针对无人潜航器的反潜策略研究

Research on anti-submarine strategy for unmanned undersea vehicles

近年来无人潜航器对国家海洋国土安全带来的威胁逐渐增大,其低噪声特性和隐蔽入侵方式也给反潜行动带来极大困难。为此,提出了一种两阶段规划算法,用以学习优化反潜策略,在部署阶段,建立了基于不确定性马尔可夫决策过程的反潜资源分配模型,并设计了鲁棒性部署策略强化学习算法,用以求解不确定条件下分配模型的纳什均衡解。在搜索阶段,建立了基于部分可观察马尔可夫决策过程的搜潜模型,并设计了基于多智能体强化学习的搜潜策略学习算法。最后,通过仿真实验验证了本算法与比对算法相比具有更高的性能。

In recent years,the threat of unmanned underwater vehicles(UUV)to national sea security has gradually increased.At the same,it is difficult to detect UUVs for its low noise and team intrusion.A two stage anti-submarine planning method is proposed to learn the optimal anti-submarine strategy.During the deployment stage,a resource allocation model based on uncertain Markov decision process(MDP)is proposed,whose Nash equilibrium point is solved by the elaborately designed robust reinforcement learning algorithm of the deployment strategy.In the search stage,a search model based on partially observable Markov decision process(POMDP)is proposed which is solved by the search strategy learning algorithm based on multi-agent reinforcement learning(MARL).Simulation results show that the proposed algorithm outperforms other algorithms.