Rapid optimization method for target shape profile RCS based on grid projection

Abstract

The application provides a target shape profile RCS fast optimization method based on grid projection, and belongs to the technical field of electromagnetic calculation, and the method comprises the following steps: obtaining the geometric and grid information of an initial target body; extracting the shape profile function of the initial target body; calculating the radar scattering cross section by using a preset full-wave electromagnetic simulation algorithm; optimizing the shape profile function of the target body according to the radar scattering cross section, so as to obtain the optimized target shape parameters; updating the geometric shape of the target body by using grid projection according to the optimized target shape parameters; iteratively optimizing the shape profile function of the target body, and outputting the iteration result; modeling and calculating the current distribution of the target according to the iteration result, processing and analyzing the geometric model of the target body by using the groove digging or capacitive inductive load increasing method, and completing the target shape profile RCS fast optimization. The application solves the problems of large time occupation and low efficiency of the existing shape profile optimization method in establishing a geometric model and grid division.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electromagnetic calculation, and particularly relates to a target shape profile RCS fast optimization method based on grid projection. BACKGROUND

[0002] With the development of electronic science and technology, the importance of electronic stealth technology is increasingly prominent, and becomes an important factor in modern design and engineering. For example, in the research of radar stealth performance, low RCS is the goal pursued. At present, the main methods to reduce the RCS of a target body include changing its shape profile and applying a wave-absorbing material. In particular, adjusting the geometric model of the target body is an effective way to optimize the RCS, which is usually completed through actual testing or software simulation. In order to reduce the cost, efficient full-wave electromagnetic simulation software is usually used to evaluate the RCS of the model. When using a full-wave algorithm to calculate the RCS, the geometric model of the target body needs to be meshed first to generate a grid file, and then the full-wave algorithm is applied to solve the RCS. However, when using simulation technology to optimize the model, the establishment of the geometric model and the meshing often occupy a large amount of time.

[0003] Nowadays, a common method to optimize the radar cross section (RCS) is to use a genetic algorithm to optimize the geometric shape of a conical target. The existing shape profile optimization method is mainly based on a genetic algorithm, and combines the programmable function of electromagnetic simulation software to customize the shape profile function of the optimized target. This process includes constructing a model according to the function geometry, meshing, and calculating the RCS by using electromagnetic simulation software. In this process, the RCS value is used as the fitness evaluation standard to guide the optimization of the shape profile function. Then, the modeling, meshing and RCS calculation are performed again until the number of iterations reaches the preset limit or the fitness of the optimal individual meets the preset threshold. Although the existing method is effective when applied to simple geometric bodies, when facing complex geometric bodies, the efficiency is seriously affected due to the increased difficulty of meshing and the prolonged meshing time when calculating the high-frequency band. SUMMARY

[0004] In view of the above problems in the prior art, the application provides a target shape profile RCS fast optimization method based on grid projection, which solves the problems of the existing shape profile optimization method that the establishment of the geometric model and the meshing occupy a large amount of time and the low efficiency.

[0005] In order to achieve the above purpose, the technical scheme adopted by the application is as follows: a target shape profile RCS fast optimization method based on grid projection, comprising the following steps:

[0006] S1, constructing a geometric model of an initial target body and obtaining a key process file;

[0007] S2, extract the shape contour function of the initial target body to obtain the target shape parameter of the geometric model, and determine the optimization interval of the target shape parameter;

[0008] S3, read the key process file by using a preset full-wave electromagnetic simulation algorithm, and calculate the radar cross section and the maximum change influence;

[0009] S4, according to the radar cross section, the maximum change influence and the target shape parameter of the geometric model, and combining the optimization interval of the target shape parameter, the shape contour function of the target body is optimized to obtain the optimized target shape parameter;

[0010] S5, according to the optimized target shape parameter, the target body geometry is updated by using grid projection;

[0011] S6, steps S3, S4 and S5 are taken as an iterative loop, the shape contour function of the target body is iteratively optimized, whether the preset condition is reached is judged, if yes, the iteration is stopped and the iteration result is output, otherwise the iteration is continued;

[0012] S7, according to the iteration result, the current distribution of the corresponding model is calculated, the geometric model of the target body is processed and analyzed by using the method of digging slot or increasing capacitive inductive load, and the target shape contour RCS fast optimization is completed.

