

[0023] Figure 2 is a schematic diagram of another method for predicting the evaporation duct height for radar sea clutter provided by an embodiment of the present disclosure;

[0024] Figure 3 is a schematic diagram of another method for predicting the evaporation duct height for radar sea clutter provided by an embodiment of the present disclosure;

[0025] Figure 4 is a schematic diagram of another method for predicting the evaporation duct height for radar sea clutter provided by an embodiment of the present disclosure;

[0026] Figure 5 This is an application diagram provided by an embodiment of the present disclosure;

[0027] Figure 6-1 RMSE (Root Mean Square Error) versus population size curve provided by the embodiment of the present disclosure;

[0028] Figure 6-2 is a curve showing the change of the average number of iterations with the population size provided by the embodiment of the present disclosure;

[0029] Figure 7 Schematic diagram of an evaporation duct height prediction device for radar sea clutter provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] In the description and claims of the embodiments of the present disclosure, as well as in the accompanying drawings, the terms "first," "second," and the like are used to distinguish similar items and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate to describe the embodiments of the present disclosure herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions.

[0031] Unless otherwise stated, the term "plurality" means two or more.

[0032] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0033] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0034] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0035] Combine Figure 1 As shown, an embodiment of the present disclosure provides a method for predicting the evaporation duct height for radar sea clutter, comprising:

[0036] In step S01, the detection device optimizes the initial population based on the elite reverse learning strategy to construct an initial solution population, wherein the initial solution population is composed of estimated angle values.

[0037] S02, the detection device classifies the initial solution population according to the fitness values ​​of the monarch butterflies in the initial solution population to obtain high-quality populations and sub-optimal populations.

[0038] S03, the detection equipment updates the individual positions of monarch butterflies in the high-quality population and the suboptimal population based on the optimized MBO algorithm.

[0039] S04: The detection device iterates the merged solution population based on a composite mutation operator to obtain a target solution population, thereby obtaining a target angle estimate based on the target solution population. The merged solution population is generated by merging the updated high-quality population with the suboptimal population.

[0040] The evaporation duct height prediction method for radar sea clutter, provided by the disclosed embodiments, optimizes the initial population based on an elite reverse learning strategy to construct an initial solution population. The initial solution population is then classified into high-quality and suboptimal populations based on the fitness values ​​of the monarch butterflies within the initial solution population. By constructing and adjusting the structure of the initial solution population, the diversity within the population is increased, reducing the risk of the optimized MBO algorithm falling into a local optimum. Furthermore, this population structure adjustment method draws on the effective search clues carried by the outstanding individuals within the existing population, accelerating the convergence of the global optimization process. The disclosed embodiments then update the positions of the monarch butterflies in the high-quality and suboptimal populations based on the optimized MBO algorithm. This strengthens the search strategy within the neighborhood of the superior solution through local search, significantly increasing the probability of finding the global optimal solution. This also overcomes the single inheritance mechanism problem of the original MBO algorithm through iteration. The disclosed embodiments also iterate the merged solution population using a composite mutation operator to dynamically adjust the step length, increasing population diversity while effectively addressing potential premature convergence issues. The disclosed embodiment further utilizes a composite mutation operator to perturb the currently optimal individual in the combined solution population to form a new individual, thereby obtaining a target solution population for height prediction. In summary, the disclosed embodiment improves the MBO algorithm from multiple perspectives, including optimizing the initial population and refining the optimization method. This allows the optimized MBO algorithm to demonstrate superior search capabilities and higher computational efficiency in evaporation duct height prediction, thereby improving the accuracy and stability of evaporation duct height prediction.

[0041] Optionally, the detection device updates the individual locations of monarch butterflies of high-quality populations based on an optimized MBO algorithm, including:

[0042] The detection equipment obtains the monarch butterfly individuals with the best fitness values ​​in the high-quality population and the sub-optimal population

[0043] The detection device randomly selects an individual from the high-quality population and the suboptimal population. and

[0044] The detection equipment randomly selects a monarch butterfly individual from the high-quality population and the low-quality population. and Among them, q1, q2∈[1,Q] and q1≠q2≠r1≠r2.

