Using approximate dynamic programming for multi-ESM scheduling to track ground moving targets

作     者：WAN Kaifang GAO Xiaoguang LI Bo LI Fei 

作者机构：Key Laboratory of Aerospace Information Perception and Photoelectric Control Ministry of EducationSchool of Electronics and Information Northwestern Polytechnical University 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2018年第29卷第1期

页      面：74-85页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 071102[理学-系统分析与集成] 

基　　金：supported by the National Natural Science Foundation of China(61573285 61305133) 

主　　题：sensor scheduling target tracking approximate dynamic programming non-myopic rollout belief state 

摘      要：This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.