| Summary: | In cognitive electronic warfare, when a typical combat vehicle, such as an unmanned combat air vehicle (UCAV), uses radar sensors to explore an unknown space, the target-searching fails due to an inefficient servoing/tracking system. Thus, to solve this problem, we developed an autonomous reasoning search method that can generate efficient decision-making actions and guide the UCAV as early as possible to the target area. For high-dimensional continuous action space, the UCAV’s maneuvering strategies are subject to certain physical constraints. We first record the path histories of the UCAV as a sample set of supervised experiments and then construct a grid cell network using long short-term memory (LSTM) to generate a new displacement prediction to replace the target location estimation. Finally, we enable a variety of continuous-control-based deep reinforcement learning algorithms to output optimal/sub-optimal decision-making actions. All these tasks are performed in a three-dimensional target-searching simulator, i.e., the Explorer game. Please note that we use the behavior angle (BHA) for the first time as the main factor of the reward-shaping of the deep reinforcement learning framework and successfully make the trained UCAV achieve a 99.96% target destruction rate, i.e., the game win rate, in a 0.1 s operating cycle.
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