A Nonlinear Filter Based on Fokker Planck Equation and MCMC Measurement Updates
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This paper presents a nonlinear filter based on the Fokker-Planck equation (FPE) for uncertainty propagation, coupled with a fast measurement update step. The measurement update is implemented as a function approximation performed over a Markov chain Monte Carlo (MCMC) sample of the un-normalized posterior obtained from the Bayes rule. MCMC sampling also results in fast computation of the normalization factor of the posterior, which is typically a computationally heavy step. A previously developed semianalytical meshless tool is employed to solve FPE for high dimensional systems in real time. Performance of the filter is studied for dynamical systems with 2 and 4 dimensional state spaces. ©2010 IEEE.
author list (cited authors)
Kumar, M., & Chakravorty, S.