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Distributed Kalman Filter with minimum-time covariance computation
Thia, Jerry; Yuan, Ye; Shi, Ling et al.
2013In The proceedings of the IEEE 52nd Annual Conference on Decision and Control
Peer reviewed
 

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Abstract :
[en] This paper considerably improves the well-known Distributed Kalman Filter (DKF) algorithm by Olfati-Saber (2007) by introducing a novel decentralised consensus value computation scheme, using only local observations of sensors. It has been shown that the state estimates obtained in [8] and [9] approaches those of the Central Kalman Filter (CKF) asymptotically. However, the convergence to the CKF can sometimes be too slow. This paper proposes an algorithm that enables every node in a sensor network to compute the global average consensus matrix of measurement noise covariance in minimum time without accessing global information. Compared with the algorithm in [8], our theoretical analysis and simulation results show that the new algorithm can offer improved performance in terms of time taken for the state estimates to converge to that of the CKF.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Thia, Jerry
Yuan, Ye
Shi, Ling
Goncalves, Jorge ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Language :
English
Title :
Distributed Kalman Filter with minimum-time covariance computation
Publication date :
2013
Event name :
IEEE 52nd Annual Conference on Decision and Control
Event place :
Florence, Italy
Event date :
December 10-13, 2013
Main work title :
The proceedings of the IEEE 52nd Annual Conference on Decision and Control
Publisher :
IEEE
ISBN/EAN :
978-1-4673-5714-2
Pages :
1995 - 2000
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 10 March 2015

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