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.
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e-mail: sunsl@hlju.edu.cn
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December 2005
Technical Papers
Multi-Sensor Information Fusion Kalman Filter Weighted by Scalars for Systems with Colored Measurement Noises
Shu-Li Sun,
Shu-Li Sun
Department of Automation,
e-mail: sunsl@hlju.edu.cn
Heilongjiang University
, Harbin 150080, People’s Republic of China
Search for other works by this author on:
Zi-Li Deng
Zi-Li Deng
Department of Automation,
Heilongjiang University
, Harbin 150080, People’s Republic of China
Search for other works by this author on:
Shu-Li Sun
Department of Automation,
Heilongjiang University
, Harbin 150080, People’s Republic of Chinae-mail: sunsl@hlju.edu.cn
Zi-Li Deng
Department of Automation,
Heilongjiang University
, Harbin 150080, People’s Republic of ChinaJ. Dyn. Sys., Meas., Control. Dec 2005, 127(4): 663-667 (5 pages)
Published Online: February 21, 2005
Article history
Received:
August 12, 2003
Revised:
February 21, 2005
Citation
Sun, S., and Deng, Z. (February 21, 2005). "Multi-Sensor Information Fusion Kalman Filter Weighted by Scalars for Systems with Colored Measurement Noises." ASME. J. Dyn. Sys., Meas., Control. December 2005; 127(4): 663–667. https://doi.org/10.1115/1.2101844
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