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Consistent Kalman filters for nonlinear uncertain systems over sensor networks
XingkangHe,WenchaoXue,HaitaoFang,XiaomingHu
0
(Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 100 44, Stockholm, Sweden;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, China;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.
关键词:  Kalman filter · Consistency · Distributed filter · Track-to-track association
DOI:https://doi.org/10.1007/s11768-020-00012-0
基金项目:This work was supported by the National Natural Science Foundation of China (Nos. 11931018, 61973299) and the Beijing Advanced Innovation Center for Intelligent Robots and Systems (No. 2019IRS09).
Consistent Kalman filters for nonlinear uncertain systems over sensor networks
Xingkang He,Wenchao Xue,Haitao Fang,Xiaoming Hu
(Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 100 44, Stockholm, Sweden;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, China;Optimization and Systems Theory, Department of Mathematics, KTH Royal Institute of Technology, 100 44, Stockholm, Sweden)
Abstract:
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.
Key words:  Kalman filter · Consistency · Distributed filter · Track-to-track association