In order to enhance the capability of tracking targets autonomously of UAV, a model for UAV on-line path planning is established based on the theoretical framework of partially observable markov decision process (POMDP). The elements of the POMDP model are analyzed and described. According to the diversity of the target motion in real world,the law of state transition in POMDP model is described by the method of Interactive Multiple Model(IMM)To adapt to the target maneuvering changes. The action strategy of the UAV is calculated through nominal belief-state optimization(NBO)algorithm which is designed to search optimal action policy to minimize the cumulative cost of action. The generated action strategy controls the UAV flight. The simulation results show that the established POMDP model can achieve autonomous planning for UAV route, and it can control the UAV to effec-tively track target. The planning path is more reasonable and efficient than the result of using single state transition law.%为提升无人机对目标的自主跟踪能力,以部分可观测马尔科夫决策过程(POMDP)为理论框架,建立起无人机路径在线规划POMDP模型.分析并描述了POMDP模型中的各个要素,针对目标运动规律的复杂性,引入交互多模型(IMM)方法描述POMDP模型中的状态转移规律,以适应目标的机动变化.同时以POMDP模型中的累加代价函数为目标函数,结合使用名义信念状态优化(NBO)算法求解无人机的行动策略,产生的行动策略控制无人机飞行.仿真结果表明,所建立的模型能够实现对无人机路径的自主规划,能够控制无人机对目标进行有效跟踪,规划的无人机路径较之使用单一的目标状态转移规律更加合理高效.
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