Iterated stochastic filters with additive updates for dynamic system identification: Annealing-type iterations and the filter bank


Autoria(s): Raveendran, Tara; Roy, Debasish; Vasu, Ram Mohan
Data(s)

2014

Resumo

A nonlinear stochastic filtering scheme based on a Gaussian sum representation of the filtering density and an annealing-type iterative update, which is additive and uses an artificial diffusion parameter, is proposed. The additive nature of the update relieves the problem of weight collapse often encountered with filters employing weighted particle based empirical approximation to the filtering density. The proposed Monte Carlo filter bank conforms in structure to the parent nonlinear filtering (Kushner-Stratonovich) equation and possesses excellent mixing properties enabling adequate exploration of the phase space of the state vector. The performance of the filter bank, presently assessed against a few carefully chosen numerical examples, provide ample evidence of its remarkable performance in terms of filter convergence and estimation accuracy vis-a-vis most other competing filters especially in higher dimensional dynamic system identification problems including cases that may demand estimating relatively minor variations in the parameter values from their reference states. (C) 2014 Elsevier Ltd. All rights reserved.

Formato

application/pdf

Identificador

http://eprints.iisc.ernet.in/50970/1/pro_eng_mec_38_77_2014.pdf

Raveendran, Tara and Roy, Debasish and Vasu, Ram Mohan (2014) Iterated stochastic filters with additive updates for dynamic system identification: Annealing-type iterations and the filter bank. In: PROBABILISTIC ENGINEERING MECHANICS, 38 (SI). pp. 77-87.

Publicador

ELSEVIER SCI LTD

Relação

http://dx.doi.org/ 10.1016/j.probengmech.2014.09.002

http://eprints.iisc.ernet.in/50970/

Palavras-Chave #Civil Engineering #Instrumentation and Applied Physics (Formally ISU)
Tipo

Journal Article

PeerReviewed