航空学报 > 2026, Vol. 47 Issue (S1): 733013-733013   doi: 10.7527/S1000-6893.2025.33013

基于运动意图识别的空间护卫策略设计

孙钦伯1, 党朝辉2()   

  1. 1.西北农林科技大学 机械与电子工程学院,杨凌 712100
    2.西北工业大学 航天学院,西安 710129
  • 收稿日期:2025-10-31 修回日期:2025-11-06 接受日期:2025-11-28 出版日期:2025-12-17 发布日期:2025-12-15
  • 通讯作者: 党朝辉 E-mail:dangzhaohui@nwpu.edu.cn
  • 基金资助:
    国家自然科学基金(12472046)

Spacecraft guardian strategy design via motion-intent recognition

Qinbo SUN1, Zhaohui DANG2()   

  1. 1.College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,China
    2.School of Astronautics,Northwestern Polytechnical University,Xi’an 710129,China
  • Received:2025-10-31 Revised:2025-11-06 Accepted:2025-11-28 Online:2025-12-17 Published:2025-12-15
  • Contact: Zhaohui DANG E-mail:dangzhaohui@nwpu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(12472046)

摘要:

针对非完全信息条件下博弈决策中目标意图未知和机动策略难以优选的问题,提出了基于意图识别的空间轨道机动决策方法。首先结合航天器脉冲机动特点,设计了有限时间区域内的模型预测控制框架,能够针对单一场景快速优化护卫策略。然后,提出了一种融合非合作目标运动意图识别结果的护卫机动博弈策略优化方法,适用于多意图场景。依据意图识别的概率动态调整策略优化指标,使得护卫机动策略在应对复杂、不确定的空间环境时更加灵活。试验结果表明,所提出的基于意图识别的轨道机动决策方法,在多种空间护卫场景中展显优势。

关键词: 非合作目标, 意图识别, 深度学习, 空间护卫, 轨道博弈

Abstract:

To address the challenges of unknown target intent and strategy selection under incomplete-information orbital games, an intent-inference-based maneuver-decision framework is proposed. First, considering the impulsive characteristics of spacecraft, a model-predictive-control scheme is devised within a finite-time horizon to rapidly generate guardian strategies for a single prespecified intent. Subsequently, an integrated guardian maneuver optimization method is developed that fuses intent interference results of a non-cooperative target, making it applicable to multi-intent scenarios. By dynamically adjusting the strategy optimization objective according to the inferred intent distribution, the escort maneuver strategy becomes more flexible when responding to uncertain operational environments. Simulation results across diverse space-guardian missions confirm the superior performance of the proposed approach.

Key words: non-cooperative target, intent recogntion, deep learning, spacecraft guarding, orbital game

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