[0045] The detection equipment updates the individual locations of monarch butterflies from high-quality populations based on the local search rules of the MBO algorithm.

[0046] Among them, ∝ d Represents the mutation parameter, and the local search rules include:

[0047]

[0048] Since the optimal solution in the MBO algorithm is closer to the global optimal solution, strengthening the search strategy within the neighborhood of the optimal solution can significantly increase the probability of finding the global optimal solution. Based on this, the disclosed embodiments integrate the concepts of mutation and crossover, providing a differential mutation migration operator (i.e., a local search rule) and replacing the original migration operator of the original MBO algorithm. This allows the MBO algorithm to perform a local search near the optimal solution, increasing the probability of finding the global optimal solution.

[0049] Optionally, the detection device updates the individual positions of monarch butterflies of the suboptimal population based on an optimized MBO algorithm, including:

[0050] The detection device obtains the upper limit value of the adjustment rate BAR max and the lower limit value of the adjustment rate BAR min , maximum number of iterations t m .

[0051] The detection device adjusts the adjustment rate according to the adjustment rate formula of the MBO algorithm, and iterates the suboptimal population based on the adjusted adjustment rate to update the individual positions of monarch butterflies in the suboptimal population.

[0052] Where t represents the current number of iterations, and the adjustment rate formula is:

[0053] BAR=η+μt.

[0054]

[0055] Thus, in the original MBO algorithm, the migration rate and adjustment rate remain constant during the individual update process, and all individuals obtained by the adjustment operator are accepted. The disclosed embodiment introduces an adaptive adjustment operator (i.e., an adjustment rate formula) to linearly increase the adjustment rate with the number of iterations, thereby optimizing the adjustment operator of the original MBO algorithm and effectively overcoming the single inheritance problem of the original MBO algorithm.

[0056] In the embodiment of the present disclosure, the detection device introduces a Gauss-Cauchy mutation operator (hereinafter referred to as a combined mutation operator). The combined mutation operator represents a combination of a certain probability P and a certain number of variables. m Introducing and modifying some genes in the gene sequence of an individual to generate a new individual that is different from the original individual. Common mutation operations include Gaussian mutation, Cauchy mutation, and deletion mutation. The disclosed embodiment combines Gaussian mutation and Cauchy mutation to propose a combined mutation strategy.

[0057] Gaussian distribution is denoted as N(μ,σ 2 ), which describes the random variable x under the parameters μ, σ 2 The distribution characteristics under the condition of , its probability density function expression is:

[0058]

[0059] Among them, μ represents the expected value, σ represents the standard deviation, and σ 2 Represents variance.

[0060] The Cauchy distribution is denoted as C(γ,x0), and its probability density function is expressed as:

[0061]

[0062] Among them, x0 represents the location parameter and γ represents the scale parameter.

[0063] In order to solve the problem of the MBO algorithm being easily trapped in local optimality in the late iteration stage, the disclosed embodiment integrates the Gaussian distribution and Cauchy distribution principles to propose a combined mutation operator. By adopting a dynamic adaptive strategy to adjust the step length, the diversity of the population is increased, while effectively addressing the potential premature convergence problem. When updating the monarch butterfly population, the combined mutation operator is used to perturb the current optimal individual to form a new individual. The new individual expression is:

[0064]

[0065]

[0066] Where ∝1 represents the first variable step length, and ∝2 represents the second variable step length. Both ∝1 and ∝2 are parameters of the dynamic adaptation strategy. C represents a random number following a Cauchy distribution, and N represents a random number following a Gaussian distribution.

