单甘霖1,,,
段修生2,
乔成林1,
王浩天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 050003
详细信息
作者简介:徐公国：男，1990年生，博士生，研究方向为传感器管理、信息融合
单甘霖：男，1962年生，教授，博士生导师，研究方向为信息融合理论与应用、武器系统仿真
段修生：男，1970年生，教授，博士生导师，研究方向为电子装备故障诊断、信息融合
乔成林：男，1990年生，博士生，研究方向为信息融合、传感器管理
王浩天：男，1989年生，博士生，研究方向为故障诊断、智能优化算法
通讯作者:单甘霖 shanganlin@163.com
中图分类号:TP391计量
文章访问数:1614
HTML全文浏览量:798
PDF下载量:58
被引次数:0
出版历程
收稿日期:2018-12-06
修回日期:2019-05-26
网络出版日期:2019-06-03
刊出日期:2019-09-10
Scheduling Method Based on Markov Decision Process for Multi-sensor Cooperative Detection and Tracking
Gongguo XU1,Ganlin SHAN1,,,
Xiusheng DUAN2,
Chenglin QIAO1,
Haotian WANG1
1. Department of Electronic and Optical Engineering, Army Engineering University Shijiazhuang Campus, Shijiazhuang 050003, China
2. Department of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050003, China
摘要
摘要:针对多任务场景下的传感器调度问题，该文提出一种面向目标协同检测与跟踪的多传感器调度方法。首先，该方法基于部分可观马尔科夫决策过程(POMDP)构建传感器调度模型，并基于后验克拉美-罗下界(PCRLB)设计优化目标函数。其次，考虑传感器切换时间和目标数目的时变性，采用随机分布粒子计算新生目标的检测概率，给出了固定目标数目和时变目标数目情形下的传感器调度方法。最后，为满足在线调度的实时性需求，采用自适应多种群协同差分进化(AMCDE)算法求解传感器调度方案。仿真结果表明，该方法能够有效应对多任务场景，实现多传感器资源的合理调度。
关键词:检测与跟踪/
传感器调度/
马尔科夫决策过程/
差分进化
Abstract:In order to solve the problem of sensor scheduling in the multi-task scenario, a multi-sensor scheduling method for target cooperative detection and tracking is proposed. Firstly, the sensor scheduling model is built based on the Partially Observable Markov Decision Process (POMDP) and an objective function is designed based on Posterior Carmér-Rao Lower Bound (PCRLB). Then, considering sensor switching time and the change of target number, the randomly distributed particles are used to calculate the detection probability of new target, and the sensor scheduling methods are given for the situations with fixed target number and time-varying target number. At last, to meet the real-time requirement of online scheduling, an Adaptive Multi-swarm Cooperative Differential Evolution (AMCDE) algorithm is used to solve the sensor scheduling scheme. Simulation results show that the method can effectively deal with multi-task scenarios and realize reasonable scheduling of multi-sensor resources.
Key words:Detection and tracking/
Sensor scheduling/
Markov decision process/
Differential evolution
PDF全文下载地址:
https://jeit.ac.cn/article/exportPdf?id=ef009c19-297d-43ea-a7a7-8652021ce867
