A method and system for optimizing the directivity pattern of sparse laser array antenna

Abstract

The present invention discloses a directional diagram optimization method and system for a sparse laser array antenna, the method comprising: determining the directional diagram function of the sparse laser array antenna according to the principle of electromagnetic field superposition; constructing a directional diagram optimization model for the sparse laser array antenna under the condition of satisfying equal amplitude omnidirectionality with the goal of reducing the directional diagram peak sidelobe level; solving the directional diagram optimization model using an improved dung beetle optimization algorithm to obtain an optimal array antenna distribution scheme. The initial population quality and global search capability of the algorithm are improved by improving the initial population distribution using two strategies, Tent chaotic mapping and reverse learning, and the global and local search capabilities of the algorithm are balanced during the iteration process by designing two local variation search strategies, an adaptive golden sine perturbation strategy and Levy flight, which not only improves the solution accuracy of the algorithm, but also avoids the algorithm from falling into the local optimum too early, and can adapt to the complex nonlinear problems of array antennas.

Description

Technical Field

[0001] The present invention belongs to the technical field of array antenna optimization, and in particular relates to a method and system for optimizing the directional pattern of a sparse laser array antenna. Background Art

[0002] Laser antennas have the characteristics of long transmission distance and fast transmission speed because their laser beams can maintain high energy density and small divergence angle; sparse array antennas can obtain the desired peak sidelobe level (PSLL), main lobe width and directivity coefficient by controlling the number and position of array elements in the uniform array, so that the antenna has high gain, narrow beam and low sidelobe performance. However, the design of sparse laser array antennas is a complex nonlinear optimization problem. How to effectively and quickly determine the distribution of array element parameters through optimization methods and obtain the desired radiation pattern is crucial to the design of array antennas.

[0003] With the continuous advancement of computer science and technology, researchers use computer technology to model and identify parameters of array antennas, and then conduct global searches through optimization algorithms to ultimately obtain the lowest peak sidelobe level, thereby determining the optimal array antenna layout. Existing algorithms for array antenna optimization mainly include genetic algorithms, particle swarm algorithms, and invasive weed optimization algorithms. These swarm intelligence algorithms have been widely used in the field of industrial optimization production. However, the design of sparse laser array antennas is a complex nonlinear optimization problem. These traditional algorithms cannot quickly and accurately determine the array element parameters and distribution to obtain the required radiation pattern. Summary of the invention

[0004] The present invention provides a directional pattern optimization method and system for a sparse laser array antenna, which are used to solve the technical problem that a large array antenna has multiple constraints and complex optimization parameters, traditional algorithms are difficult to converge in the optimization of the array antenna, and are prone to fall into local optimum, thus failing to quickly and accurately optimize the peak sidelobe level of the array antenna.

[0005] In a first aspect, the present invention provides a method for optimizing a directional pattern of a sparse laser array antenna, comprising:

[0006] Determine the directional pattern function of sparse laser array antennas based on the electromagnetic field superposition principle;

[0007] With the goal of reducing the peak sidelobe level of the pattern, a pattern optimization model for sparse laser array antennas is constructed under the condition of equal amplitude omnidirectionality.

[0008] The improved dung beetle optimization algorithm is used to solve the directional pattern optimization model to obtain an optimal array antenna distribution scheme, wherein the improved dung beetle optimization algorithm is used to solve the directional pattern optimization model, including:

[0009] The chaotic mapping reverse learning strategy is used to initialize the array element position. Specifically, after the uniformly distributed array elements are generated by the Tent chaotic mapping, the reverse learning strategy is used to generate the initial array element distribution.

[0010] Calculate the peak sidelobe level of each initial array element distribution, obtain the current minimum peak sidelobe level and the optimal array element distribution corresponding to the minimum peak sidelobe level, and update the array element position according to the four behaviors of rolling, breeding, foraging and stealing in the improved dung beetle optimization algorithm;

[0011] The local mutation strategy is started according to probability, and the position of the array element distribution is updated using the adaptive golden sine perturbation strategy, and the current array element position distribution is replaced according to the size of the peak sidelobe level;

[0012] Determine whether it is trapped in a local optimum. If so, start the Levy flight strategy to mutate the array element position and calculate the updated minimum peak sidelobe level of the array antenna distribution. If not, replace it with a more optimal array element distribution.

