NDLI: Globally optimal distributed Kalman filtering fusion

Content Provider	Springer Nature Link
Author	Shen, XiaoJing Luo, YingTing Zhu, YunMin Song, EnBin
Copyright Year	2012
Abstract	The goal of this paper is to give a survey of the previous works on the globally optimal distributed Kalman filtering fusion with classical and nonclassical dynamic systems. Then, we summarize some of our recent results on nonclassical and unideal dynamic systems, including dynamic systems with feedback and cross-correlated sensor measurement noises, dynamic systems with random parameter matrices, and dynamic systems with out-of-sequence or asynchronous measurements. The global optimality in this paper means that the distributed Kalman filtering fusion is exactly equal to the corresponding centralized optimal Kalman filtering fusion. Therefore, not only all of the proposed fusion algorithms here are distributed, but performance as good as that of the corresponding optimal centralized fusion algorithms is achieved. There also exist many papers for other fusion optimality (e.g., the optimal convex linear estimation/compression fusion) discussion, which are not involved in this paper.
Starting Page	512
Ending Page	529
Page Count	18
File Format	PDF
ISSN	1674733X
Journal	Science in China Series : Information Sciences
Volume Number	55
Issue Number	3
e-ISSN	18691919
Language	English
Publisher	SP Science China Press
Publisher Date	2012-02-25
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Kalman filtering distributed estimation fusion feedback cross-correlated sensor measurement noises random Kalman filtering out-of-sequence measurements Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Computer Science

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