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朱允民

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期刊论文

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

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

Sensors 2008, 8, 8086-8103,-0001,():

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摘要/描述

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.

版权说明:以下全部内容由朱允民上传于   2009年06月29日 15时39分48秒,版权归本人所有。

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