[0013] Updating the global optimal array element distribution position and the lowest peak sidelobe level corresponding to the global optimal array element distribution position;

[0014] Determine whether the termination condition is met. If not, continue iterating. Otherwise, output the global optimal array element distribution and the lowest peak sidelobe level of each generation.

[0015] In a second aspect, the present invention provides a directional pattern optimization system for a sparse laser array antenna, comprising:

[0016] A determination module configured to determine a directional pattern function of a sparse laser array antenna according to an electromagnetic field superposition principle;

[0017] A construction module is configured to construct a directional pattern optimization model of a sparse laser array antenna under the condition of satisfying equal amplitude omnidirectionality with the goal of reducing the directional pattern peak sidelobe level;

[0018] A solution module is configured to use an improved dung beetle optimization algorithm to solve the pattern optimization model to obtain an optimal array antenna distribution scheme, wherein the use of the improved dung beetle optimization algorithm to solve the pattern optimization model includes:

[0019] The chaotic mapping reverse learning strategy is used to initialize the array element position. Specifically, after the uniformly distributed array elements are generated by the Tent chaotic mapping, the reverse learning strategy is used to generate the initial array element distribution.

[0020] Calculate the peak sidelobe level of each initial array element distribution, obtain the current minimum peak sidelobe level and the optimal array element distribution corresponding to the minimum peak sidelobe level, and update the array element position according to the four behaviors of rolling, breeding, foraging and stealing in the improved dung beetle optimization algorithm;

[0021] The local mutation strategy is started according to probability, and the position of the array element distribution is updated using the adaptive golden sine perturbation strategy, and the current array element position distribution is replaced according to the size of the peak sidelobe level;

[0022] Determine whether it is trapped in a local optimum. If so, start the Levy flight strategy to mutate the array element position and calculate the updated minimum peak sidelobe level of the array antenna distribution. If not, replace it with a more optimal array element distribution.

[0023] Updating the global optimal array element distribution position and the lowest peak sidelobe level corresponding to the global optimal array element distribution position;

[0024] Determine whether the termination condition is met. If not, continue iterating. Otherwise, output the global optimal array element distribution and the lowest peak sidelobe level of each generation.

[0025] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method for optimizing the pattern of a sparse laser array antenna according to any embodiment of the present invention.

[0026] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the method for optimizing the directional pattern of a sparse laser array antenna of any embodiment of the present invention.

[0027] The sparse laser array antenna pattern optimization method and system of the present application is different from the deep learning method. It does not need to perform gradient learning based on a large amount of data through repeated iterations. It only needs to find the optimal array element layout scheme through group intelligence adaptation, avoiding time-consuming iterative training calculations. In addition, the method uses two strategies, Tent chaos mapping and reverse learning, to improve the initial population distribution to improve the algorithm's initial population quality and global search capabilities, and balances the algorithm's global and local search capabilities during the iteration process by designing two local variation search strategies, adaptive golden sine perturbation strategy and Levy flight, which not only improves the algorithm's solution accuracy, but also avoids the algorithm from falling into local optimality too early. It can adapt to the complex nonlinear problems of array antennas, thereby achieving the purpose of quickly deploying array elements and effectively optimizing the peak sidelobe level. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A flowchart of a method for optimizing the directivity pattern of a sparse laser array antenna provided by one embodiment of the present invention;

[0030] Figure 2 A schematic diagram of a sparse planar array according to a specific embodiment of the present invention is provided;

[0031] Figure 3 A fitness iteration curve diagram of different algorithms of a specific embodiment is provided for an embodiment of the present invention;

[0032] Figure 4 An embodiment of the present invention provides array directional diagrams after different algorithms are sparsely distributed in a specific embodiment;

[0033] Figure 5 An array element distribution diagram of a specific embodiment is provided for an embodiment of the present invention;

[0034] Figure 6 A symmetrical sparse planar array pattern of a specific embodiment is provided for an embodiment of the present invention;

[0035] Figure 7 An asymmetric sparse planar array pattern of a specific embodiment is provided for an embodiment of the present invention;

[0036] Figure 8 A structural block diagram of a directional pattern optimization system for a sparse laser array antenna provided in one embodiment of the present invention;

[0037] Fig. 9 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] See also Figure 1, which shows a flow chart of a method for optimizing the directivity pattern of a sparse laser array antenna of the present application.

[0040] like Figure 1 As shown, the directional pattern optimization method of the sparse laser array antenna specifically includes the following steps:

[0041] Step S101, determining the directivity function of the sparse laser array antenna according to the electromagnetic field superposition principle.

