Path planning for unmanned aerial vehicle passive detection under the framework of partially observable markov decision process

In order to improve the UAV's autonomous target-tracking ability under the condition of passive detection, this paper established an unmanned aerial vehicle (UAV) path planning POMDP model in passive detection situation under the theoretical framework of Partially Observable Markov Decision Process (POMDP). A method of finite set of action was proposed based on the characteristics of path planning. The optimal operation was solved out by combining the method and Unscented Kalman Filter (UKF). The relevant simulation results show that the model can achieve efficient planning for UAV route, and control the UAV to effectively track target, therefore proving its validity in the process.