Record Details

On the choice of importance distributions for unconstrained and constrained state estimation using particle filter

DSpace at IIT Bombay

View Archive Info
 
 
Field Value
 
Title On the choice of importance distributions for unconstrained and constrained state estimation using particle filter
 
Creator PRAKASH, J
PATWARDHAN, SC
SHAH, SL
 
Subject ENSEMBLE KALMAN FILTER
BAYESIAN-ESTIMATION
DATA RECONCILIATION
SYSTEMS
APPROXIMATIONS
Nonlinear observers
Particle filters
Constrained state estimation
Importance sampling
Truncated distributions and unscented particle filter
 
Description 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.
 
Publisher ELSEVIER SCI LTD
 
Date 2012-06-26T07:30:07Z
2012-06-26T07:30:07Z
2011
 
Type Article
 
Identifier JOURNAL OF PROCESS CONTROL,21(1)3-16
0959-1524
http://dx.doi.org/10.1016/j.jprocont.2010.08.001
http://dspace.library.iitb.ac.in/jspui/handle/100/14139
 
Language English