Target contour RCS (Radar Cross-Section) rapid optimization method based on grid projection

Abstract

The invention provides a target contour RCS rapid optimization method based on grid projection, and belongs to the technical field of electromagnetic calculation, and the method comprises the steps: obtaining the geometric and grid information of an initial target body; extracting an outline function of the initial target body; calculating a radar cross section by using a preset full-wave electromagnetic simulation algorithm; according to the radar cross section, optimizing an outline function of the target body to obtain optimized target outline parameters; updating the geometrical shape of the target body by utilizing grid projection according to the optimized target appearance parameters; performing iterative optimization on the outline function of the target body, and outputting an iteration result; and modeling and calculating current distribution of the target according to an iteration result, and processing and analyzing a geometric model of the target body by utilizing a grooving or capacitive and inductive load increasing method to finish rapid optimization of the target contour RCS. According to the method, the problems that a large amount of time is occupied and the efficiency is low when a geometric model and mesh generation are established through an existing contour optimization method are solved.

Description

Technical Field

[0001] The invention belongs to the technical field of electromagnetic computing, and in particular relates to a method for quickly optimizing the RCS of a target shape profile based on grid projection. Background Art

[0002] With the development of electronic science and technology, the importance of electronic stealth technology has become increasingly prominent and has become an important factor in modern design and engineering. For example, in the study of radar stealth performance, low RCS is the goal to be pursued. At present, the main methods to reduce the RCS of the target body include changing its shape profile and applying absorbing materials. 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 costs, efficient full-wave electromagnetic simulation software is usually used to evaluate the RCS of the model. When using the full-wave algorithm for RCS calculation, the geometric model of the target body must first be meshed to generate a mesh file, and then the full-wave algorithm is applied to solve the RCS. However, when using simulation technology for model optimization, establishing the geometric model and meshing often take up a lot of time.

[0003] Nowadays, a common method for optimizing radar cross section (RCS) is to optimize the geometric shape of cone targets using genetic algorithms; existing shape profile optimization methods are mainly based on genetic algorithms, combined with the programmable functions of electromagnetic simulation software to customize the shape profile function of the optimization target. This process includes building a model based on the function geometry, meshing, and calculating RCS using electromagnetic simulation software. In this process, the RCS value is used as a fitness evaluation criterion to guide the optimization of the shape profile function. Subsequently, modeling, meshing, and RCS calculation are performed again until the number of iterations reaches a preset limit or the fitness of the optimal individual meets the preset threshold. Although the existing methods are effective on simple geometries, when faced with complex geometries, the efficiency is seriously affected due to the increased difficulty of meshing and the extended meshing time caused by calculating high-frequency bands. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for rapid optimization of the RCS of a target shape contour based on grid projection, which solves the problem that the establishment of geometric models and grid division in the existing shape contour optimization methods takes a lot of time and is inefficient.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: a method for rapid optimization of the RCS of a target contour based on grid projection, comprising the following steps:

[0006] S1. Build the geometric model of the initial target body and obtain key process files;

[0007] S2, extracting the shape contour function of the initial target body, obtaining the target shape parameters of the geometric model, and determining the optimization interval of the target shape parameters;

[0008] S3. Use the preset full-wave electromagnetic simulation algorithm to read the key process files and calculate the radar cross section and the maximum change impact;

[0009] S4, optimizing the target body's shape contour function according to the radar scattering cross section, the maximum change impact and the target shape parameters of the geometric model, and combining the optimization interval of the target shape parameters to obtain optimized target shape parameters;

[0010] S5. Update the target body geometry using mesh projection according to the optimized target shape parameters;

[0011] S6, taking steps S3, S4 and S5 as an iterative loop, iteratively optimizing the contour function of the target body, and judging whether the iterative loop meets the preset condition, if so, stopping the iteration and outputting the iteration result, otherwise continuing the iteration;

[0012] S7. Re-model and calculate the current distribution of the corresponding model based on the iteration results. Use the method of digging grooves 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 shape contour RCS.