[0067] Optionally, combined Figure 2 As shown, the detection device iterates the merged solution population based on the composite mutation operator to obtain the target solution population to obtain the target estimated angle value based on the target solution population, including:

[0068] S11, the detection device obtains a Cauchy random number C that obeys a Cauchy distribution and a Gaussian random number N that obeys a Gaussian distribution.

[0069] S12: The detection device obtains a first variable step length ∝1 and a second variable step length ∝2 associated with the adaptive strategy. Here, ∝1+∝2=1, and both the first variable step length ∝1 and the second variable step length ∝2 are determined by the current number of iterations, and the first variable step length ∝1 is negatively correlated with the current number of iterations, while the second variable step length ∝2 is positively correlated with the current number of iterations.

[0070] S13, the detection device iterates the merged solution population according to the combined mutation operator, updates the individual positions of the monarch butterflies in the merged solution population to obtain the target solution population to obtain the target estimated angle value based on the target solution population.

[0071] Among them, the combined mutation operator is ∝1C+∝2N, and the new monarch butterfly individual is determined by the following formula:

[0072]

[0073] In this way, the embodiment of the present disclosure can increase the diversity of the population by adopting a dynamic adaptive strategy to adjust the variable step length, while effectively dealing with potential premature convergence problems, thereby accelerating the convergence rate of the MBO algorithm, reducing the amount of computation, and improving the stability of the optimized MBO algorithm in evaporation duct height prediction.

[0074] The detection device determines the first variable step length ∝1 and the second variable step length ∝2 in the following manner:

[0075]

[0076] Thus, by configuring the two parameters as described above, the first variable step length gradually decreases with the increase in the number of iterations, while the second variable step length gradually increases with the increase in the number of iterations. Thus, in the early stages of the iteration, the combined mutation strategy primarily utilizes the Cauchy distribution for perturbation, using a relatively large variable step length to reduce the risk of falling into a local minimum. In the later stages of the iteration, the combined mutation strategy primarily utilizes the Gaussian distribution for perturbation, using a relatively large variable step length, which is more conducive to more refined local exploration. Based on this, the disclosed embodiment configures a combined mutation operator to achieve adaptive adjustment of the variable step length, which helps overcome potential premature convergence problems, thereby accelerating the convergence rate of the MBO algorithm, reducing the algorithm's computational complexity, and improving the performance of the optimized MBO algorithm in evaporation duct height prediction. In summary, the disclosed embodiment improves the MBO algorithm from multiple perspectives, including optimizing the initial population, improving the optimization method, and adjusting the search step size, so that the optimized MBO algorithm exhibits better search capabilities and higher computational efficiency in evaporation duct height prediction, thereby improving the accuracy and stability of evaporation duct height prediction.

[0077] Optionally, combined Figure 3 As shown, the detection device optimizes the initial population based on the elite reverse learning strategy to construct the initial solution population, including:

[0078] S21, the detection device randomly generates an initial population s in the search space i,j Among them, the initial population is represented by P, P=[X1,X2,...,X Q ], X i =[x i,1 ,x i,2 ,...,x i,D ], X i represents the position of the i-th monarch butterfly individual, Q represents the population size, and D represents the dimension of the solution space.

[0079] S22, the detection device detects the initial population s i,j , generate reverse population

[0080] In this step, the detection device is based on the initial population s i,j , generate reverse population Including: Detection equipment chooses an elite individual; among them, the elite individual is recorded as s i,j =(s i,1 ,s i,2 ,...,s i,D ). The detection device obtains the reverse population according to the reverse solution formula The reverse solution formula is expressed as: u j =maxs i,j , l j =mins i,j ;maxs i,j Indicates the upper threshold of the j-th dimension search space, mins i,j Represents the lower threshold of the j-th dimension search space, a random number δ∈[0,1].

[0081] S23, detection equipment merges the initial population s i,j With reverse population Generate an initial solution population.