[0013] The beneficial effects of the present application are: the present application constructs the geometric model of the target body, analyzes the target body by using the full-wave electromagnetic simulation algorithm, calculates the radar cross section, and continuously optimizes the shape contour function of the target body through loop iteration, realizes the target shape contour RCS fast optimization, shortens the time of establishing the geometric model and the grid division, and improves the efficiency of the shape contour optimization method.

[0014] Further, the S1 comprises the following steps:

[0015] S101, constructing the geometric model of the initial target body;

[0016] S102, performing non-conformal grid division on the geometric model to obtain the initial target body grid file;

[0017] S103, according to the preset electromagnetic simulation software, analyzing the initial target body grid file, and extracting the demand information of the preset electromagnetic simulation software to obtain the key process file.

[0018] The beneficial effects of the above further scheme are: the present application uses non-conformal grid division technology, uses finer grid in the geometric detail rich area of the target body, and uses larger grid in the area with relatively simple geometric structure, optimizes the distribution of the whole grid, and improves the calculation efficiency and accuracy of the shape contour optimization method.

[0019] Further, the S2 comprises the following steps:

[0020] S201, classifying the initial target body into a rotating body target and a complex geometric body target, extracting a parent line equation of the rotating body target, and calculating a first contour function through the parent line equation;

[0021] S202, modeling the external contour of the complex geometric body by using a surface parameterization technology, and extracting a second contour function;

[0022] S203, integrating the first contour function and the second contour function to obtain a contour function and a target contour parameter of the geometric model of the target body, and determining an optimization interval of the target contour parameter.

[0023] The above further scheme has the beneficial effects that: the target body is classified, the complex model is modeled in detail by using the surface parameterization calculation, the ability of the contour optimization method to dissect the complex geometric body is improved, the optimization interval of the appropriate target contour parameter is determined, and the efficiency of the overall contour optimization and the practicality of the optimization result are improved.

[0024] Further, the S3 comprises the following steps:

[0025] S301, setting simulation parameters according to a preset full-wave electromagnetic simulation algorithm, and obtaining a configuration file according to the optimization interval of the parameters in the target contour function;

[0026] S302, reading the configuration file and a key process file by using the preset full-wave electromagnetic simulation algorithm, calculating the radar scattering cross section of the target body, and obtaining the radar scattering cross section;

[0027] S303, observing the change of the radar scattering cross section of the target under each radar viewing angle, analyzing the radar scattering cross section, and determining the maximum shape change influence.

[0028] The above further scheme has the beneficial effects that: the radar scattering cross section is calculated by using the preset full-wave electromagnetic simulation algorithm, and the maximum shape change influence is determined, the calculation time of the radar scattering cross section is reduced, the targeted optimization influence object of the subsequent genetic algorithm is provided, large-scale problems can be effectively processed, and the precision and efficiency of the full-wave electromagnetic simulation are ensured.

[0029] Further, the S4 comprises the following steps:

[0030] S401, setting basic parameters of a genetic algorithm, and generating an initial population of the genetic algorithm according to the target contour parameter of the geometric model and in combination with the maximum shape change influence;

[0031] S402, take the radar scattering cross section as the fitness evaluation standard;

[0032] S403, according to the fitness evaluation standard and in combination with the optimization interval of the target shape parameter, the initial population is optimized by using genetic operation, and the optimal solution of each generation population is output, and the optimization of the shape contour function of the target body is completed;

[0033] S404, according to the optimal solution of each generation population, the optimized target shape parameter is obtained.

