RLS adaptive filtering with sparsity regularization

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

27 Citations (Scopus)

Abstract

We propose a new algorithm for the adaptive identification of sparse systems. The algorithm is based on the minimization of the RLS cost function when regularized by adding a sparsity inducing ℓ1 norm penalty. The resulting recursive update equations for the system impulse response estimate are in a similar form to the regular RLS. However, they include novel terms which account for the sparsity prior. The proposed, ℓ1 relaxation based RLS algorithm emphasizes sparsity during the adaptive filtering process and allows for faster convergence when the system under consideration is sparse. Computer simulations comparing the performance of the proposed algorithm to conventional RLS and other adaptive algorithms are provided. Simulations demonstrate that the new algorithm exploits the inherent sparse structure effectively.

Original languageEnglish
Title of host publication10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
Pages550-553
Number of pages4
DOIs
Publication statusPublished - 2010
Event10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010 - Kuala Lumpur, Malaysia
Duration: 10 May 201013 May 2010

Publication series

Name10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010

Conference

Conference10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
Country/TerritoryMalaysia
CityKuala Lumpur
Period10/05/1013/05/10

Keywords

  • Adaptive filters
  • RLS
  • Sparsity

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