Title :
Combined particle and smooth variable structure filtering for nonlinear estimation problems
Author :
Gadsden, S. Andrew ; Dunne, Darcy ; Habibi, Saeid R. ; Kirubarajan, Thia
Author_Institution :
Dept. of Mech. Eng., McMaster Univ., Hamilton, ON, Canada
Abstract :
In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the smooth variable structure filter (SVSF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SVSF is a relatively new estimation strategy based on sliding mode concepts, formulated in a predictor-corrector format. It has been shown to be very robust to modeling errors and uncertainties. The combined method (PF-SVSF) utilizes the estimates and state error covariance of the SVSF to formulate the proposal distribution which generates the particles used by the PF. The PF-SVSF method is applied on a nonlinear target tracking problem, where the results are compared with other popular estimation methods.
Keywords :
approximation theory; nonlinear estimation; parameter estimation; particle filtering (numerical methods); probability; state estimation; target tracking; combined particle structure filtering; nonlinear estimation problems; nonlinear target tracking problem; parameter estimation method; predictor-corrector format; probability distribution function; smooth variable structure filtering; state error covariance; state estimation method; Covariance matrix; Equations; Estimation; Mathematical model; Particle filters; Smoothing methods; Target tracking; Particle filter; nonlinear estimation; smooth variable structure filter; tracking;
Conference_Titel :
Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4577-0267-9
