Online intelligent maneuvering penetration methods of missile with respect to unknown intercepting strategies based on reinforcement learning

This paper considers the maneuvering penetration methods of missile which do not know the intercepting strategies of the interceptor beforehand. Based on reinforcement learning, the online intelligent maneuvering penetration methods of missile are derived. When the missile is locked by the interceptor, in terms of the tracking characteristics of the interceptor, the missile carries out tentative maneuvers which lead to the interceptor makes the responses respectively, in the light of the information on interceptor responses which can be gathered by the missile-borne detectors, online game confrontation learning is employed to increase the miss distance of the interceptor in guidance blind area by reinforcement learning algorithm, the results of which are used to generate maneuvering strategies that make the missile to achieve the successful penetration. The simulation results show that, compared with no maneuvering methods or random maneuvering methods, the methods proposed not only present higher probability of successful penetration, but also need less overload and lower command switching frequency. Moreover, the effectiveness of this maneuvering penetration methods can be realized under the condition of limited number of training.