Interactive Multiple-Model Learning Filter for Spacecraft Pursuit–Evasion Game Strategy Switch Based on Long Short-Term Memory Network

Abstract

Aiming to address the problem of pursuit and interception for spacecraft using multiple evasion strategies, a pursuit strategy involving the use of an interactive multiple-model filter (IMM) in a pursuit–evasion game is considered, where the Evader adopts a switchable evasion strategy based on a linear quadratic method and zero-effort miss method. In this case, an improved interactive multiple-model feedback-learning filter method based on a long short-term memory network (LSTM-IMML) is proposed to estimate the Evader’s strategy mode, with the resulting estimation allowing the Pursuer to then switch its own strategy to the appropriate pursuit strategy to intercept the Evader. Also, the improved interactive multiple-model feedback learning filter can feed back the fusion estimation of the last-time state to the next-time state to improve estimation accuracy. An LSTM-based probability estimation network is designed to accurately estimate the probability of different modes. The proposed LSTM-IMML method can be used in the pursuit–evasion game when the Evader is able to switch its evasion strategy. The simulation results show that the LSTM-IMML method has better state estimation accuracy, and the mode probability estimation of the Evader is more exact and stable.

Keywords

• pursuit–evasion game
• interactive multiple-model filter
• long short-term memory network
• mode probability