Multisensor data fusion in nonlinear Bayesian filtering

Rashid, U; Tuan, HD; Apkarian, P; Kha, HH

Multisensor data fusion in nonlinear Bayesian filtering

Rashid, U Tuan, HD

Apkarian, P Kha, HH

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Publication Type:: Conference Proceeding
Citation:: 2012 4th International Conference on Communications and Electronics, ICCE 2012, 2012, pp. 351 - 354
Issue Date:: 2012-11-22

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Field	Value	Language
dc.contributor.author	Rashid, U	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Apkarian, P	en_US
dc.contributor.author	Kha, HH	en_US
dc.date.issued	2012-11-22	en_US
dc.identifier.citation	2012 4th International Conference on Communications and Electronics, ICCE 2012, 2012, pp. 351 - 354	en_US
dc.identifier.isbn	9781467324939	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30768
dc.description.abstract	In this paper, an optimal multisensor data fusion method is proposed to estimate the state of a highly nonlinear dynamic model. Data fusion from spatially distributed sensors is expressed as a semi definite program (SDP) that aims at minimizing mean-squared error (MSE) of the state estimate under total transmit power constraints. Furthermore, a Bayesian filtering technique, based on unscented transformations and linear fractional transformations (LFT), is presented under multisensor framework to implement the SDP. Extensive simulations are performed to justify effectiveness of the proposed multisensor scheme over a single sensor supplied with the same power budget as that of the entire sensor network. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 4th International Conference on Communications and Electronics, ICCE 2012	en_US
dc.relation.isbasedon	10.1109/CCE.2012.6315926	en_US
dc.title	Multisensor data fusion in nonlinear Bayesian filtering	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Hue, Vietnam
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, an optimal multisensor data fusion method is proposed to estimate the state of a highly nonlinear dynamic model. Data fusion from spatially distributed sensors is expressed as a semi definite program (SDP) that aims at minimizing mean-squared error (MSE) of the state estimate under total transmit power constraints. Furthermore, a Bayesian filtering technique, based on unscented transformations and linear fractional transformations (LFT), is presented under multisensor framework to implement the SDP. Extensive simulations are performed to justify effectiveness of the proposed multisensor scheme over a single sensor supplied with the same power budget as that of the entire sensor network. © 2012 IEEE.

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http://hdl.handle.net/10453/30768