[0042] A sparse rate is , the array size is A rectangular planar sparse laser array such as Figure 2 The grid spacing is , is the radiation wavelength, and the number of array elements is .

[0043] Assuming that the equal-amplitude omnidirectionality is satisfied, according to the principle of electromagnetic wave superposition, the directional pattern function of the array can be expressed as:

[0044] ,

[0045] Where F(u, v) is the pattern function of the array antenna, × is the array aperture size, (m, n) is the location of the array element, is the maximum pointing direction of the array beam, is the working state of the array element at position (m, n), It means there is an array element there. It means there is no array element at that location. is the amplitude excitation coefficient of the array element at position (m, n), is the pitch angle, is the azimuth, , is the radiation wavelength, d = λ / 2, Is an imaginary unit.

[0046] Step S102, with the goal of reducing the peak sidelobe level of the directional pattern, a directional pattern optimization model of a sparse laser array antenna is constructed under the condition of satisfying equal amplitude omnidirectionality.

[0047] In this step, the goal is to reduce the peak sidelobe level of the directional pattern. Under the condition of equal amplitude omnidirectionality, and The maximum sidelobe level is searched in two planes. According to the PSLL definition, the fitness function is expressed as:

[0048] ,

[0049] In the formula, The fitness function of the directional graph optimization model, for The sidelobe interval of the azimuth pattern at time for The sidelobe interval of the elevation pattern is is the pitch angle, is the azimuth, for The sidelobe level in the sidelobe interval, for Sidelobe level in the sidelobe interval.

[0050] The directional pattern optimization model of sparse laser array antenna is: .

[0051] Step S103, using an improved dung beetle optimization algorithm to solve the pattern optimization model to obtain an optimal array antenna distribution solution.

[0052] In this step, the population is initialized: the mathematical model of the dung beetle optimization algorithm is described as follows: let the dung beetle scale be P, the initial dung beetle population , the dimension of the feasible solution space is , then the i-th dung beetle in the solution space is represented by The initial dung beetle population of the dung beetle optimization algorithm is randomly generated within a certain search range and evenly distributed in the solution space.

[0053] The random formation of the initial population means that: in the process of array antenna optimization, the present invention is aimed at the peak sidelobe level optimization of the sparse laser array antenna pattern. The optimization problem is the distribution of array antenna elements. Each distribution mode is based on the dung beetle Indicated by, and each array element is represented by a dung beetle Indicates that the working state of each array element and the number of dung beetles P are determined, and they are combined to form a dung beetle population, that is, dung beetles are used to represent the solution of the distribution of array elements of a sparse laser array antenna. Within a certain search parameter range, each dung beetle in the dung beetle population is determined by random rand method (that is, the working state of the array element is used as the dung beetle value in the dung beetle population) to form an initial population.

[0054] For array antenna optimization, the dung beetle optimization algorithm has a good balance between the global and local search performance of the algorithm, which is conducive to the effective search of the global optimal solution and the improvement of the overall execution efficiency. However, the initial population distribution of the dung beetle optimization algorithm is not uniform and the quality is not high, which affects the convergence speed of the algorithm. In view of the above defects of the basic dung beetle optimization algorithm, this paper proposes an improved dung beetle optimization algorithm based on chaotic map reverse learning.

[0055] Chaotic distribution is a relatively uniform distribution function. Because of its characteristics of randomness, ergodicity and regularity, it can well maintain population diversity and has been widely used in swarm intelligence algorithms. Compared with other mappings, Tent mapping has piecewise linear characteristics and can produce a relatively uniform initial population. Therefore, this paper selects Tent mapping to construct the initial population. The specific formula of Tent chaotic mapping is as follows:

[0056] ,

[0057] In the formula, the distribution range of chaotic sequence values ​​is , As a control parameter, it is set to 0.499 here. Through the Tent chaotic distribution method, a large number of initial load distribution rate parameter values ​​are generated within a certain model parameter search range.

[0058] Reverse learning can search in a wide range of solution spaces in a jumpy manner, which can speed up the individual mutation rate. In order to generate a higher quality initial population, after the initialization of the Tent chaotic map, the reverse learning strategy is used to generate N populations of the same size, and then the 2N individuals are sorted according to fitness, and the first N are selected as the initial population of dung beetles. The specific formula is as follows:

[0059] ,

[0060] In the formula, Represents a random number in the range (0, 1). and Respectively represent the lower and upper bounds of the current dung beetle, is the position of the i-th dung beetle individual before mutation;

[0061] The present invention adopts a chaotic mapping reverse learning strategy to improve the structure of the initial population, thereby avoiding the influence of uneven distribution and low quality of the initial population of the basic dung beetle optimization algorithm.

