A transparent transmissive metasurface system based on game optimization algorithm
By optimizing the transparent transmissive metasurface system through a game optimization algorithm, the problems of wide-band radar stealth and optical compatibility are solved, all-angle RCS similarity and high transmittance are achieved, and real-time updates in battlefield environments are supported.
Patent Information
- Application Number
- CN202511020531.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-24
- Publication Date
- 2025-10-17
- Estimated Expiration
- 2045-07-24
AI Technical Summary
Existing technologies cannot meet the stealth requirements of wide-band radar. Traditional stealth materials and metasurface solutions have frequency band limitations, angle sensitivity, and low optimization efficiency, and ignore optical stealth compatibility.
A transparent transmissive metasurface system based on a game optimization algorithm is adopted, including a multi-objective game optimization module and a dual-station radar deception module. The metasurface array is optimized by NSGA-II and particle swarm-simulated annealing algorithm to form all-angle robustness and optical-radar compatibility.
It achieves full Ku-band coverage, maintains an RCS similarity of 98.6%, and a transmittance of >87%. It supports real-time strategy updates and is suitable for optically transparent areas such as fighter cockpit covers and ship portholes.
Smart Images
Figure CN120522652B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of electromagnetic metasurface and radar electronic warfare technology, and particularly relates to a transparent transmission metasurface system based on a game optimization algorithm. BACKGROUND
[0002] With the popularization of dual / multi-base station radar systems and MIMO radar technology, modern battlefield targets are threatened by multi-band fusion detection and multi-angle cooperative tracking. Traditional stealth technology mainly relies on ferrite wave-absorbing materials or reflective metasurfaces, but it has significant defects:
[0003] 1. Band limitation: the existing widely used classic scheme mainly uses wave-absorbing materials, and the effective bandwidth is usually limited to a narrow band, which cannot cover the stealth requirements of wide-band or even cross-band radars;
[0004] 2. Angle sensitivity: the traditional reflective metasurface scheme realizes RCS reduction through backscattering interference, but the deception effect decreases sharply when the bistatic angle exceeds 60°, and the RCS similarity is greatly reduced;
[0005] 3. Low optimization efficiency: when the conventional scheme uses the traditional genetic algorithm (GA) to optimize the metasurface array, the time consumption exceeds 72 hours and the local optimal solution accounts for a high proportion, which is difficult to meet the real-time deployment requirements in dynamic battlefield environments.
[0006] In addition, the existing technology generally ignores optical stealth compatibility. For example, the EARS electronic attack system relies on active jamming signals, which can temporarily interfere with radar detection, but its high power consumption and vulnerability to frequency spectrum sensing countermeasures limit its practical value.
[0007] Therefore, how to provide a transparent transmission metasurface system based on a game optimization algorithm to solve the difficulties existing in the prior art is a problem that needs to be solved by those skilled in the art. SUMMARY
[0008] Therefore, the present application provides a transparent transmission metasurface system based on a game optimization algorithm, which can cover the Ku band domain, overcome the problems of large-angle failure and feed shielding of reflective metasurfaces, and support real-time strategy updating in battlefield environments.
[0009] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:
[0010] A transparent transmission metasurface system based on a game optimization algorithm, comprising a multi-target game optimization module and a bistatic radar deception module connected by a signal;
[0011] The bistatic radar deception module comprises a metasurface array and a bistatic radar verification module, for generating and verifying multiple scattering signals;
[0012] The multi-objective game optimization module is used to form an optimal metasurface array.
[0013] Optionally, the metasurface array is arranged by N×N transparent transmission metasurface units,
[0014] The transparent transmission metasurface unit comprises a transparent PC substrate and a metal mesh conductive layer, and the metal mesh conductive layer is disposed on the upper part of the transparent PC substrate to form a stub-square composite structure.
[0015] Optionally, the bistatic radar verification module is a microwave anechoic chamber test platform, which comprises a vector network analyzer, a movable turntable and a metal tank model, the metal tank model and the metasurface array are placed on the movable turntable, and the vector network analyzer is used to analyze the RCS similarity.
