Target tracking formulation of the SVSF as a probabilistic data association algorithm

Attari M; Gadsden SA; Habibi SR

Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/31216

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DC Field	Value	Language
dc.contributor.author	Attari M	-
dc.contributor.author	Gadsden SA	-
dc.contributor.author	Habibi SR	-
dc.date.accessioned	2025-02-27T20:06:26Z	-
dc.date.available	2025-02-27T20:06:26Z	-
dc.date.issued	2013-01-01	-
dc.identifier.issn	0743-1619	-
dc.identifier.issn	2378-5861	-
dc.identifier.uri	http://hdl.handle.net/11375/31216	-
dc.description.abstract	Target tracking algorithms are important for a number of applications, including: physics, air traffic control, ground vehicle monitoring, and processing medical images. The probabilistic data association algorithm, in conjunction with the Kalman filter (KF), is one of the most popular and well-studied strategies. The relatively new smooth variable structure filter (SVSF) offers a robust and stable estimation strategy under the presence of modeling errors, unlike the KF method. The purpose of this paper is to introduce and formulate the SVSF-PDA, which can be used for target tracking. A simple example is used to compare the estimation results of the popular KF-PDA with the new SVSF-PDA. © 2013 AACC American Automatic Control Council.	-
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)	-
dc.subject	40 Engineering	-
dc.subject	4001 Aerospace Engineering	-
dc.title	Target tracking formulation of the SVSF as a probabilistic data association algorithm	-
dc.type	Article	-
dc.date.updated	2025-02-27T20:06:26Z	-
dc.contributor.department	Mechanical Engineering	-
dc.identifier.doi	https://doi.org/10.1109/acc.2013.6580830	-
Appears in Collections:	Mechanical Engineering Publications

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