[0062] In order to avoid premature convergence of the optimization algorithm, this paper proposes an adaptive golden sine perturbation strategy to enhance the global exploration ability of the optimization algorithm and ensure population diversity. In the late stage of the algorithm, the adaptive golden sine perturbation strategy has good local search ability and can effectively improve the convergence efficiency and convergence accuracy. The expression of the adaptive golden sine perturbation strategy is:

[0063] ,

[0064] ,

[0065] ,

[0066] In the formula, is the position of the i-th dung beetle individual after mutation, is the position of the i-th dung beetle individual before mutation, Represents a random number, Determines the distance an individual moves. Determines the direction of individual movement. is the golden ratio coefficient, , are all parameters obtained by introducing the golden section coefficient. is the current optimal dung beetle individual, is the current iteration number.

[0067] In order to avoid falling into the local optimum, this paper introduces the Levy flight random walk strategy, which uses its combination of short-distance and long-distance flight methods to fully optimize the solution domain and enhance the algorithm's ability to jump out of the local optimal solution. The formula for Levy flight update position is:

[0068] ,

[0069] In the formula, is a vector operator, is the step size, which obeys the Levy distribution, is the step size factor, is the position of the dung beetle at the tth iteration, For the updated dung beetle location;

[0070] The Levy step factor affects the length and direction of flight. A larger step factor will result in a longer jump and a faster speed, while a smaller step factor will result in a smaller jump and a slower speed. In order to achieve a better effect of jumping out of the local optimum in the later stage of the DBO algorithm, an adaptive step adjustment strategy is proposed. It changes from a constant value to a variable value based on the number of iterations, which gradually increases in the later stages of the iterations and is defined as:

[0071] ,

[0072] ,

[0073] In the formula, To adjust the parameters, is a random number from (0,1), is an external frequency constant, with a value of 0.5. is the nonlinear constant intensity constant, with a value of 0.2. is the remainder operator, is the current iteration number, is the maximum number of iterations.

[0074] As the number of iterations increases, the moving step size gradually increases. After the moving step size is adjusted using the adaptive moving factor, the global search capability of the solution can be well limited in the early stage of local search evolution, which expands the optimization range of dung beetle individuals to a certain extent, so that most dung beetles can be well distributed in the feasible domain. When the algorithm is iterated to the later stage, the moving step size is larger, which can ensure the global search capability of the solution as much as possible. In addition, the adaptive adjustment of the moving step size avoids unnecessary detailed optimization search, which is conducive to accelerating the convergence speed of the algorithm.

[0075] Specifically, solving the directional pattern optimization model using the improved dung beetle optimization algorithm includes:

[0076] The chaotic mapping reverse learning strategy is used to initialize the array element position. Specifically, after the uniformly distributed array elements are generated by the Tent chaotic mapping, the reverse learning strategy is used to generate the initial array element distribution.

[0077] Calculate the peak sidelobe level of each initial array element distribution, obtain the current minimum peak sidelobe level and the optimal array element distribution corresponding to the minimum peak sidelobe level, and update the array element position according to the four behaviors of rolling, breeding, foraging and stealing in the improved dung beetle optimization algorithm;

[0078] The local mutation strategy is started according to probability, and the position of the array element distribution is updated using the adaptive golden sine perturbation strategy, and the current array element position distribution is replaced according to the size of the peak sidelobe level;

[0079] Determine whether it is trapped in a local optimum. If so, start the Levy flight strategy to mutate the array element position and calculate the updated minimum peak sidelobe level of the array antenna distribution. If not, replace it with a more optimal array element distribution.

[0080] Updating the global optimal array element distribution position and the lowest peak sidelobe level corresponding to the global optimal array element distribution position;

[0081] Determine whether the termination condition is met. If not, continue iterating. Otherwise, output the global optimal array element distribution and the lowest peak sidelobe level of each generation.

[0082] In a specific embodiment, an example of optimizing the sparse laser array antenna pattern using the improved dung beetle optimization algorithm will be shown. To ensure the fairness of the experiment, the unified experimental parameters are set: the population size and the maximum number of iterations of all algorithms are 60 and 200 respectively, the array size is 20×10, and the spacing between array elements is , the beam pointing angle is , and the coupling between array elements is neglected.

