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

Sensors

Yingting Luo²  Yunmin Zhu¹  Dandan Luo²  Jie Zhou²  Enbin Song² 

[1] Department of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, P. R. China; E-Mail

关键词: Random parameters matrices;    Kalman filtering;    Centralized fusion;    Distributed fusion;   

DOI  :  10.3390/s8128086

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.