网格化目标约束性隐身外形优化设计

      Objective-constrained optimization design of stealth shapes based on grid-based modeling

      • 摘要: 为了解决在约束性条件下目标外形隐身设计问题,提出了一种基于网格化建模的目标外形优化方案。通过将目标表面离散为精细的网格,针对约束性条件求解出各个网格节点优化范围,并利用矩量法和遗传算法进行散射场计算和优化,通过算法迭代后可以得到优化后的模型结构。优化结果表明,本方法在给定的约束上下界内,可使文中两目标雷达散射截面平均减小超过12 dBsm。该方法能更为精细地调整目标结构,在严格的约束性条件下发挥重要作用,为目标隐身外形优化设计提供了一种新的方案。

         

        Abstract: In order to solve the problem of covert target shape design under constraint conditions, a target shape optimization scheme based on mesh modelling is proposed. By discretizing the target surface into fine meshes, the optimization domain of each mesh node is solved for the constraints, and the scattering field is calculated and optimized using the method of moments and genetic algorithm, and the optimized model structure can be obtained after iteration of the algorithm. The optimization results show that the method reduces the radar scattering cross section of both targets by more than 12 dBsm on average within the given constraints and upper bounds, which can fine-tune the target structure and play an important role in the strict constraints, and provides a new scheme for the optimal design of the stealthy target shape.

         

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