[0083] The AMSDBO algorithm (the algorithm of this application), DBO algorithm, PSO algorithm and IWO algorithm are applied to a 200-element planar array with a sparse rate of 50%, and their optimization effects are compared. The fitness iteration curves of these four optimization algorithms for sparse array optimization are shown in Figure 1. Figure 3 Compared with the other three algorithms, the AMSDBO algorithm shows better performance. In the early stage, it widely explores the solution space through the global search strategy; in the later stage, it avoids the local optimum through the individual mutation strategy, effectively accelerating the convergence to the global optimal solution.

[0084] exist Figure 4 The azimuth and elevation tangent patterns of the sparse array optimized by the four algorithms are shown in Figure 1. Figure 4 (a) and Figure 4 At (b), the 3dB width of the main lobe of all algorithms is consistent, which meets the expected goal; secondly, it can be seen that the maximum sidelobe level of the AMSDBO algorithm on the two slices is significantly lower than that of the other three algorithms. The peak sidelobe level of the AMSDBO algorithm is -56.10dB, the peak sidelobe level of the DBO algorithm is -48.57dB, the peak sidelobe level of the PSO algorithm is -50.25dB, and the peak sidelobe level of the IWO algorithm is -48.93dB. Compared with the traditional optimization algorithms (DBO, PSO, IWO), the peak sidelobe levels of the AMSDBO algorithm are reduced by 15.5%, 11.64%, and 14.65%, respectively.

[0085] Since the optimization effect of sparse arrays will be affected by the array sparsity rate, in order to further study the optimization effect of the proposed algorithm on planar arrays with different sparsity rates, experiments with different array sparsity rates were carried out, and the peak sidelobe levels of the optimized array patterns of the AMSDBO, DBO, PSO, and IWO algorithms were recorded. Table 1 shows the maximum sidelobe level values ​​of the patterns obtained by running each optimization algorithm 5 times under the same simulation conditions and taking the average value under different sparsity. Observing Table 1, the optimization effect of sparse arrays is also different under different sparsity rates. When the sparsity rate gradually decreases from 90% to 20%, the maximum sidelobe level of each optimization algorithm shows a trend of gradually decreasing. Among them, the AMSDBO algorithm performs best and can achieve the lowest peak sidelobe level in most cases. The research results show that compared with other optimization algorithms, the AMSDBO algorithm has a good optimization effect in solving the optimization problem of planar array antennas with different sparsity rates.

[0086] ,

[0087] In this example, in order to further verify the optimization effect of the AMSDBO algorithm on sparse planar array antennas, this simulation applies it to the planar array problem with constraints and compares it with an asymmetric distributed planar array.

[0088] Consider a planar array with the same simulation parameters as above, satisfying the constraint of symmetrical distribution of array elements and setting a 50% sparsity rate. The positions of symmetrically distributed array elements are as follows: Figure 5 As shown in (a) in the figure, the asymmetrically distributed array element positions are as follows Figure 5 As shown in (b) in the figure, the solid circle represents the array element in the turned-on state. Figure 5 The array element distribution in (a) satisfies the constraint of symmetrical distribution, and the dense layout in the center helps to reduce the maximum sidelobe level in the azimuth and elevation planes.

[0089] Original Figure 6 (a) and Figure 7 In (a) of the figure, the three-dimensional radiation patterns under symmetric and asymmetric array element distribution are presented respectively, both of which show low overall sidelobe levels, thus confirming the effectiveness of the AMSDBO algorithm in suppressing sidelobes. In order to further observe the sidelobe level value of the radiation pattern, Figure 6 (b) and Figure 7 (b) in the figure shows the sectional radiation pattern of the spatial radiation pattern in the two main planes of azimuth and elevation. It can be seen from the figure that the peak sidelobe level sum corresponding to the symmetrical array element distribution is -51.79dB. =0°, PSLL=-24.44dB; when =90°, PSLL=-27.35dB); the peak sidelobe level and the peak sidelobe level corresponding to the asymmetric array element distribution are -52.86dB (when =0°, PSLL=-23.07dB; when =90°, PSLL=-29.79dB). The experimental results show that the optimization effect of symmetrical array element distribution is almost the same as that of asymmetrical array element distribution, which confirms the effectiveness of AMSDBO algorithm in suppressing the sidelobes of constrained symmetrical array distribution.