[0082] In this step, the detection equipment merges the initial population s i,j With reverse population Generate the initial solution population, including: detection device to reverse population Perform out-of-bounds check to obtain the reverse elite population. The out-of-bounds check is as follows:

[0083] Thus, the disclosed embodiments introduce an elite reverse learning strategy, which has a dual purpose. First, based on reverse learning records, this strategy effectively increases diversity within the population by adjusting the structure of the initial population, reducing the risk of falling into a local optimal solution. Second, this strategy draws on the effective search clues carried by outstanding individuals within the existing population, accelerating the convergence rate of the global optimal process.

[0084] Optionally, the detection device classifies the initial solution population according to the fitness values ​​of the monarch butterflies in the initial solution population to obtain a high-quality population and a suboptimal population, including:

[0085] The detection equipment sorts the fitness values ​​of all monarch butterfly individuals in the initial solution population in descending order to obtain the sorting result.

[0086] The detection equipment selects the first N monarch butterfly individuals from the sorting results to form a high-quality population.

[0087] The detection equipment selects other monarch butterfly individuals from the sorting results to form a suboptimal population.

[0088] In this way, by adjusting the structure of the initial solution population, the disclosed embodiment effectively increases the diversity within the population, reducing the risk of the MBO algorithm falling into a local optimal solution. Furthermore, the disclosed embodiment draws on the effective search clues carried by outstanding individuals within the population, accelerating the convergence rate of the entire optimization process.

[0089] It should be noted that the detection device sorts the fitness values ​​of all monarch butterfly individuals in the initial solution population in descending order to obtain a sorting result, including: the detection device determines the likelihood function as the fitness function; the detection device obtains the fitness values ​​of all monarch butterfly individuals in the initial solution population according to the fitness function; the detection device sorts the fitness values ​​of all monarch butterfly individuals in descending order to obtain a sorting result.

[0090] Combine Figure 4 As shown, the embodiment of the present disclosure also provides a method for predicting the evaporation duct height for radar sea clutter, comprising:

[0091] S31, the detection device optimizes the initial population based on the elite reverse learning strategy to construct an initial solution population, wherein the initial solution population is composed of estimated angle values.

[0092] S32, the detection device classifies the initial solution population according to the fitness values ​​of the monarch butterflies in the initial solution population to obtain a high-quality population and a sub-optimal population.

[0093] S33, the detection equipment updates the individual positions of monarch butterflies in the high-quality population and the suboptimal population based on the optimized MBO algorithm.

[0094] S34, the detection device iterates the merged solution population based on the composite mutation operator to obtain the target solution population, wherein the merged solution population is generated by merging the updated high-quality population and the suboptimal population.

[0095] S35, after the detection device obtains the target solution population, it obtains the target function value after each iteration.

[0096] S36, when the objective function value satisfies the termination condition, the detection device determines that the optimal solution after the end of this iteration is the target estimated angle value.

[0097] In this step, the detection device determines whether the objective function value meets the termination condition in the following manner: when the change in the objective function value is less than the function threshold, it is determined that the objective function value meets the termination condition; when the change in the objective function value is greater than or equal to the function threshold, it is determined that the objective function value does not meet the termination condition.

[0098] S37, when the objective function value does not meet the termination condition, the detection device performs the next iteration and determines the optimal solution under the maximum number of iterations as the target estimated angle value.

[0099] The evaporation duct height prediction method for radar sea clutter provided in the embodiments of the present disclosure is adopted. The embodiments of the present disclosure improve the MBO algorithm from multiple aspects, including optimizing the initial population and improving the optimization method. This enables the optimized MBO algorithm to exhibit better search capabilities and higher computational efficiency in evaporation duct height prediction, thereby improving the accuracy and stability of evaporation duct height prediction.

