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首页> 外文期刊>IEEE sensors journal >Deep Reinforcement Learning-Based Radar Network Target Assignment
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Deep Reinforcement Learning-Based Radar Network Target Assignment

机译：基于深度加强学习的雷达网络目标分配

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摘要

This study focuses on the problem of target assignment when a phased-array radar network detects hypersonic-glide vehicles in near-space and proposes a method for target assignment based on deep reinforcement learning. The state, action, and reward functions of the agent and the structure of the deep Q network are designed. To solve the problem of the large scale of the agent's action space, an incremental search method is proposed. The proposed incremental search method reduces the scale of the search space by at least 16 orders of magnitude. To improve the agent's solution quality during a search, an agent soft search constraint is designed. Setting the soft search constraint filters out the obviously inferior solutions, thereby improving the solution quality. The performance of the proposed method is verified in two scenarios: a multitarget attack and a saturation attack. The simulation results showed that the performance of the method proposed in this paper is significantly better than those achieved by the heuristic algorithm and the random assignment method in terms of the number of target assignments, the target threat degree, the number of radar switches and the cumulative detection duration.
机译：该研究侧重于当分阶段阵列雷达网络检测到近空间中的超音速滑动车辆时的目标分配问题，并提出了一种基于深度加强学习的目标分配方法。设计了代理的状态，动作和奖励功能和深Q网络的结构。为了解决代理的动作空间的大规模的问题，提出了一种增量搜索方法。所提出的增量搜索方法将搜索空间的比例降低了至少16个级数。为了在搜索过程中提高代理的解决方案质量，设计了代理软搜索约束。将软搜索约束滤除明显劣质的解决方案，从而提高了解决方案质量。所提出的方法的性能在两种情况下验证：多重攻击和饱和攻击。仿真结果表明，本文提出的方法的性能明显优于由启发式算法和随机分配方法在目标分配的数量，目标威胁程度，雷达交换机的数量和雷达交换机的数量方面实现的累积检测持续时间。

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