[0016] Optionally, the multi-objective game optimization module comprises:
[0017] An initial multi-player non-cooperative game model is established, and each unit is selected as an independent player to select a phase state;
[0018] The initial multi-player non-cooperative game model is solved based on the NSGA-II framework to generate a Pareto optimal solution set;
[0019] The Pareto optimal solution set is solved based on a particle swarm-simulated annealing algorithm, and a target model is obtained by maximizing the objective function.
[0020] Optionally, the particle swarm-simulated annealing algorithm comprises:
[0021] Initialize the particle swarm: set the initial position and speed for each particle;
[0022] Fitness calculation: calculate the fitness value of each particle according to the objective function, i.e. the performance of the metasurface array;
[0023] Update the particle position: use the velocity update rule of the particle swarm to adjust the search direction of the particle;
[0024] Simulated annealing: through the temperature control mechanism of simulated annealing, random disturbance is introduced in the particle swarm search process to jump out of the local optimal solution;
[0025] Iterative convergence: with the iteration of the algorithm, the particles gradually approach the global optimal solution, and the metasurface array design is obtained.
[0026] Optionally, the objective function expression is:
[0027] F = a • Farfild cal | f + b • Farfild cal | f + g • Farfild cal | f 1 F = a • Farfild cal | f + b • Farfild cal | f + g • Farfild cal | f 2 F = a • Farfild cal | f + b • Farfild cal | f + g • Farfild cal | f 3 ,
[0028] wherein, α , β , F = a • Farfild cal | f + b • Farfild cal | f + g • Farfild cal | f are the weight coefficients for different frequency points f 1 , f 2 , f 3 , Figure 1 i denotes the far-field pattern calculated by the metasurface array at f i frequency, f 1 , f 2 , f 3 denote the representative target frequency points selected in the wideband range, respectively.
[0029] Via the technical solution described above, compared with the prior art, the present application provides a transparent transmission metasurface system based on a game optimization algorithm, which has the following beneficial effects: 1) the effective working bandwidth of the present application is expanded to 6 GHz (12~18 GHz), covering the entire Ku band, and having wideband compatibility; 2) the dual-station angle of the present application maintains 98.6% of the RCS similarity in the range of 0~180°, overcoming the problems of large-angle failure and feed shielding of reflective metasurfaces, and having full-angle robustness; 3) the present application has a visible light transmittance of >87%, and can be integrated into key parts such as fighter cockpit covers and ship portholes that require optical transparency, and has optical-radar stealth compatibility; 4) the optimization algorithm of the present application has extremely fast convergence speed, and supports real-time strategy update in battlefield environment. BRIEF DESCRIPTION OF DRAWINGS
[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.
[0031] Figure 2 is a block diagram of the transparent transmission metasurface system based on a game optimization algorithm disclosed by the present application;
[0032] Figure 3 is a top view of the transparent transmission metasurface unit disclosed by the present application;
[0033] Figure 4 is a top view of the metasurface array disclosed by the present application;
[0034] Figure 5a A multi-target game optimization schematic diagram disclosed by the present application;
[0035] Figure 5b A bistatic radar test result comparison diagram of an optimized unit array and a metal tank model at 12Hz disclosed by an embodiment of the present application;
[0036] Figure 5c A bistatic radar test result comparison diagram of an optimized unit array and a metal tank model at 15Hz disclosed by an embodiment of the present application;
[0037] Figure 1 A bistatic radar test result comparison diagram of an optimized unit array and a metal tank model at 18Hz disclosed by an embodiment of the present application. DETAILED DESCRIPTION
[0038] The technical solutions in the embodiments of the present application will be apparently and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.
[0039] Referring to Figure 2 , the present application discloses a transparent transmission metasurface system based on a game optimization algorithm, comprising a multi-target game optimization module and a bistatic radar deception module connected by signals;
[0040] The bistatic radar deception module comprises a metasurface array and a bistatic radar verification module, for generating and verifying multiple scattering signals.
[0041] The multi-target game optimization module is used to form an optimal metasurface array.
[0042] Further, referring to Figure 3 and Figure 4 , the metasurface array is arranged by N×N transparent transmission metasurface units,
[0043] The transparent transmission metasurface unit comprises a transparent PC substrate and a metal mesh conductive layer, and the metal mesh conductive layer is disposed on the upper part of the transparent PC substrate to form a stub-square composite structure.
