Convergence of basis pursuit de-noising with dynamic filtering

Adam S. Charles, Christopher J. Rozell

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

Abstract

Causal inference of dynamically changing signals is a vital task in many applications, including real-time image processing and channel estimation. Over the past few years, many algorithms have been proposed to accomplish this task, but extremely few algorithms have any theoretical guarantees on stability, convergence or performance. In this work we use results from the sparsity-based signal processing literature to demonstrate some basic bounds for one particular algorithm: basis pursuit de-noising with dynamic filtering (BPDN-DF). We show for what parameter ranges the algorithm remains stable for, and provide some guarantees on the steady-state approximation error.

Original languageEnglish (US)
Title of host publication2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages374-378
Number of pages5
ISBN (Electronic)9781479970889
DOIs
StatePublished - Feb 5 2014
Externally publishedYes
Event2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States
Duration: Dec 3 2014Dec 5 2014

Publication series

Name2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014

Conference

Conference2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
Country/TerritoryUnited States
CityAtlanta
Period12/3/1412/5/14

Keywords

  • Convergence
  • Dynamic filtering
  • Sparse signals

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems

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