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

S. A. Gadsden; D. Dunne; R. Tharmarasa; S. R. Habibi; T. Kirubarajan

doi:10.1117/12.884063

5 May 2011 Application of the smooth variable structure filter to a multi-target tracking problem

S. A. Gadsden, D. Dunne, R. Tharmarasa, S. R. Habibi, T. Kirubarajan

Proceedings Volume 8050, Signal Processing, Sensor Fusion, and Target Recognition XX; 805009 (2011) https://doi.org/10.1117/12.884063
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

S. A. Gadsden, D. Dunne, R. Tharmarasa, S. R. Habibi, and T. Kirubarajan "Application of the smooth variable structure filter to a multi-target tracking problem", Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 805009 (5 May 2011); https://doi.org/10.1117/12.884063

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