摘要
针对多被动式移动传感器协同工作时跟踪精度不稳定等问题,提出了一种基于多步预测的移动传感器长期调度方法。该方法结合部分可观马尔科夫决策过程(POMDP)构建多传感器调度模型,并基于后验克拉美罗下界(PCRLB)建立了传感器调度过程中的单步与长期代价函数;为有效减少计算复杂度,利用大量无迹采样粒子来近似估算长期代价值;通过将多约束非线性调度问题转化为决策树优化问题,可快速获取传感器长期调度方法,并给出了一种基于分支定界技术的改进决策树搜索算法。实验结果表明,所提方法能够实现移动式传感器的合理调度,在决策步长为2时,其目标跟踪精度相较于短期调度可平均提升6.08%;改进搜索算法的求解速度也更加迅速,能够有效满足在线调度的实时性要求。
A non-myopic scheduling method is proposed on the basis of multi-step prediction to solve the problem that tracking accuracies are not stable when multiple passive mobile sensors work together. A multi-sensor scheduling model is built based on the partially observable Markov decision process (POMDP), and a single-step cost function and a multi-step cost function in the scheduling process are given according to posterior Cramér-Rao lower bound (PCRLB). A large number of unscented sampling particles are used to approximate the multi-step prediction costs and to reduce the computation complexity. The sensor scheduling scheme is quickly obtained by transforming the multi-constraint nonlinear scheduling problem into a decision tree optimization problem, and solving the problem using an improved decision tree search algorithm based on the branch-and-bound technique. Simulation results and a comparison with the myopic scheduling method show that the proposed method can effectively make mobile sensors move reasonably, and the tracking accuracy improves by 6.08% on average when the decision step size is two. It is concluded that the improved search algorithm solves the problem faster and meets the real-time requirement of online scheduling.
作者
徐公国
单甘霖
段修生
XU Gongguo;SHAN Ganlin;DUAN Xiusheng(Department of Electronic and Optical Engineering,Army Engineering University,Shijiazhuang 050003,China;School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050003,China)
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2019年第6期125-133,150,共10页
Journal of Xi'an Jiaotong University
基金
“十三五”装备预研国防科技重点实验室基金资助项目(012015012600A2203)
关键词
移动传感器
传感器调度
部分可观马尔科夫决策过程
后验克拉美罗下界
决策树
mobile sensor
sensor scheduling
partially observable Markov decision process
posterior Cramér-Rao lower bound
decision tree