[0100] In a practical application, in the study of predicting evaporation duct height by radar sea clutter inversion, the evaporation duct height prediction problem based on maximum likelihood can be specifically described as:

[0101] By choosing a suitable evaporation duct height estimate The likelihood function is maximized. By combining the optimized MBO algorithm with the maximum likelihood estimation method, the likelihood function becomes the fitness function of the IMBO algorithm.

[0102] The initial population of monarch butterflies is P = [X1, X2, ..., X Q ]; where X i =[x i,1 ,x i,2 ,…,x i,D ] represents the position of the i-th monarch butterfly individual in the population, D represents the dimension of the solution space (that is, the number of information sources N), and Q represents the population size.

[0103] In each iteration, Q monarch butterfly individuals are generated, and their positions X i That is, the Q group estimate of the unknown parameter Θ, denoted as in In other words, x i,1 ,x i,2 ,…,x i,D Angle estimation value corresponding to each source

[0104] Combine Figure 5 As shown in FIG, the evaporation duct height prediction method for radar sea clutter specifically performs the following steps:

[0105] Step 101: Initialize algorithm parameters. The algorithm parameters include population size Q, dimension D and maximum number of iterations t m .

[0106] Step 102: Initialize the monarch butterfly population to obtain an initial population, that is, initialize the initial solution set of the angle estimation value. Generate a reverse elite population through step S23 in the above embodiment, and then merge the initial population with the reverse elite population to generate an initial solution population.

[0107] Step 103: Update individual positions (i.e. angles) by iteration value) and the current optimal value (i.e. the current optimal angle estimation value ):

[0108] a. Classify the initial population according to the fitness value of each monarch butterfly individual. The first N1 individuals with better fitness form the high-quality population, and the remaining (Q-N1) individuals form the suboptimal population.

[0109] b. Update the position of monarch butterflies in the high-quality population according to the local search rule.

[0110] c. Update the positions of individual monarch butterflies in the suboptimal population according to the adjustment rate formula.

[0111] d. Combine the high-quality populations from steps b and c with the suboptimal populations, and use the combined mutation operator to perform the optimal solution. Make perturbations to generate new solutions.

[0112] e. If the new solution value exceeds the defined angle search range, the updated new solution is randomly selected within the definition domain, and the fitness of the updated new solution is compared with the new solution before the update, and the new solution with the higher fitness value is selected as the current optimal solution

[0113] Step 104: After each iteration of step 103, the objective function value is obtained.

[0114] When the change in the objective function value is less than the function threshold, the algorithm terminates and outputs the current optimal solution. As the global optimal solution, the global optimal solution is determined as the target estimated angle value.

[0115] When the change in the objective function is greater than or equal to the function threshold, return to step 103 and iterate again until the maximum number of iterations t is reached. m , and determine the maximum number of iterations t m The optimal solution under is taken as the global optimal solution and the global optimal solution is determined as the target estimated angle value.

[0116] In another practical application, in order to more intuitively compare the advantages of the present application in terms of computational complexity, the computational complexity of the optimized monarch butterfly algorithm used in the present application depends only on the population size and the maximum number of iterations. The computational complexity C can be expressed as:

[0117] C=t m ×Q×Δ.

[0118] Among them, t mis the maximum number of iterations, and Q is the population size. Therefore, the population size Q is closely related to the computational effort C. For ML-DOA estimation methods based on intelligent algorithms, the population size determines the number of iterations involved and the number of likelihood functions to be solved in each iteration. Therefore, in practical scenarios, developing methods that can maintain high estimation accuracy even with limited population sizes is essential.

[0119] In the disclosed embodiment, Figure 6-1 and Figure 6-2 In the figure, the bat algorithm is represented by ML-BA (Maximum Likelihood Estimation-Bat Algorithm). The particle swarm algorithm is represented by ML-PSO (Maximum Likelihood Estimation-Particle Swarm Optimization). The monarch butterfly algorithm is represented by ML-MBO (Maximum Likelihood Estimation-Monarch Butterfly Optimization Algorithm). The method used in the present invention is represented by ML-IMBO (Maximum Likelihood Estimation-Improved Monarch Butterfly Optimization Algorithm). The crowd search algorithm is represented by ML-SOA (Seeker Optimization Algorithm).

