Multi-sensor centralized fusion without measurement noise covariance by variational bayesian approximation

Gao, X; Chen, J; Tao, D; Li, X

Multi-sensor centralized fusion without measurement noise covariance by variational bayesian approximation

Gao, X Chen, J Tao, D

Li, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Aerospace and Electronic Systems, 2011, 47 (1), pp. 718 - 727
Issue Date:: 2011-01-01

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Field	Value	Language
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Chen, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2011-01-01	en_US
dc.identifier.citation	IEEE Transactions on Aerospace and Electronic Systems, 2011, 47 (1), pp. 718 - 727	en_US
dc.identifier.issn	0018-9251	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15336
dc.description.abstract	The work presented here solves the multi-sensor centralized fusion problem in the linear Gaussian model without the measurement noise variance. We generalize the variational Bayesian approximation based adaptive Kalman filter (VB-AKF) from the single sensor filtering to a multi-sensor fusion system, and propose two new centralized fusion algorithms, i.e., VB-AKF-based augmented centralized fusion algorithm and VB-AKF-based sequential centralized fusion algorithm, to deal with the case that the measurement noise variance is unknown. The simulation results show the effectiveness of the proposed algorithms. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Aerospace and Electronic Systems	en_US
dc.relation.isbasedon	10.1109/TAES.2011.5705702	en_US
dc.subject.classification	Aerospace & Aeronautics	en_US
dc.title	Multi-sensor centralized fusion without measurement noise covariance by variational bayesian approximation	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	47	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0901 Aerospace Engineering	en_US
utslib.for	0909 Geomatic Engineering	en_US
dc.location.activity	WOS:000286931800048	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	47	en_US

Abstract:

The work presented here solves the multi-sensor centralized fusion problem in the linear Gaussian model without the measurement noise variance. We generalize the variational Bayesian approximation based adaptive Kalman filter (VB-AKF) from the single sensor filtering to a multi-sensor fusion system, and propose two new centralized fusion algorithms, i.e., VB-AKF-based augmented centralized fusion algorithm and VB-AKF-based sequential centralized fusion algorithm, to deal with the case that the measurement noise variance is unknown. The simulation results show the effectiveness of the proposed algorithms. © 2011 IEEE.

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http://hdl.handle.net/10453/15336