Multi-Sensor Information Fusion Kalman Filter Weighted by Scalars for Systems with Colored Measurement Noises

[+] Author and Article Information
Shu-Li Sun

Department of Automation, Heilongjiang University, Harbin 150080, People’s Republic of Chinasunsl@hlju.edu.cn

Zi-Li Deng

Department of Automation, Heilongjiang University, Harbin 150080, People’s Republic of China

J. Dyn. Sys., Meas., Control 127(4), 663-667 (Feb 21, 2005) (5 pages) doi:10.1115/1.2101844 History: Received August 12, 2003; Revised February 21, 2005

An optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense. Based on this fusion criterion, a scalar weighting information fusion decentralized Kalman filter is given for discrete time-varying linear stochastic control systems measured by multiple sensors with colored measurement noises, which is equivalent to an information fusion Kalman predictor for systems with correlated noises. It has a two-layer fusion structure with fault tolerant and robust properties. Its precision is higher than that of each local filter. Compared with the fusion filter weighted by matrices and the centralized filter, it has lower precision when all sensors are faultless, but has reduced computational burden. Simulation researches show the effectiveness.

Copyright © 2005 by American Society of Mechanical Engineers
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Grahic Jump Location
Figure 2

The scalar weighting information fusion decentralized filters and the centralized filters (a) Position s(t) and Kalman filter ŝo(t∣t) and ŝc(t∣t). (b) Velocity ṡ(t) and Kalman filter ṡ̂o(t∣t) and ṡ̂c(t∣t)

Grahic Jump Location
Figure 1

The optimal information fusion decentralized Kalman predictor with a two-layer fusion structure



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