[0013] The beneficial effects of the present invention are as follows: the present invention constructs a geometric model of the target body, uses a full-wave electromagnetic simulation algorithm to analyze the target body, calculates the radar scattering cross section, and continuously optimizes the shape contour function of the target body through cyclic iteration, thereby achieving rapid optimization of the target shape contour RCS, shortening the time for establishing the geometric model and meshing, and improving the efficiency of the shape contour optimization method.

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

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

[0016] S102, performing non-conformal meshing on the geometric model to obtain an initial target volume mesh file;

[0017] S103, according to the preset electromagnetic simulation software, the initial target body mesh file is parsed, and the requirement information of the preset electromagnetic simulation software is extracted to obtain the key process file.

[0018] The beneficial effect of the above further scheme is: the present invention uses non-conformal meshing technology to use finer meshes in areas with rich geometric details of the target body and uses larger meshes in areas with relatively simple geometric structures, thereby optimizing the distribution of the overall mesh and improving the computational efficiency and accuracy of the shape contour optimization method.

[0019] Furthermore, 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 the generatrix equation of the rotating body target, and calculating the first shape contour function through the generatrix equation;

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

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

[0023] The beneficial effects of the above further scheme are as follows: the present invention improves the ability of the shape contour optimization method to segment complex geometric bodies by classifying the target bodies and performing detailed modeling of complex models using surface parameterization calculations, and determines the appropriate optimization range of the target shape parameters, thereby improving the efficiency of the overall shape contour optimization and the practicality of the optimization results.

[0024] Furthermore, 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 an optimization interval of parameters in a target shape profile function;

[0026] S302, using a preset full-wave electromagnetic simulation algorithm to read a configuration file and a key process file, calculate a radar cross section of the target body, and obtain a radar cross section;

[0027] S303. Analyze the radar cross section by observing the change of the radar cross section of the target under each radar viewing angle, and determine the maximum impact of the shape change.

[0028] The beneficial effects of the above further scheme are as follows: the present invention adopts a preset full-wave electromagnetic simulation algorithm to calculate the radar cross section and determine the maximum impact of shape changes, thereby reducing the calculation time of the radar cross section, providing targeted optimization of the impact object for the subsequent genetic algorithm, and being able to effectively handle large-scale problems, thereby ensuring the accuracy and efficiency of full-wave electromagnetic simulation.

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

[0030] S401, setting basic parameters of the genetic algorithm, and generating an initial population of the genetic algorithm according to target shape parameters of the geometric model and the maximum shape change impact;

[0031] S402, using radar cross section as a fitness evaluation criterion;

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

[0033] S404. Obtain optimized target shape parameters according to the optimal solution of each generation population.

[0034] Furthermore, the S403 includes the following steps:

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

[0036] S4032, randomly pairing the selected excellent individuals, and performing a crossover operation on the parameters of the excellent individuals to generate offspring individuals;

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

[0038] S4034, repeating steps S4031 to S4033, selecting the individual with the highest fitness score as the optimal solution for each generation;

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

[0040] The beneficial effect of the above further scheme is: the present invention optimizes the shape profile function through the genetic algorithm and uses the radar scattering cross section as the fitness evaluation standard, thereby improving the iteration speed of the genetic algorithm and enhancing the optimization accuracy of the genetic algorithm for the shape profile function of the target body.

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

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

[0043] S502, mapping the updated shape function to obtain an updated coordinate file by calculation;

[0044] S503: Combine the updated coordinate file and the key process file to obtain an updated geometric shape of the target body.

[0045] The beneficial effect of the above further scheme is: the present invention uses grid projection technology to directly map the optimized target body contour to the original grid structure, reducing repeated modeling steps, omitting grid division and analysis steps, thereby improving the efficiency of the overall contour optimization process.

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

[0047] S601, calculating the radar cross section, optimizing the target outline, and updating the target body geometry are used as an iterative loop to optimize the target body outline and record the fitness of the individual in this iteration;

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

[0049] S603, according to 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;

[0050] S604: Define the population that has completed iteration as the optimal iteration population, and combine the optimal iteration population and the optimal target shape parameter to output the iteration result.

