Transparent transmission metasurface system based on game optimization algorithm

Abstract

The invention discloses a transparent transmission metasurface system based on a game optimization algorithm, and relates to the technical field of electromagnetic metasurfaces and radar electronic warfare. Comprising a multi-target game optimization module and a double-station radar deception module which are in signal connection. The double-station radar deception module comprises a metasurface array and a double-station radar verification module and is used for generating and verifying a plurality of scattering signals; and the multi-target game optimization module is used for forming an optimal metasurface array. According to the invention, the whole domain of the Ku wave band can be covered, the problems of large-angle failure, feed source shielding and the like of a reflective metasurface are solved, and real-time strategy updating in a battlefield environment is supported.

Description

Technical Field

[0001] The present invention relates to the field of electromagnetic metasurfaces and radar electronic warfare technology, and in particular to a transparent transmission metasurface system based on a game optimization algorithm. Background Art

[0002] With the popularization of dual / multi-base radar systems and MIMO radar technology, modern battlefield targets face the threat of multi-band fusion detection and multi-angle coordinated tracking. Traditional stealth technology mainly relies on ferrite absorbing materials or reflective metasurface design, but it has significant drawbacks: 1. Bandwidth limitation: The widely used classic solutions mainly use absorbing materials. Their effective bandwidth is usually limited to a narrow band and cannot meet the stealth requirements of wide-band or even cross-band radars. 2. Angular sensitivity: Traditionally used reflective metasurface solutions achieve RCS reduction through backscatter interference, but their deception effectiveness tends to drop sharply when the bistatic angle exceeds 60°, and the RCS similarity is greatly reduced; 3. Low optimization efficiency: Conventional solutions using traditional genetic algorithms (GA) to optimize metasurface arrays take more than 72 hours and often produce a high proportion of local optimal solutions, making it difficult to meet real-time deployment requirements in dynamic battlefield environments.

[0003] Furthermore, existing technologies generally ignore optical stealth compatibility. For example, the EARS electronic attack system relies on active jamming signals, which can temporarily disrupt radar detection, but its high power consumption and susceptibility to spectrum sensing countermeasures limit its practical value.

[0004] Therefore, how to provide a transparent transmissive metasurface system based on a game optimization algorithm to solve the difficulties existing in the existing technology is an urgent problem that those skilled in the art need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a transparent transmissive metasurface system based on a game optimization algorithm, which can cover the entire Ku band, overcome the problems of large-angle failure and feed shielding of reflective metasurfaces, and support real-time strategy updates in battlefield environments.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A transparent transmissive metasurface system based on a game optimization algorithm, including 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.

[0007] Optionally, the metasurface array is composed of N×N transparent transmission metasurface units arranged in an array. 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.

[0008] Optionally, 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 vector network analyzer is used to analyze the RCS similarity.

[0009] Optional, 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.

[0010] Optional, 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.

[0011] Optionally, the objective function expression is: F=α·Farfild_cal|f 1 +β·Farfild_cal|f 2 +γ·Farfild_cal|f 3 , in, α 、 β 、 γ For different frequencies f 1 、 f 2 、 f 3 The weight coefficient of Farfild_cal|f i Represented as a metasurface array in fi The far-field pattern calculated at the frequency, f 1 、 f 2 、 f 3 They represent representative target frequency points selected within a wide frequency band.

[0012] Through the above technical solution, it can be seen that compared with the existing technology, the present invention provides a transparent transmissive metasurface system based on a game optimization algorithm, which has the following beneficial effects: 1) The effective working bandwidth of the present invention is extended to 6 GHz (12~18GHz), covering the entire Ku band, and has wide-band compatibility; 2) The dual-station angle of the present invention maintains an RCS similarity of 98.6% in the range of 0~180°, overcoming the problems of large-angle failure and feed source shielding of reflective metasurfaces, and has full-angle robustness; 3) The visible light transmittance of the present invention is >87%, and it can be integrated into key parts that require optical transparency, such as fighter cockpit covers and ship portholes, and has optical-radar stealth compatibility; 4) The optimization algorithm of the present invention converges extremely fast, supporting real-time strategy updates in battlefield environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0014] Figure 1 This is a block diagram of a transparent transmissive metasurface system based on a game optimization algorithm disclosed in the present invention; Figure 2 A top view of the transparent transmissive metasurface unit disclosed in the present invention; Figure 3 A top view of the metasurface array disclosed in the present invention; Figure 4 This is a schematic diagram of the multi-objective game optimization principle disclosed in the present invention; Figure 5a A comparison chart of the test results of a dual-station radar using an optimized unit array and a metal tank model at 12 Hz disclosed in an embodiment of the present invention; Figure 5b This is a comparison chart of the test results of the dual-station radar at 15 Hz using the optimized unit array disclosed in an embodiment of the present invention and a metal tank model; Figure 5c This is a comparison chart of the dual-station radar test results of the optimized unit array and the metal tank model at 18 Hz disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0016] Reference Figure 1 As shown, the present invention discloses a transparent transmission metasurface system based on a game optimization algorithm, including a signal-connected multi-objective game optimization module 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.

