Application of the smooth variable structure filter to a multi-target tracking problem
S. Andrew Gadsden, Darcy Dunne, Ratnasingham Tharmarasa, Saeid R. Habibi, Thia Kirubarajan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
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.