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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/14139

Title: On the choice of importance distributions for unconstrained and constrained state estimation using particle filter
Authors: PRAKASH, J
PATWARDHAN, SC
SHAH, SL
Keywords: ENSEMBLE KALMAN FILTER
BAYESIAN-ESTIMATION
DATA RECONCILIATION
SYSTEMS
APPROXIMATIONS
Issue Date: 2011
Publisher: ELSEVIER SCI LTD
Citation: JOURNAL OF PROCESS CONTROL,21(1)3-16
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. (C) 2010 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.jprocont.2010.08.001
http://dspace.library.iitb.ac.in/jspui/handle/100/14139
ISSN: 0959-1524
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