Backward hidden Markov chain for outlier-robust filtering and fixed-interval smoothing

Boujemaa Ait-El-Fquih, Cedric Gouy-Pailler

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

2 Scopus citations

Abstract

This paper addresses the problem of recursive estimation of a process in presence of outliers among the observations. It focuses on deriving robust approximate Kalman-like backward filtering and backward-forward fixed-interval smoothing algorithms in the context of linear hidden Markov chain with heavy-tailed measurement noise. The proposed algorithms are derived based on the backward Markovianity of the model as well as the variational Bayesian approach. In a simulation design, our algorithms are shown to outperform the classical Kalman filter in the presence of outliers.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages5504-5508
Number of pages5
DOIs
StatePublished - Oct 18 2013
Externally publishedYes
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period05/26/1305/31/13

Keywords

  • Backward Markovian models
  • Kalman-like algorithms
  • Robust filtering
  • Robust smoothing
  • Variational Bayes

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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