[0051] The beneficial effect of the above further scheme is: the present invention optimizes the shape contour of the target body through iterative cycles, and realizes the rapid iteration of the shape contour function of the target body by integrating the full-wave electromagnetic simulation algorithm, genetic algorithm and grid projection, thereby improving the efficiency and effect of shape contour optimization.

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

[0053] S701, extracting an individual with the lowest radar cross section according to the optimal iterative population in the iterative result;

[0054] S702, reconstructing the geometric model using the optimal target shape parameters 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, according to the current distribution, the preliminary optimization model is processed by digging grooves or adding capacitive and inductive loads to obtain an optimized target geometric model;

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

[0057] The beneficial effect of the above further scheme is: the present invention reduces the local current and the current distribution of the target body by regulating the current distribution, including but not limited to digging grooves or increasing capacitive and inductive loads in current-intensive areas, thereby effectively reducing the RCS, improving the accuracy of RCS optimization of the target body, and improving the effectiveness of shape profile optimization, and also providing more operational ways to reduce RCS. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 The figure is a flow chart of the method of the present invention.

[0059] Figure 2 Schematic diagram of the geometric model of the initial target body in the embodiment.

[0060] Figure 3 Schematic diagram of grid projection in the embodiment.

[0061] Figure 4 It is a grid model diagram in the embodiment.

[0062] Figure 5 This is a preliminary optimization model diagram in the embodiment.

[0063] Figure 6 It is a comparison diagram of the RCS of the geometric model before and after the preliminary optimization in the embodiment.

[0064] Figure 7 This is the current distribution diagram of the preliminary optimization model in the embodiment.

[0065] Figure 8 This is a model diagram of the part where the groove is to be processed in the embodiment.

[0066] Fig. 9 This is a current distribution diagram after trenching in the embodiment.

[0067] Fig.10 It is a comparison diagram of the RCS of the preliminary optimized model and the optimized target geometric model in the embodiment. DETAILED DESCRIPTION

[0068] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0069] Before describing this embodiment, the following terms are explained:

[0070] RCS: radar cross section;

[0071] theta: Angle in the pitch plane.

[0072] Example

[0073] like Figure 1 As shown, the present invention provides a method for rapid optimization of the RCS of a target contour based on grid projection, and the implementation method thereof is as follows:

[0074] S1. Build the geometric model of the initial target body and obtain key process files. The specific steps are as follows:

[0075] S101, constructing a geometric model of an initial target body;

[0076] S102, performing non-conformal meshing on the geometric model to obtain an initial target volume mesh file;

[0077] S103, according to the preset electromagnetic simulation software, the initial target body mesh file is parsed, and the requirement information of the preset electromagnetic simulation software is extracted to obtain the key process file.

[0078] In this embodiment, in order to obtain the geometric information and grid information of the initial target body, such as Figure 2 As shown in the figure, the precise geometric model of the initial target body is constructed and Figure 2 The model is used as an example for implementation;

[0079] like Figure 3 As shown in the figure, the precise geometric model is non-conformally meshed, a finer mesh is used in the target area with rich geometric details, and a larger mesh is used in the area with relatively simple geometric structure, and the initial target body mesh file is generated. The initial target body mesh file is used as the optimization starting point and the optimization is performed step by step; the initial target body mesh file is parsed, and then the key process files suitable for the preset efficient electromagnetic simulation software are extracted, and the following are obtained: Figure 4 shown.

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

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

[0082] S2. Extract the initial target body's contour function, obtain the target shape parameters of the geometric model, and determine the optimization range of the target shape parameters. The specific steps are as follows:

[0083] S201, classifying the initial target body into a rotating body target and a complex geometric body target, extracting the generatrix equation of the rotating body target, and calculating the first shape contour function through the generatrix equation;

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

[0085] S203 , integrating the first shape contour function and the second shape contour function to obtain the shape contour function of the target body and the target shape parameters of the geometric model, and determining the optimization interval of the target shape parameters.

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

[0087] Extract the contour function of the target body: According to the optimization target, the optimization target is divided into two target types for contour extraction. For the rotating body target, the key is to extract the generatrix equation, and use the generatrix equation to calculate the contour function; for complex geometric bodies with irregular shapes, such as helicopters, the surface parameterization technology 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 shape profile function, determine the optimization interval of each parameter in the shape profile function. The appropriate optimization interval directly affects the efficiency of the optimization and the practicality of the results.

