CUED Publications database

Monte Carlo filtering and smoothing with application to time-varying spectral estimation

Doucet, A and Godsill, SJ and West, M (2000) Monte Carlo filtering and smoothing with application to time-varying spectral estimation. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2. pp. 701-704. ISSN 0736-7791

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Abstract

We develop methods for performing filtering and smoothing in non-linear non-Gaussian dynamical models. The methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. In particular, novel techniques are presented for generation of random realisations from the joint smoothing distribution and for MAP estimation of the state sequence. Realisations of the smoothing distribution are generated in a forward-backward procedure, while the MAP estimation procedure can be performed in a single forward pass of the Viterbi algorithm applied to a discretised version of the state space. An application to spectral estimation for time-varying autoregressions is described.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:53
Last Modified: 26 Nov 2014 19:06
DOI: