基于传感器网络的远程状态估计

被引:3
作者
肖力
孙志刚
胡晓娅
陈绵云
机构
[1] 华中科技大学控制科学与工程系图像信息处理与智能控制教育部重点实验室
关键词
传感器网络; 卡尔曼滤波; 修正的黎卡提方程; 临界到达概率;
D O I
暂无
中图分类号
TP212.9 [传感器的应用]; TN929.5 [移动通信];
学科分类号
080202 ; 080402 ; 080904 ; 0810 ; 081001 ;
摘要
针对传感器网络中的远程状态估计,提出一种多传感器切换的卡尔曼滤波器.通过分析估计误差的统计特性,证明估计误差的协方差具有边界,采用线性矩阵不等式的形式给出了边界的收敛条件.研究测量数据丢失对估计器性能的影响,使用临界到达概率作为估计器的稳定性判据,得到采用线性矩阵不等式求解临界到达概率的方法.数值仿真证实了结论的正确性.
引用
收藏
页码:763 / 766
页数:4
相关论文
共 10 条
[1]  
Estimation with lossy measurements:jump estimators for jump systems. S.C.Smith,P.Seiler. IEEE Transactions on Automatic Control . 2003
[2]  
Computer Controlled Systems. K.J. Astrom,B. Wittenmark. . 1997
[3]  
Stability of Kalman filtering with Markovian packetlosses. H.Minyi,D.Subhrakanti. Automatica . 2007
[4]  
Kalman filtering with intermittent observations. SINOPOLI B,SCHENATO L,FRANSCESCHETTI M,et al. IEEE Transac-tions on Automatic Control . 2004
[5]  
On a stochastic sensor selection algorithm with applications in sensor scheduling and sensorcoverage. GUPTA V,CHUNG T H,HASSIBI B,et al. Automatica . 2006
[6]  
Linear Matrix Inequalities in Systems and Control Theory. S. Boyd,L.El Ghaoui,E. Feron,et al. SIAM Studies in Applied Mathematics . 1994
[7]  
Survey of maneuvering target tracking part I:dy-namic models. LI X R,JILKOV V P. IEEE Transactions on Aerospace and Electronic Sys-tem . 2003
[8]  
Convex Analysis and Optimization. BERTSEKAS D P,NEDIC A,OZDAGLAR A E. . 2003
[9]  
State estimation utilizing multi-ple description coding over lossy networks. JIN Z,GUPTA V,HASSIBI B,et al. Proceedings of the44th IEEE Conference on Decision and Control,and the European Control Conference . 2005
[10]  
Kalman Filtering with Partial Observation Losses. LIU X,GOLDSMITH A. Proceedings of the43rd IEEE Conference on Decision and Control . 2004