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• Zhu, Y
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• Zhou, J
• Song, E
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• Zhou, J
• Song, E
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• Zhou, J
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Sensors 2008, 8(12), 8086-8103; doi:10.3390/s8128086

Article

Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications

Yingting Luo, Yunmin Zhu* , Dandan Luo, Jie Zhou, Enbin Song and Donghua Wang

Department of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, P. R. China

* Author to whom correspondence should be addressed.

Received: 28 August 2008; in revised form: 26 November 2008 / Accepted: 3 December 2008 / Published: 8 December 2008

(This article belongs to the Special Issue Aerospace Sensor Systems)

Download PDF Full-Text [187 KB, uploaded 9 December 2008 10:59 CET]

Abstract: This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is proved that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements; therefore, it achieves the best performance. More importantly, this result can be applied to Kalman filtering with uncertain observations including the measurement with a false alarm probability as a special case, as well as, randomly variant dynamic systems with multiple models. Numerical examples are given which support our analysis and show significant performance loss of ignoring the randomness of the parameter matrices.

Keywords: Random parameters matrices; Kalman filtering; Centralized fusion; Distributed fusion

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