电子学报 ›› 2014, Vol. 42 ›› Issue (10): 2104-2109.DOI: 10.3969/j.issn.0372-2112.2014.10.037

• 科研通信 • 上一篇    下一篇

基于部分可观马氏决策过程的多平台主被动传感器调度

张子宁1,2, 单甘霖1, 段修生1   

  1. 1. 军械工程学院电子与光学工程系, 河北石家庄 050003;
    2. 北京航天飞控中心, 北京 100094
  • 收稿日期:2013-06-17 修回日期:2014-04-09 出版日期:2014-10-25
    • 作者简介:
    • 张子宁 男,1984年生于河北石家庄,博士,主要从事传感器管理及信息融合方面的研究. E-mail:iron_zay@126.com;单甘霖 男,1962年生于江苏如东,教授,主要从事信息融合、神经网络和目标跟踪等方面的研究工作.段修生 男,1970年生于河北,副教授,主要研究方向为人工智能和故障诊断.
    • 基金资助:
    • 军内科研重点项目资助课题

POMDP-Based Scheduling of Active/Passive Sensors in Multi-Platform

ZHANG Zi-ning1,2, SHAN Gan-lin1, DUAN Xiu-sheng1   

  1. 1. Department of Electronic and Optical Engineering, Ordnance Engineering College, Shijiazhuang, Hebei 050003, China;
    2. Beijing Aerospace Control Center, Beijing 100094, China
  • Received:2013-06-17 Revised:2014-04-09 Online:2014-10-25 Published:2014-10-25
    • Supported by:
    • Key scientific research projects within the military

摘要:

为了使有限时域内的跟踪精度和辐射风险达到最佳平衡,本文研究了多传感器平台在协同跟踪目标时的主被动传感器调度问题.将该问题建立成基于部分可观马氏决策过程的数学模型以同步实现目标跟踪和辐射控制.在先见优化思想的基础上,借助由无迹采样近似得到的精度收益及由隐马氏模型滤波器推导出的辐射代价将调度问题转化成决策树问题,并采用分枝定界方法求解.仿真结果表明了该方法的有效性.

关键词: 传感器调度, 部分可观马氏决策过程, 先见优化, 无迹采样, 分枝定界

Abstract:

To make an optimal trade-off between the tracking accuracy and the radiation risk in a period of time, this paper studies the scheduling problem of selecting the active/passive sensors in the multi-platform for target tracking.The problem is formulated as a partially observable Markov decision process (POMDP), which can take both target tracking and emission control into account.Based on the foresight optimization, the approximate accuracy reward and the radiation cost, which are derived from the unscented transformation sampling and hidden Markov model (HMM) filter respectively, transform our problem into a tree search problem, and the branch and bound method is used for problem solution.The simulation results demonstrate the effectiveness of our approach.

Key words: sensor scheduling, partially observable Markov decision process, foresight optimization, unscented transformation sampling, branch and bound

中图分类号: