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Journal of System Simulation

Abstract

Abstract: To solve the problems of multiple types of state information and correlation of time-series state information encountered in the dynamic weapon target assignment problem, a dynamic weapon target assignment method based on an improved deep reinforcement learning algorithm is proposed. A multiinput assignment model of target missile-interceptor unit, interceptor unit, and defense unit under multiwave target and multi-phase is constructed. A multi-input state space is designed, and a Markov decision process is established in conjunction with the problem model. A feature extraction network combining multi-input information processing and gated recurrent network is designed, which improves the ability to extract state information, retains the necessary state information and forgets the unimportant state information, and the multi-head attention mechanism is introduced into the strategy network to improve the expressiveness and convergence speed of the model. As shown by the experimental results, the dynamic weapon target assignment method in this paper has better convergence speed and interception gain.

First Page

508

Last Page

516

CLC

TP391.9; E91

Recommended Citation

Fei Shuaidi, Cai Changlong, Liu Fei, et al. Research on the Target Allocation Method for Air Defense and Anti-missile Defense of Naval Ships[J]. Journal of System Simulation, 2025, 37(2): 508-516.

Corresponding Author

Cai Changlong

DOI

10.16182/j.issn1004731x.joss.23-1219

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