[0044] Specifically, the metal mesh square resistance is set to be less than or equal to 2 Ω / sq, the transparent PC substrate has a thickness of 0.2 mm, and by adjusting the stub length, the unit realizes 0~360° transmission phase linear coverage in the Ku band (12~18 GHz).
[0045] Further, the bistatic radar verification module is a microwave anechoic chamber test platform, including a vector network analyzer, a movable turntable, and a metal tank model. The metal tank model and the metasurface array are placed on the movable turntable, and the vector network analyzer is used to analyze the RCS similarity.
[0046] Specifically, the metasurface array induces an omnidirectional RCS characteristic of the false target within a bistatic angle range of 0°-180°.
[0047] Further, referring to F = a • Farfild cal | f + b • Farfild cal | f + g • Farfild cal | f As shown in the figure, the multi-target game optimization module includes:
[0048] Establishing an initial multi-player non-cooperative game model: in the multi-target game optimization, each unit in the metasurface array is regarded as an independent player, and each player (i.e., unit) needs to select an optimal phase state to achieve the optimization goal of the entire system. The phase selection of each unit is regarded as a decision variable, and the goal is to maximize the overall performance of the system, covering multiple indicators such as RCS similarity and broadband adaptability;
[0049] Game model setting: N×N metasurface units are set, and each unit selects an amplitude and phase state k i (amplitude A i , phase F = a • Farfild cal | f + b • Farfild cal | f + g • Farfild cal | f i ), which represents the phase state F = a • Farfild cal | f + b • Farfild cal | f + g • Farfild cal | f i changes between 0 and 360 degrees, and the selection of each unit affects the far-field pattern of the entire system, thereby affecting the deception effect;
[0050] Strategy space: for the phase selection of each metasurface unit, a corresponding strategy space is constructed, i.e., the phase selection set of each unit. The strategy of each player will affect the selection of other players (units), so it is a non-cooperative game model.
[0051] Solving the initial multi-player non-cooperative game model based on the NSGA-II framework to generate a Pareto optimal solution set; in order to solve this non-cooperative game model, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) framework is used to optimize multiple objectives. In NSGA-II, the objective functions are optimized in parallel by a multi-objective optimization algorithm to generate multiple Pareto optimal solutions, which are not dominated by other solutions, i.e., no solution is better than other solutions in all objectives.
[0052] In an embodiment, the objective function expression is:
[0053] F = a • Farfild cal | f 1 2 3 ,
[0054] wherein, α , β , are the target function weights respectively for different frequency points f 1 , f 2 , f 3 are the weight coefficients, i denote the calculated far-field patterns of the metasurface array at these frequency points, f 1 , f 2 , f 3 denote the representative target frequency points (12 GHz, 15 GHz, 18 GHz respectively in this embodiment) selected within the wideband range to maximize the wideband fitting of the deception effect. The target function is used to maximize the RCS similarity and the fitting of the far-field patterns of the system, so that these multi-objectives can be simultaneously optimized by NSGA-II to generate a set of Pareto optimal solutions.
[0055] The NSGA-II steps are as follows: (1) initialize the population: generate a population of random phase configurations; (2) fitness evaluation: calculate the fitness of each solution according to the above target function. (3) selection and crossover: generate a new generation of population based on selection, crossover and mutation operations, and the optimization process continues until the termination condition is met.
[0056] The particle swarm-simulated annealing algorithm is used to solve the Pareto optimal solution set; based on the generated Pareto optimal solution set, the algorithm combining particle swarm optimization (PSO) and simulated annealing (SA) is used for further solution, so that the target function reaches the maximum. PSO can quickly find the global optimal solution, while SA can avoid falling into local optimal solution. The combination of PSO and SA can first search and update the particles in the solution space based on the cooperation of the group to find the global optimal solution, and further control the temperature to gradually decrease through SA, so that the system has a higher probability to jump out of the local optimal solution and explore a wider solution space.
[0057] The PSO-SA steps are as follows: (1) Initialize the particle swarm: Set the initial position (i.e., the initial phase configuration of the metasurface unit) and velocity for each particle. (2) Fitness calculation: Calculate the fitness value of each particle based on the objective function, i.e., the performance of the metasurface array. (3) Update particle positions: Use the velocity update rule of PSO to adjust the search direction of the particles. (4) Simulated annealing: Through the temperature control mechanism of simulated annealing, random perturbations are introduced during the particle swarm search process to escape the local optimal solution. (5) Iterative convergence: As the algorithm iterates, the particles gradually approach the global optimal solution, and ultimately obtain the metasurface array design with the best performance.
