Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications
Department of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, P. R. China
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Sensors 2008, 8(12), 8086-8103; https://doi.org/10.3390/s8128086
Received: 28 August 2008
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Revised: 26 November 2008
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Accepted: 3 December 2008
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Published: 8 December 2008
(This article belongs to the Special Issue Aerospace Sensor Systems)
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.
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MDPI and ACS Style
Luo, Y.; Zhu, Y.; Luo, D.; Zhou, J.; Song, E.; Wang, D. Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications. Sensors 2008, 8, 8086-8103. https://doi.org/10.3390/s8128086
AMA Style
Luo Y, Zhu Y, Luo D, Zhou J, Song E, Wang D. Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications. Sensors. 2008; 8(12):8086-8103. https://doi.org/10.3390/s8128086
Chicago/Turabian StyleLuo, Yingting, Yunmin Zhu, Dandan Luo, Jie Zhou, Enbin Song, and Donghua Wang. 2008. "Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications" Sensors 8, no. 12: 8086-8103. https://doi.org/10.3390/s8128086
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