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

dc.contributor.author	Attari M
dc.contributor.author	Gadsden SA
dc.contributor.author	Habibi SR
dc.contributor.department	Mechanical Engineering
dc.date.accessioned	2025-02-27T20:06:26Z
dc.date.available	2025-02-27T20:06:26Z
dc.date.issued	2013-01-01
dc.date.updated	2025-02-27T20:06:26Z
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.identifier.doi	https://doi.org/10.1109/acc.2013.6580830
dc.identifier.issn	0743-1619
dc.identifier.issn	2378-5861
dc.identifier.uri	http://hdl.handle.net/11375/31216
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

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