[0120] In order to observe the effect of population size on estimation accuracy, Figure 6-2 The graph shows the average number of iterations as a function of the population size. It can be seen that across the entire range of population sizes, the average number of iterations for the ML-IMBO algorithm is significantly lower than for the other algorithms. The ML-BA, ML-PSO, and ML-MBO algorithms require more iterations to find the optimal solution. The ML-SOA algorithm, however, cannot converge within the maximum number of iterations.

[0121] Figure 6-1 The curve showing the variation of RMSE with population size shows that compared with the other four algorithms, the ML-IMBO algorithm can maintain a stable RMSE when the population size is small, and its average number of iterations is the lowest. The computational complexity formula (C) shows that the ML-IMBO algorithm has the lowest computational complexity compared with the other four algorithms.

[0122] Combine Figure 7As shown, an embodiment of the present disclosure provides an evaporation duct height prediction device 100 for radar sea clutter, comprising a processor 700 and a memory 701. Optionally, the device 70 may further comprise a communication interface 702 and a bus 703. The processor 700, the communication interface 702, and the memory 701 may communicate with each other via the bus 703. The communication interface 702 may be used for information transmission. The processor 700 may invoke logic instructions in the memory 701 to execute the evaporation duct height prediction method for radar sea clutter of the above embodiment.

[0123] In addition, the logic instructions in the memory 701 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0124] Memory 701, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as the program instructions / modules corresponding to the methods in the embodiments of the present disclosure. Processor 700 executes the program instructions / modules stored in memory 701 to perform functional applications and data processing, thereby implementing the evaporation duct height prediction method for radar sea clutter in the above-mentioned embodiments.

[0125] The memory 701 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal device. Furthermore, the memory 701 may include high-speed random access memory and non-volatile memory.

[0126] The presently disclosed embodiments provide a detection device comprising: a detection device body, and the aforementioned evaporation duct height prediction device 100 for radar sea clutter. The evaporation duct height prediction device 100 for radar sea clutter is mounted within the detection device body. The installation relationship described herein is not limited to placement within the detection device body but also includes installation connections with other components of the detection device, including but not limited to physical connections, electrical connections, or signal transmission connections. Those skilled in the art will appreciate that the evaporation duct height prediction device 100 for radar sea clutter can be adapted to any applicable detection device body, thereby implementing other feasible embodiments.

[0127] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned evaporation duct height prediction method for radar sea clutter.

[0128] The technical solutions of the embodiments of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present disclosure. The aforementioned storage medium may be a non-transitory storage medium, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code.

[0129] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to also include plural forms. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variations "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups of these. In the absence of further restrictions, an element defined by the sentence "comprising a..." does not exclude the presence of other identical elements in the process, method or device that includes the element. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments can be referenced to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can be found in the description of the method part.

Claims

1. A method for predicting the evaporation duct height for radar sea clutter, characterized in that: include: An initial population is optimized based on an elite reverse learning strategy to construct an initial solution population; wherein the initial solution population is composed of estimated angle values; wherein the initial solution population is composed of estimated angle values; According to the fitness values ​​of monarch butterflies in the initial population, the initial population is classified into high-quality population and suboptimal population; Update the individual positions of monarch butterflies in the high-quality and suboptimal populations based on the optimized MBO algorithm; The merged solution population is iterated based on a composite mutation operator to obtain a target solution population to obtain a target estimated angle value based on the target solution population; wherein the merged solution population is generated by merging the updated high-quality population and the suboptimal population.

