Nonlinear dynamic state estimation in instrumented structures with conditionally linear Gaussian substructures

Radhika, B and Manohar, CS (2012) Nonlinear dynamic state estimation in instrumented structures with conditionally linear Gaussian substructures. In: Probabilistic Engineering Mechanics, 30 . pp. 89-103.

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Abstract

Many problems of state estimation in structural dynamics permit a partitioning of system states into nonlinear and conditionally linear substructures. This enables a part of the problem to be solved exactly, using the Kalman filter, and the remainder using Monte Carlo simulations. The present study develops an algorithm that combines sequential importance sampling based particle filtering with Kalman filtering to a fairly general form of process equations and demonstrates the application of a substructuring scheme to problems of hidden state estimation in structures with local nonlinearities, response sensitivity model updating in nonlinear systems, and characterization of residual displacements in instrumented inelastic structures. The paper also theoretically demonstrates that the sampling variance associated with the substructuring scheme used does not exceed the sampling variance corresponding to the Monte Carlo filtering without substructuring. (C) 2012 Elsevier Ltd. All rights reserved.

Item Type:	Journal Article
Publication:	Probabilistic Engineering Mechanics
Publisher:	Elsevier Science
Additional Information:	Copyright for this article belongs to Elsevier Science.
Keywords:	MULTIPLE-TARGET TRACKING;SEQUENTIAL MONTE-CARLO;PARTICLE FILTERS;SPACE MODELS;SYSTEMS;IDENTIFICATION
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	30 Nov 2012 08:43
Last Modified:	30 Nov 2012 08:43
URI:	http://eprints.iisc.ac.in/id/eprint/45192

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