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<p>Tracking time-sensitive space targets, characterized by high uncertainty and highly dynamic motion, requires multi-satellite coordination while accounting for the risk of target loss. This study proposes a mathematical model and a Time-Sensitive Space Multi-Target Observation Scheduling (TSSMTOS) algorithm that considers both tracking rewards and target loss scenarios. First, we establish a generalized real-time multi-satellite scheduling framework for tracking these unpredictable, rapidly evolving space targets. Subsequently, we introduce a Double Deep Q-network for Variable-Number Targets (DDQN-VNT). This approach enables the real-time allocation of dual satellites to each target, guided by heuristic rules, effectively addressing dynamic observation requirements. Experimental results demonstrate that DDQN-VNT outperforms traditional rule-based algorithms, the Parallel Dual Adaptive Genetic Algorithm (PDA-GA), and Adaptive Large Neighborhood Search (ALNS) in complex scenarios, exhibiting superior performance, enhanced efficiency, and robust generalization capabilities.</p>

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Multi-satellite cooperative scheduling for time-sensitive space targets tracking based on double deep Q-network

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Shunyi Cao,
  • Xiaolu Liu,
  • Lei He,
  • Jie Chun

摘要

Tracking time-sensitive space targets, characterized by high uncertainty and highly dynamic motion, requires multi-satellite coordination while accounting for the risk of target loss. This study proposes a mathematical model and a Time-Sensitive Space Multi-Target Observation Scheduling (TSSMTOS) algorithm that considers both tracking rewards and target loss scenarios. First, we establish a generalized real-time multi-satellite scheduling framework for tracking these unpredictable, rapidly evolving space targets. Subsequently, we introduce a Double Deep Q-network for Variable-Number Targets (DDQN-VNT). This approach enables the real-time allocation of dual satellites to each target, guided by heuristic rules, effectively addressing dynamic observation requirements. Experimental results demonstrate that DDQN-VNT outperforms traditional rule-based algorithms, the Parallel Dual Adaptive Genetic Algorithm (PDA-GA), and Adaptive Large Neighborhood Search (ALNS) in complex scenarios, exhibiting superior performance, enhanced efficiency, and robust generalization capabilities.