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基于运动意图识别的空间护卫策略设计(2026增刊1,集群会议增刊,投稿号20250406)

孙钦伯1,党朝辉2   

  1. 1. 西北农林科技大学
    2. 西北工业大学
  • 收稿日期:2025-10-31 修回日期:2025-12-12 出版日期:2025-12-15 发布日期:2025-12-15
  • 通讯作者: 党朝辉
  • 基金资助:
    国家自然科学基金

Spacecraft Guardian Strategy Design via Motion-Intent Recognition in Orbital Games

Qin-Bo SUN1,Zhaohui DANG2   

  1. 1. Northwest A&F University
    2.
  • Received:2025-10-31 Revised:2025-12-12 Online:2025-12-15 Published:2025-12-15
  • Contact: Zhaohui DANG
  • Supported by:
    National Natural Science Foundation of China

摘要: 中文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,本文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。实验结果表明,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. Exploiting the impulsive characteristics of spacecraft, a model-predictive-control scheme is first 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 probabilistic intent-inference outputs, thereby extending applicability to multi-intent scenarios. The optimization objective is dynamically re-weighted according to the inferred intent distribution, affording enhanced adaptability 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 Inference, Deep Learning, Spacecraft Guarding, Orbital Game

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