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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/31199
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dc.contributor.authorGadsden SA-
dc.contributor.authorDunne D-
dc.contributor.authorTharmarasa R-
dc.contributor.authorHabibi SR-
dc.contributor.authorKirubarajan T-
dc.contributor.editorKadar I-
dc.date.accessioned2025-02-27T19:33:20Z-
dc.date.available2025-02-27T19:33:20Z-
dc.date.issued2011-05-13-
dc.identifier.issn0277-786X-
dc.identifier.issn1996-756X-
dc.identifier.urihttp://hdl.handle.net/11375/31199-
dc.description.abstractThe most popular and well-studied estimation method is the Kalman filter (KF), which was introduced in the 1960s. It yields a statistically optimal solution for linear estimation problems. The smooth variable structure filter (SVSF) is a relatively new estimation strategy based on sliding mode theory, and has been shown to be robust to modeling uncertainties. The SVSF makes use of an existence subspace and of a smoothing boundary layer to keep the estimates bounded within a region of the true state trajectory. This article discusses the application of two estimation strategies (the KF and the SVSF) on a multi-target tracking problem. © 2011 SPIE.-
dc.publisherSPIE, the international society for optics and photonics-
dc.subject40 Engineering-
dc.subject4006 Communications Engineering-
dc.subject4009 Electronics, Sensors and Digital Hardware-
dc.subject51 Physical Sciences-
dc.subject5102 Atomic, Molecular and Optical Physics-
dc.titleApplication of the smooth variable structure filter to a multi-target tracking problem-
dc.typeArticle-
dc.date.updated2025-02-27T19:33:20Z-
dc.contributor.departmentMechanical Engineering-
dc.identifier.doihttps://doi.org/10.1117/12.884063-
Appears in Collections:Mechanical Engineering Publications

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