面向目标跟踪的单平台主被动传感器长期调度

以目标跟踪为背景，研究了单平台上主被动传感器的长期调度问题。通过合理、实时地切换主被动传感器，使得有限时域内的跟踪精度和辐射风险达到合理的平衡。将该调度问题构建成部分可观马氏决策过程(partially observable Markov decision process, POMDP)以同步实现目标跟踪和辐射控制。提出以容积采样法估算长期精度收益，以隐马氏模型滤波器推导长期辐射代价。最终将原问题转化成决策树并利用分枝定界法进行求解。仿真结果证明了本方法的有效性。

Non-myopic sensor scheduling in a single platform for target tracking

A non-myopic scheduling problem of how to intelligently and dynamically select active/passive sensors in a single platform for target tracking is studied. The goal is to develop a non myopic scheme to make an optimal trade off between the tracking accuracy and the radiation risk in a period of time. The problem is formulated as a partially observable Markov decision process (POMDP) to solve target tracking and emission control together. The cubature sampling is proposed to evaluate the long term accuracy reward, and the long term radiation cost is derived from the hidden Markov model filter. The original problem is transformed into a decision tree and solved using the branch and bound method. Simulation results demonstrate the effectiveness of the proposed scheduling approach.