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Proceedings Paper

Application of the smooth variable structure filter to a multi-target tracking problem
Author(s): S. A. Gadsden; D. Dunne; R. Tharmarasa; S. R. Habibi; T. Kirubarajan
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Paper Abstract

The 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.

Paper Details

Date Published: 5 May 2011
PDF: 8 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 805009 (5 May 2011); doi: 10.1117/12.884063
Show Author Affiliations
S. A. Gadsden, McMaster Univ. (Canada)
D. Dunne, McMaster Univ. (Canada)
R. Tharmarasa, McMaster Univ. (Canada)
S. R. Habibi, McMaster Univ. (Canada)
T. Kirubarajan, McMaster Univ. (Canada)

Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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