北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (7): 1472-1480.doi: 10.13700/j.bh.1001-5965.2017.0555

• 论文 • 上一篇    下一篇

多传感器协同跟踪与辐射控制的调度算法

乔成林1, 段修生1,2, 单甘霖1   

  1. 1. 陆军工程大学 石家庄校区, 电子与光学工程系, 石家庄 050003;
    2. 石家庄铁道大学 机械工程学院, 石家庄 050043
  • 收稿日期:2017-08-31 出版日期:2018-07-20 发布日期:2018-07-25
  • 通讯作者: 段修生.E-mail:sjzdxsh@163.com E-mail:sjzdxsh@163.com
  • 作者简介:乔成林 男,博士研究生。主要研究方向:传感器管理、信息融合理论与应用;段修生 男,博士,教授,硕士生导师。主要研究方向:信息融合理论与应用、电子装备故障诊断等。单甘霖 男,博士,教授,博士生导师。主要研究方向:传感器管理、信息融合理论与应用、防空武器系统仿真与应用等。
  • 基金资助:
    国防预研基金(012015012600A2203)

Scheduling algorithm for multi-sensor collaboration tracking and radiation control

QIAO Chenglin1, DUAN Xiusheng1,2, SHAN Ganlin1   

  1. 1. Department of Electronic and Optical Engineering, Army Engineering University, Shijiazhuang 050003, China;
    2. College of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
  • Received:2017-08-31 Online:2018-07-20 Published:2018-07-25

摘要: 为了降低有源传感器在获得目标持续量测时被敌方截获的风险,提出一种多传感器协同跟踪与辐射控制的调度算法。该算法首先采用辐射度影响(ELI)衡量传感器辐射,将目标跟踪与辐射控制过程建立为部分可观马尔可夫决策(POMDP)过程。然后以隐马尔可夫模型(HMM)滤波器更新传感器辐射状态、推导长时辐射风险,以无迹卡尔曼滤波(UKF)更新目标状态、估计跟踪精度。最后考虑跟踪任务需求,构建精度约束下辐射控制的长时调度模型,并将该长时调度问题转化为决策树寻优问题,给出决策树节点次优下界值,采用改进分支定界技术(IB&B)快速求解最优调度序列。仿真结果验证了本文算法的有效性。

关键词: 传感器调度, 协同跟踪, 辐射控制, 决策树, 任务需求, 部分可观马尔可夫决策过程(POMDP)

Abstract: Active sensors obtain the target continuous measurements that can be intercepted by enemy system. To reduce the interception risk, a scheduling algorithm for multi-sensor collaboration tracking and radiation control is proposed. Firstly, the sensor radiation is represented by the emission level impact (ELI) and the processes of target tracking and radiation control are formulated as a partially observable Markov decision process (POMDP). Secondly, the hidden Markov model (HMM) filter is utilized to update the sensor radiation state and derive the non-myopic radiation risk. Meanwhile, the target state is updated by the unscented Kalman filter (UKF) which is also used to evaluate the target tracking accuracy. Finally, considering the tracking task requirement, the non-myopic scheduling model of radiation control is set up with tracking accuracy constraint and the scheduling problem is translated to a decision tree optimization problem. Then, the suboptimal lower bound of each decision tree node is given and the optimal scheduling sequence is obtained by improved branch and bound (IB&B) technique. Simulation results prove the validity of the proposed algorithm.

Key words: sensor scheduling, collaboration tracking, radiation control, decision tree, task requirement, partially observable Markov decision process (POMDP)

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