[0034] Further, the S403 comprises the following steps:

[0035] S4031, according to the initial population of the genetic algorithm, the individuals in the initial population are scored by using the fitness evaluation standard, and the excellent individuals are selected by using a preset selection method;

[0036] S4032, the selected excellent individuals are randomly paired, and the crossover operation is performed on the parameters of the excellent individuals to generate offspring individuals;

[0037] S4033, according to a preset probability, the parameters of the offspring individuals are randomly changed, and new characteristics are introduced to obtain the offspring population;

[0038] S4034, steps S4031-S4033 are repeated, and the individual with the highest fitness score is selected as the optimal solution of each generation;

[0039] S4035, in response to the termination of the genetic operation, the optimal solution of each generation population is output.

[0040] The beneficial effects of the above further scheme are: the genetic algorithm is used to optimize the shape contour function, and the radar scattering cross section is taken as the fitness evaluation standard, which improves the iteration speed of the genetic algorithm and enhances the optimization accuracy of the genetic algorithm for the shape contour function of the target body.

[0041] Further, the S5 comprises the following steps:

[0042] S501, the optimized target shape parameter is combined with the geometric model of the initial target body to obtain an updated shape function;

[0043] S502, the updated shape function is mapped to obtain an updated coordinate file;

[0044] S503, in combination with the updated coordinate file and the key process file, the updated geometric shape of the target body is obtained.

[0045] The beneficial effect of the further scheme is that the grid projection technology is used to directly map the optimized target shape profile to the original grid structure, thereby reducing repeated modeling steps, omitting the grid partitioning and analysis steps, and improving the efficiency of the overall shape profile optimization process.

[0046] Further, the S6 comprises the following steps:

[0047] S601, taking the calculation of the radar cross section, the optimization of the target shape profile, and the updating of the target body geometry as an iterative cycle, optimizing the shape profile of the target body, and recording the fitness of the individual in this iteration;

[0048] S602, determining whether the iterative cycle has reached a preset iteration number or the fitness of the target body radar cross section has reached a preset iteration threshold, if yes, stopping the iteration and executing step S603, otherwise continuing the iteration;

[0049] S603, selecting the individual with the highest fitness as the optimal solution according to the fitness of the individual in all iterations, and determining the optimal target shape parameter corresponding to the optimal solution as the optimal target shape parameter;

[0050] S604, defining the population completing the iteration as the optimal iteration population, and combining the optimal iteration population and the optimal target shape parameter as the iteration result output.

[0051] The beneficial effect of the further scheme is that the shape profile of the target body is optimized through the iterative cycle, and the whole-wave electromagnetic simulation algorithm, genetic algorithm, and grid projection are integrated to realize the rapid iteration of the shape profile function of the target body, thereby improving the efficiency and effect of the shape profile optimization.

[0052] Further, the S7 comprises the following steps:

[0053] S701, extracting the individual with the lowest radar cross section from the optimal iteration population in the iteration result;

[0054] S702, reconstructing the geometric model using the optimal target shape parameter in the iteration result to obtain a preliminary optimization model, and combining the individual with the lowest radar cross section to perform radar cross section simulation verification on the preliminary optimization model, and outputting the current distribution of the preliminary optimization model;

[0055] S703, processing the preliminary optimization model by using slotting or adding capacitive inductive load according to the current distribution to obtain an optimized target geometric model;

[0056] S704, performing radar cross section analysis on the optimized target geometric model to complete the rapid optimization of the target shape profile RCS.

[0057] The beneficial effects of the above further solutions are: the present application reduces the local current, reduces the current distribution of the target body, and effectively reduces the RCS, improves the accuracy of the RCS optimization of the target body, improves the effectiveness of the shape contour optimization, and provides more operable ways for RCS reduction, by regulating the current distribution, including but not limited to trenching or increasing capacitive inductive load in current-intensive areas. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 The flow chart of the method of the present application.

[0059] Figure 2 The geometric model of the initial target body in the example.

[0060] Figure 3 The grid projection schematic diagram in the example.

[0061] Figure 4 The grid model diagram in the example.

[0062] Figure 5 The preliminary optimization model diagram in the example.

[0063] Figure 6 The comparison diagram of the geometric model RCS before and after the preliminary optimization in the example.

[0064] Figure 7 The current distribution diagram of the preliminary optimization model in the example.

[0065] Figure 8 The part model diagram of the trenching process in the example.

[0066] Figure 9 The current distribution diagram after the trenching in the example.

[0067] Figure 10 The comparison diagram of the RCS of the preliminary optimization model and the optimized target geometric model in the example. DETAILED DESCRIPTION

[0068] The specific embodiments of the present application are described below to facilitate the understanding of the present application by those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, any changes that are obvious within the spirit and scope of the present application as defined and determined by the appended claims are obvious, and all applications utilizing the concept of the present application are within the scope of protection.

[0069] Before the present embodiment is described, the following terms are explained:

[0070] RCS: radar cross section;

[0071] theta: angle in the pitch plane.

[0072] Embodiment

[0073] As Figure 1 shown, the application provides a target shape profile RCS fast optimization method based on mesh projection, and the implementation method is as follows:

[0074] S1, construct a geometric model of the initial target body, obtain a key process file, and the specific steps are as follows:

[0075] S101, construct a geometric model of the initial target body;

[0076] S102, perform non-conformal mesh division on the geometric model to obtain an initial target body mesh file;

[0077] S103, according to a preset electromagnetic simulation software, parse the initial target body mesh file, and extract the required information of the preset electromagnetic simulation software to obtain a key process file.

[0078] In this embodiment, in order to obtain the geometric information and mesh information of the initial target body, as Figure 2 shown, a precise geometric model of the initial target body is constructed, and a model is taken as an example for implementation; Figure 2

[0079] As Figure 3 shown, non-conformal mesh division is performed on the precise geometric model, finer meshes are used in the geometric detail rich area of the target body, and larger meshes are used in the area with relatively simple geometric structure, an initial target body mesh file is generated, and the initial target body mesh file is taken as a starting point for optimization, and optimization is gradually performed; the initial target body mesh file is parsed, and then the key process file suitable for the preset high-efficiency electromagnetic simulation software is extracted to obtain Figure 4 as shown.

[0080] In this embodiment, the geometric model and mesh file of the required target body can be created by various modeling and mesh generation software.

[0081] In this embodiment, the preset high-efficiency electromagnetic simulation software is a Zhizhuan electromagnetic simulation software, the preset high-efficiency full-wave electromagnetic simulation algorithm is a multi-layer fast multipole algorithm, and the multi-layer fast multipole solver of the Zhizhuan electromagnetic simulation software is used as a calculation tool; the key process file of the multi-layer fast multipole solver is coordinate data and mesh index information.

[0082] S2, extract the shape profile function of the initial target body, obtain the target shape parameters of the geometric model, and determine the optimization interval of the target shape parameters, and the specific steps are as follows:

[0083] ​S201, classify the initial target body into a rotating body target and a complex geometric body target, extract the generatrix equation of the rotating body target, and calculate the first contour function through the generatrix equation;

[0084] S202, model the external contour of the complex geometric body using a surface parameterization technique to obtain a second contour function;

[0085] S203, integrate the first contour function and the second contour function to obtain the contour function and the target contour parameter of the geometric model, and determine the optimization interval of the target contour parameter.

[0086] In this embodiment, the contour function of the target body is extracted, and the optimization interval of the function parameter is determined;

[0087] Extract the contour function of the target body: For optimization targets, the optimization targets are divided into two target types for contour extraction. For rotating body targets, the key is to extract the generatrix equation, and the contour function is calculated using the generatrix equation. For irregularly shaped complex geometric bodies such as helicopters, a surface parameterization technique is used to model the contour in detail. According to the two target types, the contour function is extracted;

[0088] Determine the parameter optimization range: After extracting the contour function, determine the optimization interval of each parameter in the contour function. The appropriate optimization interval directly affects the efficiency of optimization and the practicality of the results.

