基于强化学习的多机协同传感器管理

In the networked war, it is urgent that airborne radar can continuously acquire target information while ensuring the safe survival. Focusing on this problem, in the context of safe transition tasks of multi-airborne cooperative operations, this paper proposes a intelligent sensor management method based on deep reinforcement learning. First, the real-time threat membership is calculated considering the signal radiation and several threat factors. Then, the radar-target assignment problem is modeled in a reinforcement learning framework. The neural network is used to approximate the action-value function, and the parameters are updated according to the temporal-difference algorithm. It can be seen from the simulation that the proposed algorithm improves the task success rate and shortens the time of task completion compared with the traditional scheduling methods.