Bayesian fixed-interval smoothing algorithms in singular state-space systems

Boujemaa Ait El Fquih*, F. Desbouvries

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Fixed-interval Bayesian smoothing in state-space systems has been addressed for a long time. However, as far as the measurement noise is concerned, only two cases have been addressed so far: the regular case, i.e. with positive definite covariance matrix; and the perfect measurement case, i.c, with zero measurement noise. In this paper we address the smoothing problem in the intermediate case where the measurement noise covariance is positive semi definite (p.s.d.) with arbitrary rank. We exploit the singularity of the model in order to transform the original state-space system into a pairwise Markov chain (PMC) with reduced state dimension. Finally, the a posteriori Markovianity of the reduced state enables us to propose a family of fixed-interval smoothing algorithms.

Original languageEnglish (US)
Title of host publicationMachine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
DOIs
StatePublished - Dec 1 2009
EventMachine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 - Grenoble, France
Duration: Sep 2 2009Sep 4 2009

Publication series

NameMachine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

Other

OtherMachine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009
CountryFrance
CityGrenoble
Period09/2/0909/4/09

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Education

Fingerprint Dive into the research topics of 'Bayesian fixed-interval smoothing algorithms in singular state-space systems'. Together they form a unique fingerprint.

Cite this