2. The method according to claim 1, characterized in that Update the individual positions of monarch butterflies in high-quality populations based on the optimized MBO algorithm, including: Obtain the monarch butterfly individuals with the best fitness values ​​in the high-quality population and the suboptimal population Randomly select an individual from the superior population and the suboptimal population and Randomly select a monarch butterfly individual from the superior population and the suboptimal population and Among them, q1, q2∈[1,Q] and q1≠q2≠r1≠r2; According to the local search rule of the MBO algorithm, the individual positions of monarch butterflies in the high-quality population are updated; Among them, ∝ d Represents the mutation parameter, and the local search rules include:

3. The method according to claim 2, characterized in that The individual positions of monarch butterflies in the suboptimal population are updated based on the optimized MBO algorithm, including: Get the upper limit value of the adjustment rate BAR max and the lower limit value of the adjustment rate BAR min , maximum number of iterations t m ; The adjustment rate is adjusted according to the adjustment rate formula of the MBO algorithm, and the suboptimal population is iterated based on the adjusted adjustment rate to update the individual positions of monarch butterflies in the suboptimal population; Where t represents the current number of iterations, and the adjustment rate formula is: BAR = η + μt; 4. The method according to claim 1, wherein The combined solution population is iterated based on the composite mutation operator to obtain a target solution population to obtain a target estimated angle value based on the target solution population, including: Obtain a Cauchy random number C that obeys a Cauchy distribution and a Gaussian random number N that obeys a Gaussian distribution; Obtaining a first variable step length ∝1 and a second variable step length ∝2 associated with the adaptive strategy; wherein ∝1+∝2=1, the first variable step length ∝1 and the second variable step length ∝2 are both determined by the current number of iterations, and the first variable step length ∝1 is negatively correlated with the current number of iterations, while the second variable step length ∝2 is positively correlated with the current number of iterations; Iterating the merged solution population according to the combined mutation operator, updating the individual positions of the monarch butterflies in the merged solution population to obtain the target solution population, so as to obtain the target estimated angle value based on the target solution population; Among them, the combined mutation operator is ∝1C+∝2N, and the new monarch butterfly individual is determined by the following formula:

5. The method according to any one of claims 1 to 4, characterized in that The initial population is optimized based on the elite reverse learning strategy to construct the initial solution population, including: Randomly generate the initial population s in the search space i,j ; Among them, the initial population is represented by P, P=[X1,X2,...,X Q ], X i =[x i,1 ,x i,2 ,...,x i,D ], X i represents the position of the i-th monarch butterfly individual, Q represents the population size, and D represents the dimension of the solution space; According to the initial population s i,j , generate reverse population Merge initial population s i,j With reverse population Generate an initial solution population.

6. The method according to any one of claims 1 to 4, characterized in that According to the fitness values ​​of monarch butterflies in the initial population, the initial population is classified into high-quality population and suboptimal population, including: Sort the fitness values ​​of all monarch butterfly individuals in the initial solution population in descending order to obtain the sorting result; Select the top N monarch butterfly individuals from the sorting results to form a high-quality population; Other monarch butterfly individuals are selected from the sorting results to form a suboptimal population.

7. The method according to any one of claims 1 to 5, characterized in that Also includes: After obtaining the target solution population, obtain the objective function value after each iteration; When the objective function value satisfies the termination condition, the optimal solution after the iteration is determined to be the target estimated angle value; or When the objective function value does not meet the termination condition, the next iteration is performed and the optimal solution under the maximum number of iterations is determined as the target estimated angle value.

8. A device for predicting the evaporation duct height of radar sea clutter, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the evaporation duct height prediction method for radar sea clutter according to any one of claims 1 to 7 when running the program instructions.

9. A detection device, characterized in that: include: Detection equipment body; The evaporation duct height prediction device for radar sea clutter according to claim 8 is installed on the detection equipment body.

10. A computer-readable storage medium storing program instructions, characterized in that: When the program instructions are executed, the computer is configured to execute the evaporation duct height prediction method for radar sea clutter according to any one of claims 1 to 7.

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