Dynamic filtering of sparse signals using reweighted ℓ1

Adam S. Charles, Christopher J. Rozell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurate estimation of undersampled time-varying signals improves as stronger signal models provide more information to aid the estimator. In class Kalman filter-type algorithms, dynamic models of signal evolution are highly leveraged but there is little exploitation of structure within a signal at a given time. In contrast, standard sparse approximation schemes (e.g., L1 minimization) utilize strong structural models for a single signal, but do not admit obvious ways to incorporate dynamic models for data streams. In this work we introduce a causal estimation algorithm to estimate time-varying sparse signals. This algorithm is based on a hierarchical probabilistic model that uses re-weighted L1 minimization as its core computation, and propagates second order statistics through time similar to classic Kalman filtering. The resulting algorithm achieves very good performance, and appears to be particularly robust to errors in the dynamic signal model.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages6451-6455
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

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/TerritoryCanada
CityVancouver, BC
Period5/26/135/31/13

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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