Pursuit-Evasion Game for Spacecraft With Incomplete Information Under <i>J</i>₂ Perturbation

In this paper, the dual spacecraft pursuit-evasion game problem under incomplete information is investigated, and a strategy-solving method for the incomplete information pursuit-evasion game based on particle swarm optimization and unscented particle filter (PSO-UPF) estimation is proposed. The completeness of the information available about the target’s cost function, which is determined by the weighting information, has a significant impact on the success of the pursuing strategy. For the cost function is unknown in incomplete information scenarios, a research framework of the pursuit-evasion game based on following observation and one-sided pursuit two stages is established. Besides, to describe the more accurate motion of the spacecraft, a Schweighart-Sedwick (SS) dynamic model is introduced that considers the effect of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> perturbation. Firstly, an equilibrium strategy for the SS model-based pursuit-evasion problem is derived under complete information. Next, for the incomplete information scenarios, an estimation method based on PSO-UPF of weight matrix information is established, which allows the cost function to be determined by the estimation method in the observation stage. Then, the pursuit strategy is re-designed in the one-sided pursuit stage based on the estimated cost function. Finally, the performance of the proposed method is validated by simulation. The results demonstrate that the approach can achieve good performance by efficiently estimating the weight information in the opponent’s cost function.