A Multi-Satellite Multi-Target Observation Task Planning and Replanning Method Based on DQN

This paper proposes a task planning method that integrates deep Q-learning network (DQN) with matrix sorting for Earth-oriented static multi-target cooperative observation tasks. The approach addresses emergent satellite failures in imaging constellations by eliminating the need for network model retraining during satellite malfunctions. It enables real-time generation of optimal task allocation schemes in contingency scenarios, ensuring efficient and adaptive task planning. Firstly, a mission scenario model is established by formulating task constraints and defining optimization objectives; secondly, a deep reinforcement learning framework is constructed to output the observation target sequence; then, the observation target sequence is transformed into a target sequence matrix, and a matrix-sorting planning method is proposed to carry out the optimal assignment of the task; lastly, a replanning method is designed for sudden satellite failure and insertion of urgent tasks. The experimental results show that the method has fast task planning speed, high task completion, and immediate task replanning capability.