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Optimal Distributed Kalman Filtering Fusion With Singular Covariances of Filtering Errors and Measurement Noises.

Authors :
Song, Enbin
Xu, Jie
Zhu, Yunmin
Source :
IEEE Transactions on Automatic Control. May2014, Vol. 59 Issue 5, p1271-1282. 12p.
Publication Year :
2014

Abstract

In this paper, we present the globally optimal distributed Kalman filtering fusion with singular covariances of filtering errors and measurement noises. The following facts motivate us to consider the problem. First, the invertibility of estimation error covariance matrices is a necessary condition for most of the existing distributed fusion algorithms. However, it can not be guaranteed to exist in practice. For example, when state estimation for a given dynamic system is subject to state equality constraints, the estimation error covariance matrices must be singular. Second, the proposed fused state estimate is still exactly the same as the centralized Kalman filtering using all sensor raw measurements. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00189286
Volume :
59
Issue :
5
Database :
Academic Search Index
Journal :
IEEE Transactions on Automatic Control
Publication Type :
Periodical
Accession number :
95697339
Full Text :
https://doi.org/10.1109/TAC.2014.2308451