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Nonlinear Bayesian state estimation: A review of recent developments

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Title Nonlinear Bayesian state estimation: A review of recent developments
 
Creator PATWARDHAN, SC
NARASIMHAN, S
JAGADEESAN, P
GOPALUNI, B
SHAH, SL
 
Subject Sequential Bayesian state estimation
Constrained state estimation
Multi-rate sampling
Observer stability
State and parameter estimation
EXTENDED KALMAN FILTER
MOVING HORIZON ESTIMATION
DISCRETE-TIME-SYSTEMS
DYNAMIC DATA RECONCILIATION
EQUALITY CONSTRAINTS
PARAMETER-ESTIMATION
MULTIVARIABLE SYSTEMS
LINEAR-SYSTEMS
ARRIVAL COST
IDENTIFICATION
 
Description Online estimation of the internal states is a perquisite for monitoring, control, and fault diagnosis of many engineering processes. A cost effective approach to monitor these variables in real time is to employ model-based state estimation techniques. Dynamic model-based state estimation is a rich and highly active area of research and many novel approaches have emerged over the last few years. In this paper, we review various recent developments in the area of nonlinear state estimators from a Bayesian perspective. In particular, we focus on the constrained state estimation (including systems modeled using differential-algebraic equations), the handling of multi-rate and delayed measurements and recent advances in model parameter estimation. Recent advances on the stability analysis of the estimation error dynamics are also briefly discussed. The review aims to provide an integrated view of important ideas, from the authors' perspective that have driven the research in this area in recent years. (C) 2012 Elsevier Ltd. All rights reserved.
 
Publisher PERGAMON-ELSEVIER SCIENCE LTD
 
Date 2014-10-15T13:07:38Z
2014-10-15T13:07:38Z
2012
 
Type Article; Proceedings Paper
 
Identifier CONTROL ENGINEERING PRACTICE, 20(10)933-953
http://dx.doi.org/10.1016/j.conengprac.2012.04.003
http://dspace.library.iitb.ac.in/jspui/handle/100/14964
 
Language en