Two-stage pursuit strategy for incomplete-information impulsive space pursuit-evasion mission using reinforcement learning

Bin Yang, Pengxuan Liu, Jinglang Feng, Shuang Li

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a novel and robust two-stage pursuit strategy for the incomplete-information impulsive space pursuit-evasion missions considering the J2 perturbation. The strategy firstly models the impulsive pursuit-evasion game problem into a far-distance rendezvous stage and a close-distance game stage according to the perception range of the evader. For the far-distance rendezvous stage, it is transformed into a rendezvous trajectory optimization problem and a new objective function is proposed to obtain the pursuit trajectory with the optimal terminal pursuit capability. For the close-distance game stage, a closed-loop pursuit approach is proposed using one of the reinforcement learning algorithms, i.e. the deep deterministic policy gradient algorithm, to solve and update the pursuit trajectory for the incomplete-information impulsive pursuit-evasion missions. The feasibility of this novel strategy and its robustness to different initial states of the pursuer and evader and to the evasion strategies are demonstrated for the sun-synchronous orbit pursuit-evasion game scenarios. The results of the Monte Carlo tests show that the successful pursuit ratio of the proposed method is over 91% for all the given scenarios
Original languageEnglish
Article number299
Number of pages16
JournalAerospace
Volume8
Issue number10
DOIs
Publication statusPublished - 14 Oct 2021

Keywords

  • space pursuit-evasion mission
  • incomplete-information game
  • reinforcement learning
  • impulsive propulsion
  • J2 perturbation

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