[0089] S3. Use the preset full-wave electromagnetic simulation algorithm to read the key process files and calculate the radar cross section and the maximum change impact. The specific steps are as follows:

[0090] S301, setting simulation parameters according to a preset full-wave electromagnetic simulation algorithm, and obtaining a configuration file according to an optimization interval of parameters in a target shape profile function;

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

[0092] S303. Analyze the radar cross section by observing the change of the radar cross section of the target under each radar viewing angle, and determine the maximum impact of the shape change.

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

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

[0095] RCS calculation: Call the multi-layer fast multipole solver of the electromagnetic simulation software Zhongsuan to read the configuration files and key process files, directly calculate the RCS of the target body, retrieve the events required for mesh generation and mesh analysis, and use the multi-layer fast multipole solver to effectively handle large-scale problems to ensure the accuracy and efficiency of the simulation;

[0096] Data analysis: Based on the calculated RCS, the RCS is analyzed in detail by observing the RCS changes of the target under various radar viewing angles;

[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 target shape parameters of the geometric model, and in combination with the optimization interval of the target shape parameters, the shape contour function of the target body is optimized to obtain the optimized target shape parameters. The specific steps are as follows:

[0099] S401, setting basic parameters of the genetic algorithm, and generating an initial population of the genetic algorithm according to target shape parameters of the geometric model and the maximum shape change impact;

[0100] S402, using radar cross section as a fitness evaluation criterion;

[0101] S403, according to the fitness evaluation standard and in combination with the optimization interval of the target shape parameters, the initial population is optimized using genetic operations, the optimal solution of each generation of population is output, and 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 the genetic algorithm, the individuals in the initial population are scored using a fitness evaluation standard, and excellent individuals are selected using a preset selection method;

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

[0104] S4033. According to a preset probability, some parameters of the offspring individuals are randomly changed, and new characteristics are introduced to obtain an offspring population;

[0105] S4034, repeating steps S4031 to S4033, selecting the individual with the highest fitness score as the optimal solution for each generation;

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

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

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

[0109] The radar cross section of the target is used as the standard for fitness evaluation. In genetic algorithms, the fitness function is used to evaluate the quality 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 of population is output to complete the optimization of the shape contour function of the target body. The specific process is divided into the following steps:

[0111] Selection operation: According to the fitness scores of individuals in the population, excellent individuals are selected from the current population according to the preset selection method to provide "genes" for the next generation; the preset selection method usually adopts roulette and tournament selection methods;

[0112] Crossover operation: randomly pair the selected excellent individuals, and perform crossover operation on the parameters of the paired individuals, exchange information between the parameters of the two paired individuals to increase the diversity of the population and produce offspring individuals;

[0113] Mutation operation: Randomly change the parameters of the offspring individuals with a preset probability and introduce new features to prevent the algorithm from falling into a local optimal solution too early;

[0114] The individual with the highest fitness score is selected as the optimal solution in each generation;

[0115] After genetic operation is optimized, the optimal solution of each generation of population is output, and the optimal solution of each generation of 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. According to the nature and complexity of the optimization problem, any optimization algorithm including but not limited to deep learning and simulated annealing algorithm can be selected. 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. Update the target geometry using mesh projection according to the optimized target shape parameters. The specific steps are as follows:

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

[0119] S502, mapping the updated shape function to obtain an updated coordinate file by calculation;

[0120] S503: Combine the updated coordinate file and the key process file to obtain an updated geometric shape of the target body.

[0121] In this embodiment, grid projection is performed to update the geometric shape of the target body. According to the optimized target shape parameters, the coordinates of the points in the key process file are combined with the optimized target shape contour. Then, the coordinates of each point in the key process file are mapped according to the optimized target shape function, and an updated coordinate file is generated by calculating the corresponding position of each point coordinate 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 grid update process is simplified and the integrity of the grid structure is maintained.