[0089] S3, read the key process file using the preset full-wave electromagnetic simulation algorithm to calculate the radar cross section and the maximum change influence, the specific steps are as follows:

[0090] S301, according to the preset full-wave electromagnetic simulation algorithm, set the simulation parameters, and obtain the configuration file according to the optimization interval of the parameters in the target contour function;

[0091] S302, read the configuration file and the key process file using the preset full-wave electromagnetic simulation algorithm to calculate the radar cross section of the target body, and obtain the radar cross section;

[0092] S303, analyze the radar cross section by observing the change of the radar cross section of the target at each radar viewing angle, and determine the maximum contour change influence.

[0093] In this embodiment, the multilayer fast multipole algorithm is used to calculate the RCS, and the calculation process is as follows:

[0094] Set the simulation parameters: set the necessary simulation parameters in the electromagnetic simulation software to form a configuration file; the configuration file includes the range of radar operating frequency and the observation viewing angle of the target body;

[0095] RCS calculation: call the multi-layer fast multipole solver of the electromagnetic simulation software to read the configuration file and key process file, directly calculate the RCS of the target body, retrieve the events required for grid partitioning and grid analysis, effectively handle large-scale problems using the multi-layer fast multipole solver, and ensure the accuracy and efficiency of the simulation;

[0096] Data analysis: based on the calculated RCS, detailed analysis of the RCS is performed by observing the RCS changes of the target body at each radar viewing angle;

[0097] Optimization preparation: by analyzing the RCS, determine the maximum shape change impact of the target body on the RCS, and use the RCS as input data for the optimization step.

[0098] S4, according to the radar scattering cross section, the maximum change impact and the geometric model of the target shape parameter, and combining the optimization interval of the target shape parameter, the shape contour function of the target body is optimized, and the optimized target shape parameter is obtained, the specific steps are as follows:

[0099] S401, set the basic parameters of genetic algorithm, and generate the initial population of genetic algorithm according to the target shape parameter of geometric model combined with the maximum shape change impact;

[0100] S402, take the radar scattering cross section as the fitness evaluation standard;

[0101] S403, according to the fitness evaluation standard, and combining the optimization interval of the target shape parameter, the initial population is optimized by using genetic operation, and the optimal solution of each generation population is output, the optimization of the shape contour function of the target body is completed, the specific steps are as follows:

[0102] S4031, according to the initial population of genetic algorithm, score the individuals in the initial population by using the fitness evaluation standard, and select excellent individuals by using the preset selection method;

[0103] S4032, randomly pair the selected excellent individuals, and perform crossover operation on the parameters of the excellent individuals to generate offspring individuals;

[0104] S4033, according to the preset probability, randomly change part of the parameters of the offspring individuals, and introduce new characteristics to obtain the offspring population;

[0105] S4034, repeat steps S4031-S4033, and select the individual with the highest fitness score as the optimal solution of each generation;

[0106] S4035, in response to the termination of genetic operation, output the optimal solution of each generation population;

[0107] S404, obtaining the optimized target shape parameters according to the optimal solution of each generation population.

[0108] In this embodiment, the genetic algorithm is used to optimize the target shape contour, the genetic algorithm is initialized, and the basic parameters of the genetic algorithm are set, including: population size, crossover rate, and mutation rate, etc.; the initial population of the genetic algorithm is generated based on the target shape parameters of the initial geometric model combined with the maximum shape change influence; each individual in the initial population represents a set of possible target shape parameters;

[0109] The radar scattering cross section size of the target body is used as the standard for fitness evaluation, and in the genetic algorithm, the fitness function is used to evaluate the standard for the advantages and disadvantages of each individual in the population;

[0110] According to the fitness evaluation standard and combined with the optimization interval of the target shape parameters, the initial population is optimized using genetic operations, and the optimal solution of each generation population is output, and the optimization of the shape contour function of the target body is completed, which is specifically divided into the following processes:

