Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach

With the increase of number satellites in space, satellite tracking, telemetry, and command (TT&C) is becoming more important for aerospace. This paper proposes a method low time fragmentation oriented. TT&C task planning based on Double Deep Q-Network (DDQN). mainly solves problem poor responses to emergency tasks caused by large amount fragments equipment under traditional task-planning methods. Firstly, multi-objective optimization model aiming at minimizing maximizing revenue formulated. Then, according conflict characteristics tasks, graph proposed, which transferred into an independent set problem. Finally, DDQN combined with structure embedding solve The experimental results demonstrate that proposed reduces fragment 32% shortens response 36% compared existing