Consistent Kalman filters for nonlinear uncertain systems over sensor networks
Xingkang He1
Wenchao Xue2
Haitao Fang2
Xiaoming Hu3
1.Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden2.LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190,China;School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China3.Optimization and Systems Theory, Department of Mathematics, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden
摘要：In this paper, we study how to design filters for nonlinear uncertain systems over sensor networks. We introduce two Kalman-type nonlinear filters in centralized and distributed frameworks. Moreover, the tuning method for the parameters of the filters is established to ensure the consistency, i.e., the mean square error is upper bounded by a known parameter matrix at each time. We apply the consistent filters to the track-to-track association analysis of multi-targets with uncertain dynamics. A novel track-to-track association algorithm is proposed to identify whether two tracks are from the same target. It is proven that the resulting probability of mis-association is lower than the desired threshold. Numerical simulations on track-to-track association are given to show the effectiveness of the methods.
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论文发表日期：2020-11-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：10( 399-408 )
英文信息