[0111] Selection operation: according to the fitness score of the individual in the population, the excellent individual is selected from the current population according to the preset selection method to provide the "gene" for the next generation; the preset selection method usually adopts the roulette and tournament selection methods;

[0112] Cross operation: the selected excellent individuals are randomly paired, and cross operation is performed on the parameters of the paired individuals, information is exchanged between the parameters of the two paired individuals to increase the diversity of the population, and offspring individuals are generated;

[0113] Mutation operation: with a preset probability, the parameters of the offspring individuals are randomly changed, and new characteristics are introduced to prevent the algorithm from falling into a local optimal solution too early;

[0114] Select the individual with the highest fitness score as the optimal solution of each generation;

[0115] After the genetic operation is optimized, according to the output of the optimal solution of each generation population, the optimal solution of each generation population is the optimized target shape parameter.

[0116] In this embodiment, the method for optimizing the target shape contour is not limited to the genetic algorithm, and any optimization algorithm including but not limited to deep learning and simulated annealing algorithm can be selected according to the nature and complexity of the optimization problem; by constructing the nature and complexity of the actual optimization problem, the method for optimizing the target shape contour is determined, which provides greater flexibility and adaptability for the optimization process.

[0117] S5, according to the optimized target shape parameters, the target body geometry is updated using grid projection, and the specific steps are as follows:

[0118] S501, combine the optimized target shape parameters with the geometric model of the initial target body to obtain an updated shape function;

[0119] S502, map the updated shape function to obtain an updated coordinate file;

[0120] S503, combine the updated coordinate file and the key process file to obtain the updated geometric shape of the target body.

[0121] In this embodiment, the grid projection is performed to update the geometric shape of the target body. According to the optimized target shape parameters, the point coordinates in the key process file are combined with the optimized target shape contour. Then, the point coordinates in the key process file are mapped according to the optimized target shape function, and the updated coordinate file is generated by calculating the corresponding positions of the point coordinates on the new shape contour. Since the topological structure between the grids of the new and old shapes remains consistent, the grid index file in the key process file does not need to be changed, and the updated geometric shape of the target body is obtained. By using the same grid index file, the process of grid updating is simplified, and the integrity of the grid structure is maintained.

[0122] S6, steps S3, S4 and S5 are taken as an iterative loop to iteratively optimize the shape contour function of the target body. It is determined whether the iterative loop reaches a preset condition. If yes, the iteration is stopped and the iteration result is output. Otherwise, the iteration is continued. The specific steps are as follows:

[0123] S601, the steps of calculating the radar cross section, optimizing the target shape contour and updating the geometric shape of the target body are taken as an iterative loop to optimize the shape contour of the target body, and the fitness of the individual in this iteration is recorded;

[0124] S602, it is determined whether the iterative loop reaches a preset iteration number or the fitness of the radar cross section of the target body reaches a preset iteration threshold. If yes, the iteration is stopped and step S603 is executed. Otherwise, the iteration is continued;

[0125] S603, according to the fitness of the individual in all iterations, the individual with the highest fitness is selected as the optimal solution, and the parameters corresponding to the optimal solution are determined as the optimal target shape parameters;

[0126] S604, the population after completing the iteration is defined as the optimal iteration population, and the optimal iteration population and the optimal target shape parameters are combined as the iteration result output.

[0127] In this embodiment, the steps of calculating the RCS using the multi-layer fast multipole algorithm, the steps of optimizing the target shape contour using the genetic algorithm, and the steps of performing grid projection to update the geometric shape of the target body are integrated into an iterative loop, and the above steps are sequentially executed in each iteration to continuously optimize the shape contour of the target body.

[0128] After each iteration, it is determined whether the termination condition of the optimization algorithm is reached, the termination condition including: whether a predetermined number of iterations is reached or whether the fitness of the target RCS reaches a preset threshold;

[0129] When the loop meets the termination condition, the optimal iteration population is obtained, the individual with the highest fitness is selected from all iterations as the optimal solution, and the parameters of the optimal solution are determined as the optimal target shape parameters. The optimal solution and the optimal target shape parameters are combined to obtain the iteration result.

