Optimization of Cooperative Countermeasure Strategy for Underwater Acoustic Deception Devices Based on Adaptive Mutation PSO
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摘要: 针对水面舰船防御水下制导装置时, 多枚水声诱骗装置协同对抗体系研究不足, 且传统穷举法存在效率低、可移植性差的问题, 文中引入粒子群优化算法对对抗模型进行优化, 并通过添加自适应惯性权重与多半径变异机制改进该算法; 同时, 构建以防御成功率、最小交战距离及舰船存活时间为核心指标的多目标优化函数, 优化舰船规避航向以及2枚助飞式水声诱骗装置的发射距离与角度。仿真结果表明, 所提改进算法相较传统算法具备更高效率、更快收敛速度与更优适应度, 同时揭示了不同舷角态势下最优对抗策略的差异性及其战术价值, 为水下制导装置防御策略制定提供重要参考。Abstract: To address the insufficient research on the cooperative combat system of multiple underwater acoustic deception devices when surface ships defend against underwater guidance devices, as well as the problems of low efficiency and poor portability of traditional exhaustive methods, this paper introduced the particle swarm optimization(PSO) algorithm to optimize the countermeasure model and improved the algorithm by introducing an adaptive inertia weight and a multi-radius mutation mechanism. Meanwhile, the paper established a multi-objective optimization function with core indicators including defense success rate, minimum engagement distance, and ship survival time to optimize the ship’s evasive course, as well as the launch distance and angle of two flying-aided underwater acoustic deception devices. Simulation results show that the proposed improved algorithm has higher efficiency, faster convergence speed, and better fitness compared with traditional algorithms. It also reveals the differences in optimal countermeasure strategies under different bearing angle situations and their tactical values, providing an important reference for the formulation of defense strategies against underwater guidance devices.
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图 3 水声诱骗装置参数多半径变异示意图
Figure 3. Schematic diagram of multi-radius parameter variation of underwater acoustic deception device
图 4 水下制导装置来袭舷角划分示意图
Figure 4. Schematic diagram of incoming bearing angle division for underwater guidance devices
图 6 大舷角态势下粒子群优化算法迭代曲线
Figure 6. Iterative curve of particle swarm optimization algorithm under large bearing angle situation
图 7 大舷角态势下100次蒙特卡洛验证结果图
Figure 7. Results of 100 times Monte Carlo verifications under large bearing angle situation
图 9 中等舷角态势下粒子群优化算法迭代曲线
Figure 9. Iteration curve of particle swarm optimization algorithm under medium bearing angle situation
图 10 中等舷角态势下100次蒙特卡洛验证结果图
Figure 10. Results of 100 times Monte Carlo verifications under medium bearing angle situation
图 12 小舷角态势下粒子群优化算法迭代曲线
Figure 12. Iterative curve of particle swarm optimization algorithm under small bearing angle situation
图 13 小舷角态势下100次蒙特卡洛验证结果图
Figure 13. Results of 100 times Monte Carlo verifications under small bearing angle situation
表 1 对抗策略优化参数及范围
Table 1. Optimization parameters and for ranges countermeasure strategy
优化对象 优化参数 参数范围 舰船 舰船规避航向/(°) (0, 180) 诱骗装置1 第1枚诱骗装置发射距离/m (600, 2 000) 诱骗装置1 第1枚诱骗装置发射舷角/(°) (0, 180) 诱骗装置2 第2枚诱骗装置发射距离/m (600, 2 000) 诱骗装置2 第2枚诱骗装置发射舷角/(°) (0, 180) 
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表 2 大舷角态势优化前后策略参数
Table 2. Strategy parameters before and after optimization under large bearing angel situation
策略参数 数值 优化前 优化后 舰船规避航向/(°) 97.22 64.84 第1枚诱骗装置释放距离/m 1 919.1 1 211.6 第1枚诱骗装置释放方位/(°) −163 −147 第2枚诱骗装置释放距离/m 1 517.9 1 276.7 第2枚诱骗装置释放方位/(°) −153 −72
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表 3 中等舷角态势优化前后策略参数
Table 3. Strategy parameters before and after optimization under medium bearing angle situation
策略参数 数值 优化前 优化后 舰艇规避航向/(°) 95.2 70.8 第1枚诱骗装置释放距离/m 1 998.4 1 687.1 第1枚诱骗装置释放方位/(°) −85.6 −53.1 第2枚诱骗装置释放距离/m 1 494.9 2 157.6 第2枚诱骗装置释放方位/(°) −75.7 −28.9
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表 4 小舷角态势优化前后策略参数
Table 4. Strategy parameters before and after optimization under small bearing angle situation
策略参数 优化前 优化后 舰船规避航向/(°) 117.74 123.71 第1枚诱骗装置释放距离/m 1 936.3 1 636.2 第1枚诱骗装置释放方位/(°) −18.8 −36.2 第2枚诱骗装置释放距离/m 1 436.7 1 429.2 第2枚诱骗装置释放方位/(°) −8.6 −5.5
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