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 0736-7791Full text not available from this repository.
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.
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:51|
|Last Modified:||11 Dec 2016 02:27|