Distributed Sigma-Point Kalman Filtering for Sensor Networks: Dynamic Consensus Approach

被引：4

作者：

Zhou, Yan ^{[1
]}

Li, Jianxun ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200030, Peoples R China

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9 | 2009年

关键词：

sigma-point Kalman filtering; average consensus; weighted statistical linearization; target tracking; sensor network;

D O I：

10.1109/ICSMC.2009.5346001

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A scalable Sigma-Point Kalman filter (DSPKF) is proposed for distributed target tracking in a sensor network in this paper. The main idea is to use dynamic consensus strategy to the information form sigma-point Kalman filter (ISPKF) that derived from weighted statistical linearization perspective. Each node estimates the global average information contribution by using local and neighbors' information rather than by the information from all nodes in the network. Therefore, the proposed DSPKF algorithm is completely distributed and applicable to large-scale sensor network. A novel dynamic consensus filter is proposed, and its asymptotical convergence performance and stability are discussed. Finally, a numerical example is given to illustrate the proposed scheme.

引用

页码：5178 / 5183

页数：6

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