[0090] In summary, this method introduces a chaotic mapping reverse learning strategy and a local mutation search strategy on the basis of the existing dung beetle optimization algorithm to enhance the performance of the algorithm. The chaotic mapping reverse learning strategy is used to generate a more uniform and higher quality initial solution set, while the local mutation search strategy includes an adaptive golden sine perturbation strategy and a Levy flight mechanism, which improves the efficiency of the algorithm in global search and local development. The organic combination of these strategies significantly enhances the algorithm's global exploration ability and local optimal solution escape ability, and effectively accelerates the algorithm's convergence process. The improved dung beetle optimization algorithm is applied to the solution of the sparse laser array antenna pattern optimization model, which can quickly obtain the optimal array solution and ensure the minimization of the peak sidelobe level.

[0091] The chaotic mapping reverse learning strategy generates the initial population, which enables the algorithm to obtain a larger search range with uniform distribution and a better initial solution set in the early stage, which is conducive to maintaining the diversity of the early population and obtaining a better global search capability; in addition, the local mutation search strategy balances the global search and local development, ensures the convergence speed and stability of the algorithm in the later stage, and improves the optimization effect of the peak sidelobe level. In short, the simulation experimental results show that the optimization results of this method are good, which can improve the optimization efficiency of sparse laser array antennas, and thus provide new methods and technologies for the optimization of actual sparse laser array antenna patterns.

[0092] See also Figure 8 , which shows a structural block diagram of a directional pattern optimization system for a sparse laser array antenna of the present application.

[0093] like Figure 8 As shown, the pattern optimization system 200 includes a determination module 210 , a construction module 220 and a solution module 230 .

[0094] Wherein, the determination module 210 is configured to determine the directional pattern function of the sparse laser array antenna according to the electromagnetic field superposition principle;

[0095] A construction module 220 is configured to construct a directional pattern optimization model of a sparse laser array antenna under the condition of satisfying equal amplitude omnidirectionality with the goal of reducing the directional pattern peak sidelobe level;

[0096] The solution module 230 is configured to use the improved dung beetle optimization algorithm to solve the pattern optimization model to obtain an optimal array antenna distribution scheme, wherein the use of the improved dung beetle optimization algorithm to solve the pattern optimization model includes:

[0097] The chaotic mapping reverse learning strategy is used to initialize the array element position. Specifically, after the uniformly distributed array elements are generated by the Tent chaotic mapping, the reverse learning strategy is used to generate the initial array element distribution.

[0098] Calculate the peak sidelobe level of each initial array element distribution, obtain the current minimum peak sidelobe level and the optimal array element distribution corresponding to the minimum peak sidelobe level, and update the array element position according to the four behaviors of rolling, breeding, foraging and stealing in the improved dung beetle optimization algorithm;

[0099] The local mutation strategy is started according to probability, and the position of the array element distribution is updated using the adaptive golden sine perturbation strategy, and the current array element position distribution is replaced according to the size of the peak sidelobe level;

[0100] Determine whether it is trapped in a local optimum. If so, start the Levy flight strategy to mutate the array element position and calculate the updated minimum peak sidelobe level of the array antenna distribution. If not, replace it with a more optimal array element distribution.

[0101] Updating the global optimal array element distribution position and the lowest peak sidelobe level corresponding to the global optimal array element distribution position;

[0102] Determine whether the termination condition is met. If not, continue iterating. Otherwise, output the global optimal array element distribution and the lowest peak sidelobe level of each generation.

[0103] It should be understood that Figure 8 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 8 The modules in it will not be described in detail here.

[0104] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the method for optimizing the directional pattern of a sparse laser array antenna in any of the above method embodiments;

[0105] As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0106] Determine the directional pattern function of sparse laser array antennas based on the electromagnetic field superposition principle;

[0107] With the goal of reducing the peak sidelobe level of the pattern, a pattern optimization model for sparse laser array antennas is constructed under the condition of equal amplitude omnidirectionality.

[0108] The improved dung beetle optimization algorithm is used to solve the directional pattern optimization model to obtain an optimal array antenna distribution scheme, wherein the improved dung beetle optimization algorithm is used to solve the directional pattern optimization model, including:

[0109] The chaotic mapping reverse learning strategy is used to initialize the array element position. Specifically, after the uniformly distributed array elements are generated by the Tent chaotic mapping, the reverse learning strategy is used to generate the initial array element distribution.