[0122] S6, taking steps S3, S4 and S5 as an iterative loop, iteratively optimizing the contour function of the target body, judging whether the iterative loop meets the preset conditions, if so, stopping the iteration and outputting the iteration result, otherwise continuing the iteration, the specific steps are as follows:

[0123] S601, calculating the radar cross section, optimizing the target outline, and updating the target body geometry are used as an iterative loop to optimize the target body outline and record the fitness of the individual in this iteration;

[0124] S602, determining whether the iteration cycle has reached a preset number of iterations or whether the fitness of the target radar cross section has reached a preset iteration threshold, if so, stopping the iteration and executing step S603, otherwise continuing the iteration;

[0125] S603, according to 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;

[0126] S604: Define the population that has completed iteration as the optimal iteration population, and combine the optimal iteration population and the optimal target shape parameter to output the iteration result.

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

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

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

[0130] S7. Re-model and calculate the current distribution of the corresponding model according to the iteration results. Use the method of digging grooves 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 shape contour RCS. The specific steps are as follows:

[0131] S701, extracting an individual with the lowest radar cross section according to the optimal iterative population in the iterative result;

[0132] S702, reconstructing the geometric model using the optimal target shape parameters 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;

[0133] S703, according to the current distribution, the preliminary optimization model is processed by digging grooves or adding capacitive and inductive loads to obtain an optimized target geometric model;

[0134] S704, performing radar cross section analysis on the optimized target geometry model to complete rapid optimization of the target shape profile RCS.

[0135] In this embodiment, according to the optimal iteration population in the iteration result obtained by the cyclic iteration, the individual with the lowest RCS is extracted, and the geometric model is reconstructed using the optimal target shape parameters to obtain a preliminary optimization model. The preliminary optimization model is as follows: Figure 5 As shown in the figure, the RCS of the geometric model before and after the initial optimization is compared. The comparison results are shown in the figure. Figure 6 As shown, Theta represents the angle in the pitch plane, and dB represents decibel;

[0136] like Figure 7 As shown, the preliminary optimization model is verified by RCS simulation to ensure the optimization effect and output the current distribution of the model;

[0137] like Figure 8 As shown in FIG. 1 , the current distribution is reduced by digging grooves or adding capacitive and inductive loads in places where the current is large, thereby reducing the RCS of the target body, and the optimized target geometric model is obtained. The current distribution of the optimized target geometric model (i.e., the current distribution after digging grooves) is output as shown in FIG. Fig. 9 As shown;

[0138] like Fig.10 As shown in the figure, the RCS changes of the preliminary optimization model and the optimized target geometry model are analyzed in detail, and the effects are evaluated. The optimized target geometry model is used as the final optimization model to complete the rapid RCS optimization of the target shape contour.

Claims

1. A method for rapid optimization of target contour RCS based on grid projection, characterized in that: The following steps are involved: S1. Build the geometric model of the initial target body and obtain key process files; S2, extracting the shape contour function of the initial target body, obtaining the target shape parameters of the geometric model, and determining the optimization interval of the target shape parameters; S3. Use the preset full-wave electromagnetic simulation algorithm to read the key process files and calculate the radar cross section and the maximum change impact; S4, optimizing the target body's shape contour function according to the radar scattering cross section, the maximum change impact and the target shape parameters of the geometric model, and combining the optimization interval of the target shape parameters to obtain optimized target shape parameters; S5. Update the target body geometry using mesh projection according to the optimized target shape parameters; S6, taking steps S3, S4 and S5 as an iterative loop, iteratively optimizing the contour function of the target body, and judging whether the iterative loop meets the preset condition, if so, stopping the iteration and outputting the iteration result, otherwise continuing the iteration; S7. Re-model and calculate the current distribution of the corresponding model based on the iteration results. Use the method of digging grooves 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 shape contour RCS.

2. The method for rapid optimization of target contour RCS based on grid projection according to claim 1 is characterized in that: The S1 comprises the following steps: S101, constructing a geometric model of an initial target body; S102, performing non-conformal meshing on the geometric model to obtain an initial target volume mesh file; S103, according to the preset electromagnetic simulation software, the initial target body mesh file is parsed, and the requirement information of the preset electromagnetic simulation software is extracted to obtain the key process file.

