Multi-sensor Ensemble Kalman filtering algorithm based on observation fuzzy support degree fusion
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- 作者：Zhentao Hu ; Jin Zhang ; zhj_henu@126.com" class="auth_mail" title="E-mail the corresponding author ; Zhen Guo
- 关键词：State estimation ; Ensemble Kalman filter ; Virtual observation set ; Fuzzy support degree
- 刊名：Optik - International Journal for Light and Electron Optics
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：127
- 期：20
- 页码：8520-8529
- 全文大小：1140 K
文摘
The effective treatment with uncertainty information of sensor observations is always considered as the key of filter design and optimization. Aiming at the observation deviation consistency problem, caused by random observation noise and external disturbance, appearing in the process of data assimilation of Ensemble Kalman filter, a novel multi-sensor Ensemble Kalman filtering algorithm based on observation fuzzy support degree fusion is proposed in this paper. Firstly, the multi-sensor observation set including real observation and virtual observation, in the multi-sensor observation framework, is constructed according to data assimilation strategy. Secondly, combined with the realization mechanism of membership function in fuzzy set theory, the confidence distance and the support degree matrix are designed to evaluate the support degree among multi-sensor observations. Furthermore, through the reasonable selection of validation observation and the weighted fusion strategy, the adverse influence caused by observation deviation consistency on filtering precision is improved. Finally, the theoretical analysis and experimental results show the feasibility and efficiency of proposed algorithm.