Nonlinear filtering for continuous-time systems using the linear fractional transformation model

Pasha, SA; Hoang, DT

Nonlinear filtering for continuous-time systems using the linear fractional transformation model

Pasha, SA Hoang, DT

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Publication Type:: Conference Proceeding
Citation:: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2009, pp. 3289 - 3292
Issue Date:: 2009-09-23

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Field	Value	Language
dc.contributor.author	Pasha, SA	en_US
dc.contributor.author	Hoang, DT https://orcid.org/0000-0003-0292-6061	en_US
dc.date.issued	2009-09-23	en_US
dc.identifier.citation	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2009, pp. 3289 - 3292	en_US
dc.identifier.isbn	9781424423545	en_US
dc.identifier.issn	1520-6149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16367
dc.description.abstract	In this paper, we propose Bayesian filtering technique for continuous-time dynamical models with sampled-data measurements using the linear fractional transformation (LFT) model which transforms the nonlinear state space model into an exact equivalent linear model with a simple nonlinear feedback loop. The linear model is amenable to Euler discretization. Simulation results demonstrate that the proposed filtering technique gives better approximation and tracking performance than the unscented Kalman filter (UKF) which diverges for highly nonlinear problems. ©2009 IEEE.	en_US
dc.relation.ispartof	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICASSP.2009.4960327	en_US
dc.title	Nonlinear filtering for continuous-time systems using the linear fractional transformation model	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Taipei, Taiwan	en_US
dc.location.activity	WOS:000180862400008
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, we propose Bayesian filtering technique for continuous-time dynamical models with sampled-data measurements using the linear fractional transformation (LFT) model which transforms the nonlinear state space model into an exact equivalent linear model with a simple nonlinear feedback loop. The linear model is amenable to Euler discretization. Simulation results demonstrate that the proposed filtering technique gives better approximation and tracking performance than the unscented Kalman filter (UKF) which diverges for highly nonlinear problems. ©2009 IEEE.

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http://hdl.handle.net/10453/16367