Iterated gain-based stochastic filters for dynamic system identification

Raveendran, Tara and Roy, Debasish and Vasu, Ram Mohan (2014) Iterated gain-based stochastic filters for dynamic system identification. In: JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 351 (2, SI). pp. 1093-1111.

PDF jou_fra_ins_eng_app_mat_351-2_1093_2014.pdf - Published Version Restricted to Registered users only Download (2MB) | Request a copy

Abstract

We propose a novel form of nonlinear stochastic filtering based on an iterative evaluation of a Kalman-like gain matrix computed within a Monte Carlo scheme as suggested by the form of the parent equation of nonlinear filtering (Kushner-Stratonovich equation) and retains the simplicity of implementation of an ensemble Kalman filter (EnKF). The numerical results, presently obtained via EnKF-like simulations with or without a reduced-rank unscented transformation, clearly indicate remarkably superior filter convergence and accuracy vis-a-vis most available filtering schemes and eminent applicability of the methods to higher dimensional dynamic system identification problems of engineering interest. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Item Type:	Journal Article
Publication:	JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
Publisher:	PERGAMON-ELSEVIER SCIENCE LTD
Additional Information:	Copyright for this article belongs to the PERGAMON-ELSEVIER SCIENCE LTD, ENGLAND
Department/Centre:	Division of Mechanical Sciences > Civil Engineering Division of Physical & Mathematical Sciences > Instrumentation Appiled Physics
Date Deposited:	20 Feb 2014 05:19
Last Modified:	20 Feb 2014 05:19
URI:	http://eprints.iisc.ac.in/id/eprint/48413

Actions (login required)

View Item