Comparison of SPARLS and RLS algorithms for adaptive filtering
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Babadi, B; Kalouptsidis, N; Tarokh, V
Published in: 2009 IEEE Sarnoff Symposium, SARNOFF 2009 - Conference Proceedings
July 23, 2009
In this paper, we overview the Low Complexity Recursive ℒ1-Regularized Least Squares (SPARLS) algorithm proposed in [2], for the estimation of sparse signals in an adaptive filtering setting. The SPARLS algorithm is based on an Expectation-Maximization type algorithm adapted for online estimation. Simulation results for the estimation of multi-path wireless channels show that the SPARLS algorithm has significant improvement over the conventional widely-used Recursive Least Squares (RLS) algorithm, in terms of both mean squared error (MSE) and computational complexity.
Duke Scholars
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2009 IEEE Sarnoff Symposium, SARNOFF 2009 - Conference Proceedings
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Publication Date
July 23, 2009
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Babadi, B., Kalouptsidis, N., & Tarokh, V. (2009). Comparison of SPARLS and RLS algorithms for adaptive filtering. In 2009 IEEE Sarnoff Symposium, SARNOFF 2009 - Conference Proceedings. https://doi.org/10.1109/SARNOF.2009.4850336
Babadi, B., N. Kalouptsidis, and V. Tarokh. “Comparison of SPARLS and RLS algorithms for adaptive filtering.” In 2009 IEEE Sarnoff Symposium, SARNOFF 2009 - Conference Proceedings, 2009. https://doi.org/10.1109/SARNOF.2009.4850336.
Babadi B, Kalouptsidis N, Tarokh V. Comparison of SPARLS and RLS algorithms for adaptive filtering. In: 2009 IEEE Sarnoff Symposium, SARNOFF 2009 - Conference Proceedings. 2009.
Babadi, B., et al. “Comparison of SPARLS and RLS algorithms for adaptive filtering.” 2009 IEEE Sarnoff Symposium, SARNOFF 2009 - Conference Proceedings, 2009. Scopus, doi:10.1109/SARNOF.2009.4850336.
Babadi B, Kalouptsidis N, Tarokh V. Comparison of SPARLS and RLS algorithms for adaptive filtering. 2009 IEEE Sarnoff Symposium, SARNOFF 2009 - Conference Proceedings. 2009.
Published In
2009 IEEE Sarnoff Symposium, SARNOFF 2009 - Conference Proceedings
DOI
Publication Date
July 23, 2009