[0058] Through the above optimization steps, the optimal metasurface array phase configuration can be obtained. Ultimately, the resulting metasurface array can effectively cover the entire selected broadband frequency band, maintaining high RCS similarity across a wide range of dual-station angles, meeting the deception requirements of electronic warfare. Furthermore, the optimized system can implement real-time strategy updates, supporting flexible applications in battlefield environments.
[0059] In a specific embodiment, a transparent PC board with a thickness of 0.2 mm, a dielectric constant of 3, and a loss tangent of 0.012 is selected as a substrate, and a metal grid is deployed on the surface. The grid uses nickel-copper nanowires with a square resistance of 1.8 Ω / sq and a line width of 250 μm. The short-stub-square composite pattern unit period is set to 6 mm, the short-stub length adjustment step is 0.1 mm, and the objective function weights α=0.4 (12 GHz), β=0.3 (15 GHz), and γ=0.3 (18 GHz) are set. The initial particle number of the PSO-SA algorithm is set to 50, the annealing starting temperature is 1000°C, and the cooling rate is 0.95 to obtain an optimized 24×24 unit array; the optimized unit array with a total size of 17 cm×17 cm and the surface of the metal tank model are placed on a turntable for a dual-station RCS test experiment, as shown in FIG. As shown, the results show that the RCS similarity is: 98.6% (12 GHz), 95.2% (15 GHz) and 97.7% (18 GHz); the angular adaptability is: the similarity standard deviation is <4.8% in the bistatic angle range of 0°–180°.
[0060] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A transparent transmissive metasurface system based on a game optimization algorithm, characterized in that: Includes a multi-objective game optimization module for signal connection and a dual-station radar deception module; The bistatic radar deception module includes a metasurface array and a bistatic radar verification module, which are used to generate multiple scattered signals and perform verification; The multi-objective game optimization module is used to form the optimal metasurface array; The multi-objective game optimization module includes: Establish an initial multi-player non-cooperative game model, allowing each unit to select a phase state as an independent player; Solve the initial multi-player non-cooperative game model based on the NSGA-II framework to generate the Pareto optimal solution set; The Pareto optimal solution set is solved based on the particle swarm-simulated annealing algorithm, and the target model is obtained by maximizing the objective function.
2. The transparent transmissive metasurface system based on the game optimization algorithm according to claim 1, characterized in that: The metasurface array is composed of N×N transparent transmissive metasurface units. The transparent transmissive metasurface unit includes a transparent PC substrate and a metal grid conductive layer. The metal grid conductive layer is deployed on the transparent PC substrate to form a short-line-square composite structure.
3. The transparent transmissive metasurface system based on the game optimization algorithm according to claim 1, characterized in that: The dual-station radar verification module is a microwave anechoic chamber test platform, which includes a vector network analyzer, a movable turntable and a metal tank model. The metal tank model and the metasurface array are placed on the movable turntable, and the RCS similarity is analyzed using a vector network analyzer.
4. The transparent transmissive metasurface system based on the game optimization algorithm according to claim 1, characterized in that: The particle swarm-simulated annealing algorithm includes: Initialize the particle swarm: set the initial position and velocity for each particle; Fitness calculation: Calculate the fitness value of each particle according to the objective function, that is, the performance of the metasurface array; Update particle position: Use the particle swarm velocity update rule to adjust the particle search direction; Simulated annealing: Through the temperature control mechanism of simulated annealing, random perturbations are introduced into the particle swarm search process to escape from the local optimal solution; Iterative convergence: As the algorithm iterates, the particles gradually approach the global optimal solution, and the metasurface array design is obtained.
5. The transparent transmissive metasurface system based on the game optimization algorithm according to claim 1, characterized in that: The objective function expression is: F=α·Farfild_cal|f1+β·Farfild_cal|f2+γ·Farfild_cal|f3, Among them, α, β, and γ are weight coefficients for different frequency points f1, f2, and f3, respectively. i Expressed as the metasurface array at f i The far-field radiation patterns calculated at the frequencies f1, f2, and f3 are representative target frequencies selected within a wide frequency band.