[0017] Further, refer to Figure 2 and Figure 3 As shown, the metasurface array is composed of N×N transparent transmission 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.

[0018] Specifically, the metal grid square resistance is set to ≤2 Ω / sq, the thickness of the transparent PC substrate is 0.2 mm, and by adjusting the length of the short line, the unit achieves 0~360° transmission phase linear coverage in the Ku band (12~18 GHz).

[0019] Furthermore, 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.

[0020] Specifically, the metasurface array induces omnidirectional RCS signatures of false targets within the bistatic angle range of 0°-180°.

[0021] Further, refer to Figure 4 As shown in Figure 2, the multi-objective game optimization module includes: Establish an initial multi-player non-cooperative game model: In multi-objective game optimization, each unit in the metasurface array is considered an independent player. 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 used 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. Game model setting: Set N×N metasurface units and let each unit choose the amplitude and phase state k i (Amplitude A i , phase θ i ), which represents the phase state θ i Varying between 0 and 360 degrees, the choice of each unit will affect the far-field pattern of the entire system, thus affecting the deception effect; Strategy Space: For each metasurface unit's phase selection, a corresponding strategy space is constructed, i.e., the set of phase selections for each unit. Each player's strategy will affect the choices of other players (units), thus forming a non-cooperative game model.

[0022] The initial multi-player non-cooperative game model is solved using the NSGA-II framework to generate a Pareto-optimal solution set. To solve this non-cooperative game model, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) framework is employed to optimize multiple objectives. In NSGA-II, the objective function is optimized in parallel using a multi-objective optimization algorithm to generate multiple Pareto-optimal solutions that are not dominated by other solutions, meaning no single solution outperforms the others on all objectives.

[0023] In an embodiment, the objective function expression is: F=α·Farfild_cal|f 1 +β·Farfild_cal|f 2 +γ·Farfild_cal|f 3 , in, α 、 β 、 γ The objective function weights are for different frequency points f 1 、 f 2 、 f 3 The weight coefficient of Farfild_ cal|f i It is represented by the far-field pattern of the metasurface array calculated at these frequencies, f 1 、 f 2 、 f 3These are respectively representative target frequencies selected within a wide frequency band (12 GHz, 15 GHz, and 18 GHz in this implementation) to maximize the broadband fit of the deception effect. This objective function is used to maximize the system's RCS similarity and far-field pattern fit. Therefore, NSGA-II can simultaneously optimize these multiple objectives to generate a set of Pareto optimal solutions.

[0024] The steps of NSGA-II are as follows: (1) Initialization of the population: Generate a population with random phase configurations; (2) Fitness evaluation: Calculate the fitness of each solution according to the above objective function; (3) Selection and crossover: Generate a new generation of population based on selection, crossover and mutation operations. The optimization process continues until the termination condition is met.

[0025] A Pareto-optimal solution set is found using a particle swarm-simulated annealing algorithm. Based on this Pareto-optimal solution set, a particle swarm optimization (PSO) algorithm combined with simulated annealing (SA) is used to further solve the problem, maximizing the objective function. PSO can quickly find the global optimal solution, while SA can avoid being trapped in local optimal solutions. The combination of PSO and SA first finds the global optimal solution through swarm collaboration, based on particle search and updates in the solution space. SA then gradually controls the temperature, increasing the probability that the system will escape the local optimal solution and explore a wider range of solutions.

[0026] 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.

[0027] 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.

[0028] 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. Figure 5a-5c 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°.

[0029] 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.

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 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.

5. The transparent transmissive metasurface system based on the game optimization algorithm according to claim 4, 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.

6. The transparent transmissive metasurface system based on the game optimization algorithm according to claim 4, characterized in that: The objective function expression is: F=α·Farfild_cal|f 1 +β·Farfild_cal|f 2 +γ·Farfild_cal|f 3 ， in, α 、 β 、 γ For different frequencies f 1 、 f 2 、 f 3 The weight coefficient of Farfild_cal|f i Represented as a metasurface array in f i The far-field pattern calculated at the frequency, f 1 、 f 2 、 f 3 They represent representative target frequency points selected within a wide frequency band.

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