3. The method for rapid optimization of target contour RCS based on grid projection according to claim 1 is characterized in that: The S2 comprises the following steps: S201, classifying the initial target body into a rotating body target and a complex geometric body target, extracting the generatrix equation of the rotating body target, and calculating the first shape contour function through the generatrix equation; S202, using surface parameterization technology to model the external contour of the complex geometric body, and extracting a second external contour function; S203 , integrating the first shape contour function and the second shape contour function to obtain the shape contour function of the target body and the target shape parameters of the geometric model, and determining the optimization interval of the target shape parameters.

4. The method for rapid optimization of target contour RCS based on grid projection according to claim 1 is characterized in that: The S3 comprises the following steps: S301, setting simulation parameters according to a preset full-wave electromagnetic simulation algorithm, and obtaining a configuration file according to an optimization interval of parameters in a target shape profile function; S302, using a preset full-wave electromagnetic simulation algorithm to read a configuration file and a key process file, calculate a radar cross section of the target body, and obtain a radar cross section; S303. Analyze the radar cross section by observing the change of the radar cross section of the target under each radar viewing angle, and determine the maximum impact of the shape change.

5. The method for rapid optimization of target contour RCS based on grid projection according to claim 4 is characterized in that: The S4 comprises the following steps: S401, setting basic parameters of the genetic algorithm, and generating an initial population of the genetic algorithm according to target shape parameters of the geometric model and the maximum shape change impact; S402, using radar cross section as a fitness evaluation criterion; S403, according to the fitness evaluation standard and in combination with the optimization interval of the target shape parameters, the initial population is optimized by using genetic operations, the optimal solution of each generation of population is output, and the optimization of the shape contour function of the target body is completed; S404. Obtain optimized target shape parameters according to the optimal solution of each generation population.

6. The method for rapid optimization of target contour RCS based on grid projection according to claim 5 is characterized in that: The S403 comprises the following steps: S4031. According to the initial population of the genetic algorithm, the individuals in the initial population are scored using a fitness evaluation standard, and excellent individuals are selected using a preset selection method; S4032, randomly pairing the selected excellent individuals, and performing a crossover operation on the parameters of the excellent individuals to generate offspring individuals; S4033. According to a preset probability, some parameters of the offspring individuals are randomly changed, and new characteristics are introduced to obtain an offspring population; S4034, repeating steps S4031 to S4033, selecting the individual with the highest fitness score as the optimal solution for each generation; S4035. In response to the termination of the genetic operation, the optimal solution of each generation of the population is output.

7. The method for rapid optimization of target contour RCS based on grid projection according to claim 2 is characterized in that: The S5 comprises the following steps: S501, combining the optimized target shape parameters with the geometric model of the initial target body to obtain an updated shape function; S502, mapping the updated shape function to obtain an updated coordinate file by calculation; S503: Combine the updated coordinate file and the key process file to obtain an updated geometric shape of the target body.

8. The method for rapid optimization of target contour RCS based on grid projection according to claim 1 is characterized in that: The S6 comprises the following steps: S601, calculating the radar cross section, optimizing the target outline, and updating the target body geometry are used as an iterative loop to optimize the target body outline and record the fitness of the individual in this iteration; S602, determining whether the iteration cycle has reached a preset number of iterations or whether the fitness of the target radar cross section has reached a preset iteration threshold, if so, stopping the iteration and executing step S603, otherwise continuing the iteration; S603, according to 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 iteration as the optimal iteration population, and combine the optimal iteration population and the optimal target shape parameter to output the iteration result.

9. The method for rapid optimization of target contour RCS based on grid projection according to claim 8, characterized in that: The S7 comprises the following steps: S701, extracting an individual with the lowest radar cross section according to the optimal iterative population in the iterative result; S702, reconstructing the geometric model using the optimal target shape parameters 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; S703, according to the current distribution, the preliminary optimization model is processed by digging grooves or adding capacitive and inductive loads to obtain an optimized target geometric model; S704, performing radar cross section analysis on the optimized target geometry model to complete rapid optimization of the target shape profile RCS.

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