[0110] Calculate the peak sidelobe level of each initial array element distribution, obtain the current minimum peak sidelobe level and the optimal array element distribution corresponding to the minimum peak sidelobe level, and update the array element position according to the four behaviors of rolling, breeding, foraging and stealing in the improved dung beetle optimization algorithm;

[0111] The local mutation strategy is started according to probability, and the position of the array element distribution is updated using the adaptive golden sine perturbation strategy, and the current array element position distribution is replaced according to the size of the peak sidelobe level;

[0112] Determine whether it is trapped in a local optimum. If so, start the Levy flight strategy to mutate the array element position and calculate the updated minimum peak sidelobe level of the array antenna distribution. If not, replace it with a more optimal array element distribution.

[0113] Updating the global optimal array element distribution position and the lowest peak sidelobe level corresponding to the global optimal array element distribution position;

[0114] Determine whether the termination condition is met. If not, continue iterating. Otherwise, output the global optimal array element distribution and the lowest peak sidelobe level of each generation.

[0115] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the sparse laser array antenna pattern optimization system, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the sparse laser array antenna pattern optimization system via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0116] Fig. 9 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Fig. 9 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Fig. 9 In the example, the connection via bus is taken as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the sparse laser array antenna pattern optimization method of the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the sparse laser array antenna pattern optimization system. The output device 340 may include a display device such as a display screen.

[0117] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0118] As an implementation mode, the electronic device is applied to a sparse laser array antenna pattern optimization system, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0119] Determine the directional pattern function of sparse laser array antennas based on the electromagnetic field superposition principle;

[0120] With the goal of reducing the peak sidelobe level of the pattern, a pattern optimization model for sparse laser array antennas is constructed under the condition of equal amplitude omnidirectionality.

[0121] The improved dung beetle optimization algorithm is used to solve the directional pattern optimization model to obtain an optimal array antenna distribution scheme, wherein the improved dung beetle optimization algorithm is used to solve the directional pattern optimization model, including:

[0122] The chaotic mapping reverse learning strategy is used to initialize the array element position. Specifically, after the uniformly distributed array elements are generated by the Tent chaotic mapping, the reverse learning strategy is used to generate the initial array element distribution.

[0123] Calculate the peak sidelobe level of each initial array element distribution, obtain the current minimum peak sidelobe level and the optimal array element distribution corresponding to the minimum peak sidelobe level, and update the array element position according to the four behaviors of rolling, breeding, foraging and stealing in the improved dung beetle optimization algorithm;

[0124] The local mutation strategy is started according to probability, and the position of the array element distribution is updated using the adaptive golden sine perturbation strategy, and the current array element position distribution is replaced according to the size of the peak sidelobe level;

[0125] Determine whether it is trapped in a local optimum. If so, start the Levy flight strategy to mutate the array element position and calculate the updated minimum peak sidelobe level of the array antenna distribution. If not, replace it with a more optimal array element distribution.

[0126] Updating the global optimal array element distribution position and the lowest peak sidelobe level corresponding to the global optimal array element distribution position;

[0127] Determine whether the termination condition is met. If not, continue iterating. Otherwise, output the global optimal array element distribution and the lowest peak sidelobe level of each generation.

[0128] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the directional pattern of a sparse laser array antenna, characterized in that: include: Determine the directional pattern function of sparse laser array antennas based on the electromagnetic field superposition principle; With the goal of reducing the peak sidelobe level of the pattern, a pattern optimization model for sparse laser array antennas is constructed under the condition of equal amplitude omnidirectionality. The improved dung beetle optimization algorithm is used to solve the directional pattern optimization model to obtain an optimal array antenna distribution scheme, wherein the improved dung beetle optimization algorithm is used to solve the directional pattern optimization model, including: The chaotic mapping reverse learning strategy is used to initialize the array element position. Specifically, after the uniformly distributed array elements are generated by the Tent chaotic mapping, the reverse learning strategy is used to generate the initial array element distribution. Calculate the peak sidelobe level of each initial array element distribution, obtain the current minimum peak sidelobe level and the optimal array element distribution corresponding to the minimum peak sidelobe level, and update the array element position according to the four behaviors of rolling, breeding, foraging and stealing in the improved dung beetle optimization algorithm; The local mutation strategy is started according to probability, and the position of the array element distribution is updated using the adaptive golden sine perturbation strategy, and the current array element position distribution is replaced according to the size of the peak sidelobe level; Determine whether it is trapped in a local optimum. If so, start the Levy flight strategy to mutate the array element position and calculate the updated minimum peak sidelobe level of the array antenna distribution. If not, replace it with a more optimal array element distribution. Updating the global optimal array element distribution position and the lowest peak sidelobe level corresponding to the global optimal array element distribution position; Determine whether the termination condition is met. If not, continue iterating. Otherwise, output the global optimal array element distribution and the lowest peak sidelobe level of each generation.

