Multi-rate distributed fusion estimation for sensor networks with packet losses

被引:168
作者
Zhang, Wen-An [1 ,2 ,3 ,4 ]
Feng, Gang [1 ]
Yu, Li [2 ,3 ]
机构
[1] City Univ Hong Kong, Dept Mech & Biomed Engn, Hong Kong, Hong Kong, Peoples R China
[2] Zhejiang Univ Technol, Dept Automat, Hangzhou 310023, Zhejiang, Peoples R China
[3] Zhejiang Prov United Key Lab Embedded Syst, Hangzhou 310023, Zhejiang, Peoples R China
[4] City Univ Hong Kong, Dept Mfg Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China
基金
中国国家自然科学基金; 高等学校博士学科点专项科研基金;
关键词
Distributed estimation; Kalman filtering; Information fusion; Wireless sensor networks; Packet losses; STOCHASTIC-SYSTEMS; KALMAN FILTER;
D O I
10.1016/j.automatica.2012.06.027
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a distributed fusion estimation method for estimating states of a dynamical process observed by wireless sensor networks (WSNs) with random packet losses. It is assumed that the dynamical process is not changing too rapidly, and a multi-rate scheme by which the sensors estimate states at a faster time scale and exchange information with neighbors at a slower time scale is proposed to reduce communication costs. The estimation is performed by taking into account the random packet losses in two stages. At the first stage, every sensor in the WSN collects measurements from its neighbors to generate a local estimate, then local estimates in the neighbors are further collected at the second stage to form a fused estimate to improve estimation performance and reduce disagreements among local estimates at different sensors. Local optimal linear estimators are designed by using the orthogonal projection principle, and the fusion estimators are designed by using a fusion rule weighted by matrices in the linear minimum variance sense. Simulations of a target tracking system are given to show that the time scale of information exchange among sensors can be slower while still maintaining satisfactory estimation performance by using the developed estimation method. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2016 / 2028
页数:13
相关论文
共 29 条
[1]  
Anderson B.D.O., 1979, Optimal Filtering
[2]   Distributed Kalman filtering based on consensus strategies [J].
Carli, Ruggero ;
Chiuso, Alessandro ;
Schenato, Luca ;
Zampieri, Sandro .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2008, 26 (04) :622-633
[3]   Diffusion Strategies for Distributed Kalman Filtering and Smoothing [J].
Cattivelli, Federico S. ;
Sayed, Ali H. .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2010, 55 (09) :2069-2084
[4]  
Chen H. M., 2004, P 43 IEEE C DEC CONT, P8179
[5]   Gossip Algorithms for Distributed Signal Processing [J].
Dimakis, Alexandros G. ;
Kar, Soummya ;
Moura, Jose M. F. ;
Rabbat, Michael G. ;
Scaglione, Anna .
PROCEEDINGS OF THE IEEE, 2010, 98 (11) :1847-1864
[6]   Distributed estimation and detection for sensor networks using hidden Markov random field models [J].
Dogandzic, Aleksandar ;
Zhang, Benhong .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2006, 54 (08) :3200-3215
[7]   Toward a theory of in-network computation in wireless sensor networks [J].
Giridhar, A ;
Kumar, PR .
IEEE COMMUNICATIONS MAGAZINE, 2006, 44 (04) :98-107
[8]   Kalman Filtering With Intermittent Observations: Weak Convergence to a Stationary Distribution [J].
Kar, Soummya ;
Sinopoli, Bruno ;
Moura, Jose M. F. .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2012, 57 (02) :405-420
[9]   Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability [J].
Kar, Soummya ;
Moura, Jose M. F. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2011, 59 (04) :1766-1784
[10]   Distributed Estimation in Energy-Constrained Wireless Sensor Networks [J].
Li, Junlin ;
AlRegib, Ghassan .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2009, 57 (10) :3746-3758