[0130] S7, according to the iteration result, the current distribution of the corresponding model is re-modeled and calculated, and the geometric model of the target body is processed and analyzed by using the method of slotting or increasing capacitive and inductive load, and the RCS fast optimization of the target shape contour is completed, the specific steps are as follows:

[0131] S701, according to the optimal iteration population in the iteration result, the individual with the lowest radar scattering cross section is extracted;

[0132] S702, the optimal target shape parameters in the iteration result are used to reconstruct the geometric model to obtain a preliminary optimization model, and the radar scattering cross section simulation verification is performed on the preliminary optimization model combined with the individual with the lowest radar scattering cross section, and the current distribution of the preliminary optimization model is output;

[0133] S703, according to the current distribution, the preliminary optimization model is processed by using the method of slotting or increasing capacitive and inductive load to obtain an optimized target geometric model;

[0134] S704, the radar scattering cross section analysis is performed on the optimized target geometric model to complete the RCS fast optimization of the target shape contour.

[0135] In this embodiment, the individual with the lowest RCS is extracted from the optimal iteration population in the iteration result obtained by the loop iteration, and the optimal target shape parameters are used to reconstruct the geometric model to obtain a preliminary optimization model, as shown in Figure 5 The RCS of the geometric model before and after the preliminary optimization is compared, and the comparison result is as shown in Figure 6 Theta in the figure represents the angle in the pitch plane, and dB represents decibel;

[0136] As shown in Figure 7 RCS simulation verification is performed on the preliminary optimization model to ensure the optimization effect and output the current distribution of the model;

[0137] As shown in Figure 8 The places with larger current are reduced by slotting or increasing capacitive and inductive load to reduce the current distribution and in turn reduce the RCS of the target body, and the optimized target geometric model is obtained, and the current distribution of the optimized target geometric model (i.e. the current distribution after slotting) is output asFigure 9 are shown;

[0138] As Figure 10 shown, the RCS changes of the preliminary optimization model and the optimized target geometric model are analyzed in detail, and the effect is evaluated. The optimized target geometric model is used as the final optimization model, and the RCS of the target contour is quickly optimized.

Claims

1. A fast optimization method for the RCS of a target shape contour based on mesh projection, characterized in that, Includes the following steps: S1. Construct the geometric model of the initial target body and obtain key process documents; S2. Extract the initial target body's outline function to obtain the target shape parameters of the geometric model, and determine the optimization range of the target shape parameters; S3. Using a preset full-wave electromagnetic simulation algorithm, key process files are read, and the radar cross section and maximum impact of modifications are calculated, specifically: S301. Based on the preset full-wave electromagnetic simulation algorithm, set the simulation parameters and obtain the configuration file according to the optimization range of the parameters in the target shape contour function; S302. Using a preset full-wave electromagnetic simulation algorithm, read the configuration file and key process files to calculate the radar cross section of the target and obtain the radar cross section. S303. By observing the changes in the radar cross section of the target under various radar views, analyze the radar cross section and determine the impact of the maximum shape modification. S4. Based on the radar cross section, maximum impact of modification, and target shape parameters of the geometric model, and combined with the optimization range of the target shape parameters, optimize the target shape contour function to obtain the optimized target shape parameters, specifically: S401. Set the basic parameters of the genetic algorithm, and generate the initial population of the genetic algorithm based on the target shape parameters of the geometric model and the influence of the maximum shape change. S402. Use radar cross section as a fitness evaluation criterion; S403. Based on the fitness evaluation criteria and combined with the optimization range of the target shape parameters, the initial population is optimized using genetic operations, and the optimal solution of each generation of the population is output to complete the optimization of the target body's shape contour function. S404. Based on the optimal solution of each generation of the population, obtain the optimized target shape parameters; S5. Update the geometry of the target body using mesh projection based on the optimized target shape parameters; S6. Using steps S3, S4 and S5 as an iterative loop, iteratively optimize the outer contour function of the target body, determine whether the iterative loop has reached the preset condition, if so, stop the iteration and output the iteration result, otherwise continue the iteration. S7. Based on the iteration results, remodel and calculate the current distribution of the corresponding model. Use the method of trenching or adding capacitive and inductive loads to process and analyze the geometric model of the target body to complete the rapid optimization of the target's outer contour RCS.