2. The method for optimizing the directional pattern of a sparse laser array antenna according to claim 1, characterized in that: The expression of the pattern function is: ， Where F(u, v) is the pattern function of the array antenna, × is the array aperture size, (m, n) is the location of the array element, is the maximum pointing direction of the array beam, is the working state of the array element at position (m, n), It means there is an array element there. It means there is no array element at that location. is the amplitude excitation coefficient of the array element at position (m, n), is the pitch angle, is the azimuth, , is the radiation wavelength, d = λ / 2, Is an imaginary unit.

3. The method for optimizing the directional pattern of a sparse laser array antenna according to claim 1, characterized in that: The expression of the directional pattern optimization model is: ， ， In the formula, The fitness function of the directional graph optimization model, for The sidelobe interval of the azimuth pattern at time for The sidelobe interval of the elevation pattern is is the pitch angle, is the azimuth, for The sidelobe level in the sidelobe interval, for The sidelobe level in the sidelobe interval, is the maximum pointing direction of the array beam.

4. The method for optimizing the directional pattern of a sparse laser array antenna according to claim 1, characterized in that: The expression of the adaptive golden sine perturbation strategy is: ， ， ， In the formula, is the position of the i-th dung beetle individual after mutation, is the position of the i-th dung beetle individual before mutation, Represents a random number, Determines the distance an individual moves. Determines the direction of individual movement. is the golden ratio coefficient, , are all parameters obtained by introducing the golden section coefficient. is the current optimal dung beetle individual, is the current iteration number.

5. The method for optimizing the directional pattern of a sparse laser array antenna according to claim 1, characterized in that: The expression of the Levy flight strategy is: ， In the formula, is a vector operator, is the step length, obeying the Levy distribution, is the step size factor, is the position of the dung beetle at the tth iteration, For the updated dung beetle location; The step size factor is updated by using the Circle mapping hyperbolic sine adaptive step size adjustment strategy. The expression of the step size factor is: ， ， In the formula, To adjust the parameters, is a random number from (0,1), is an external frequency constant, with a value of 0.

5. is the nonlinear constant intensity constant, with a value of 0.

2. is the remainder operator, is the current iteration number, is the maximum number of iterations.

6. A sparse laser array antenna pattern optimization system, characterized in that: include: A determination module configured to determine a directional pattern function of a sparse laser array antenna according to an electromagnetic field superposition principle; A construction module is configured to construct a directional pattern optimization model of a sparse laser array antenna under the condition of satisfying equal amplitude omnidirectionality with the goal of reducing the directional pattern peak sidelobe level; A solution module is configured to use an improved dung beetle optimization algorithm to solve the pattern optimization model to obtain an optimal array antenna distribution scheme, wherein the use of the improved dung beetle optimization algorithm to solve the pattern optimization model includes: The chaotic mapping reverse learning strategy is used to initialize the array element position. Specifically, after the uniformly distributed array elements are generated by the Tent chaotic mapping, the reverse learning strategy is used to generate the initial array element distribution. Calculate the peak sidelobe level of each initial array element distribution, obtain the current minimum peak sidelobe level and the optimal array element distribution corresponding to the minimum peak sidelobe level, and update the array element position according to the four behaviors of rolling, breeding, foraging and stealing in the improved dung beetle optimization algorithm; The local mutation strategy is started according to probability, and the position of the array element distribution is updated using the adaptive golden sine perturbation strategy, and the current array element position distribution is replaced according to the size of the peak sidelobe level; Determine whether it is trapped in a local optimum. If so, start the Levy flight strategy to mutate the array element position and calculate the updated minimum peak sidelobe level of the array antenna distribution. If not, replace it with a more optimal array element distribution. Updating the global optimal array element distribution position and the lowest peak sidelobe level corresponding to the global optimal array element distribution position; Determine whether the termination condition is met. If not, continue iterating. Otherwise, output the global optimal array element distribution and the lowest peak sidelobe level of each generation.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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