On the choice of importance distributions for unconstrained and constrained state estimation using particle filter |
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Authors: | J. PrakashSachin C. Patwardhan Sirish L. Shah |
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Affiliation: | a Department of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University, Chennai 600044, India b Department of Chemical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400076, India c Department of Chemical and Materials Engineering, University of Alberta, Edmonton, T6G 2G6, Canada |
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Abstract: | Recursive state estimation of constrained nonlinear dynamical system has attracted the attention of many researchers in recent years. For nonlinear/non-Gaussian state estimation problems, particle filters have been widely used (Arulampalam et al. [1]). As pointed out by Daum [2], particle filters require a proposal distribution and the choice of proposal distribution is the key design issue. In this paper, a novel approach for generating the proposal distribution based on a constrained Extended Kalman filter (C-EKF), Constrained Unscented Kalman filter (C-UKF) and constrained Ensemble Kalman filter (C-EnkF) has been proposed. The efficacy of the proposed state estimation algorithms using a particle filter is illustrated via a successful implementation on a simulated gas-phase reactor, involving constraints on estimated state variables and another example problem, which involves constraints on the process noise (Rao et al. [10]). We also propose a state estimation scheme for estimating state variables in an autonomous hybrid system using particle filter with Unscented Kalman filter as a proposal and unconstrained Ensemble Kalman filter (EnKF) as a proposal. The efficacy of the proposed state estimation scheme for an autonomous hybrid system is demonstrated by conducting simulation studies on a three-tank hybrid system. The simulation studies underline the crucial role played by the choice of proposal distribution in formulation of particle filters. |
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Keywords: | Nonlinear observers Particle filters Constrained state estimation Importance sampling Truncated distributions and unscented particle filter |
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