Combined particle and smooth variable structure filtering for nonlinear estimation problems

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.

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