2. The method for fast optimization of target shape contour RCS based on mesh projection according to claim 1, characterized in that, S1 includes the following steps: S101. Construct the geometric model of the initial target body; S102. Perform non-conformal meshing on the geometric model to obtain the initial target volume mesh file; S103. Based on the preset electromagnetic simulation software, parse the initial target body mesh file and extract the requirement information of the preset electromagnetic simulation software to obtain the key process file.

3. The method for fast optimization of target shape contour RCS based on mesh projection according to claim 1, characterized in that, S2 includes the following steps: S201. Classify the initial target body into rotating targets and complex geometric targets. Extract the generatrix equation of the rotating targets and calculate the first shape contour function through the generatrix equation. S202. Model the external contour of the complex geometry using surface parametric technology and extract the second external contour function; S203. Integrate the first and second shape contour functions to obtain the shape contour function of the target body and the target shape parameters of the geometric model, and determine the optimization range of the target shape parameters.

4. The method for fast optimization of target shape contour RCS based on mesh projection according to claim 1, characterized in that, S403 includes the following steps: S4031. Based on the initial population of the genetic algorithm, score the individuals in the initial population using the fitness evaluation criteria, and select the best individuals using the preset selection method. S4032. Randomly pair the selected superior individuals and perform crossover operations on the parameters of the superior individuals to generate offspring individuals; S4033. Based on a preset probability, randomly change some parameters of the offspring individuals and introduce new characteristics to obtain the offspring population; S4034. Repeat steps S4031 to S4033, and select the individual with the highest fitness score as the optimal solution for each generation. S4035. In response to the termination of the genetic operation, output the optimal solution for each generation of the population.

5. The method for rapid optimization of target shape contour RCS based on mesh projection according to claim 2, characterized in that, S5 includes the following steps: S501. Combine the optimized target shape parameters with the geometric model of the initial target body to obtain the updated shape function; S502. Map the updated shape function and calculate the updated coordinate file; S503. By combining the updated coordinate file and the key process file, the updated geometry of the target body is obtained.

6. The method for fast optimization of target shape contour RCS based on mesh projection according to claim 1, characterized in that, S6 includes the following steps: S601. The calculation of radar cross section, optimization of target shape profile and updating of target geometry are used as an iterative loop to optimize the target shape profile and record the fitness of each individual in this iteration. S602. Determine whether the iteration loop has reached the preset number of iterations or whether the fitness of the target's radar cross section has reached the preset iteration threshold. If yes, stop the iteration and execute step S603; otherwise, continue the iteration. S603. Based on the fitness of individuals in all iterations, select the individual with the highest fitness as the optimal solution, and determine the parameters corresponding to the optimal solution as the optimal target shape parameters. S604. Define the population that has completed the iteration as the optimal iteration population, and combine the optimal iteration population and the optimal target shape parameters as the iteration result output.

7. The method for fast optimization of target shape contour RCS based on mesh projection according to claim 6, characterized in that, S7 includes the following steps: S701. Based on the optimal iterative population in the iterative results, extract the individual with the lowest radar cross section; S702. Reconstruct the geometric model using the optimal target shape parameters from the iteration results to obtain the preliminary optimized model. Combine the individual with the lowest radar cross section to perform radar cross section simulation verification on the preliminary optimized model and output the current distribution of the preliminary optimized model. S703. Based on the current distribution, the preliminary optimization model is processed by trenching or adding capacitive loads to obtain the optimized target geometric model. S704. Perform radar cross section analysis on the optimized target geometric model to complete the rapid optimization of the target's outline RCS.

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