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

Published

Journal Article

© 2000 IEEE. We develop methods for performing filtering and smoothing in nonlinear 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.

Full Text

Duke Authors

Cited Authors

  • Doucet, A; Godsill, SJ; West, M

Published Date

  • January 1, 2000

Published In

Volume / Issue

  • 2 /

Start / End Page

  • 701 - 704

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2000.859056

Citation Source

  • Scopus