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Application of the smooth variable structure filter to a multi-target tracking problem

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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.departmentMechanical Engineering
dc.contributor.editorKadar I
dc.date.accessioned2025-02-27T19:33:20Z
dc.date.available2025-02-27T19:33:20Z
dc.date.issued2011-05-13
dc.date.updated2025-02-27T19:33:20Z
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.identifier.doihttps://doi.org/10.1117/12.884063
dc.identifier.issn0277-786X
dc.identifier.issn1996-756X
dc.identifier.urihttp://hdl.handle.net/11375/31199
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

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