Abstract
For the multisensor linear stochastic singular system with unknown noise variances, the weighted measurement fusion (WMF) self-tuning Kalman estimation problem is solved in this paper. The consistent estimates of these unknown noise variances are obtained based on the correlation method. Applying the WMF method and the singular value decomposition (SVD) method yields the WMF reduced-order subsystems. Based on these consistent estimates of unknown noise variances and the new non-singular systems, the WMF self-tuning Kalman estimators of the state components and white noise deconvolution estimators are presented. Then the WMF self-tuning Kalman estimators of the original state are presented, and their convergence has been proved by dynamic error system analysis (DESA) method and dynamic variance error system analysis (DVESA) method. A simulation example of 3-sensors circuits systems verifies the effectiveness, the accuracy relationship and the convergence.
Acknowledgments
The authors would like to thank the reviewers, associate editor, and editor for their helpful and constructive comments.
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No potential conflict of interest was reported by the authors.
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Yinfeng Dou
Yinfeng Dou was born in Harbin, China in 1982. He received the M.S. degree in department of Physical Electronics in 2009 and the Ph.D degree in department of Automation in 2017 from the Heilongjiang University. His research interest covers pattern recognition, information fusion and adaptive control.
Chenjian Ran
Chenjian Ran was born in Chongqing, China in 1981. She received her B.Sc. degree in Nanjing University of Science and Technology in 2005, and M.Sc. and Ph. D. degree in department of Automation, Heilongjiang University in 2008 and in 2011, respectively. Currently she is an associate professor at the Department of Automation, Heilongjiang University. Her research interests include multisensor information fusion, state estimation, descriptor system and self-tuning filtering.