ABSTRACT
This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor asynchronous sampling systems with correlated noises. The state updates uniformly and the sensors sample randomly. Based on the measurement augmentation method, the asynchronous sampling system is transformed to the synchronous sampling one. Local filter is designed by using an innovation analysis approach. Then, the filtering error cross-covariance matrix between any two local filters is derived. Finally, the optimal distributed fusion filter is proposed by using matrix-weighted fusion algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.
Acknowledgments
The authors would like to express their sincere thanks to the editor and the reviewers for their helpful assessments and comments.
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No potential conflict of interest was reported by the authors.
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Notes on contributors
Honglei Lin
Honglei Lin was born in Heilongjiang, China in 1986. She received the B.S. degree in department of mathematics from Mudanjiang Normal University, China, in 2010 and M.E. degree in School of Mathematics science from Heilongjiang University, China, in 2013, respectively. Currently, she is working for her Ph.D. degree in Heilongjiang University. Her main research interests are state estimation and information fusion filtering.
Shuli Sun
Shuli Sun was born in Heilongjiang, China in 1971. He received the B.S. degree in department of mathematics and M.E. degree in department of automation from Heilongjiang University, China, in 1996 and 1999, respectively. He received the Ph.D. degree in School of Astronautics from Harbin Institute of Technology, China, in 2004. He is a Research Fellow in Nanyang Technological University, Singapore, from 2006 to 2007. Since 2006, he has been a Professor in the School of Electronic Engineering of Heilongjiang University, China. His research interests are in the areas of state estimation, signal processing, information fusion and sensor network.