Sparse LMS for system identification

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Sparse LMS for system identification
We propose a new approach to adaptive system identification when the system model is sparse. The approach applies the ℓ1 relaxation, common in compressive sensing, to improve the performance of LMS-type adaptive methods. This results in two new algorithms, the Zero-Attracting LMS (ZA-LMS) and the Reweighted ZeroAttracting LMS (RZA-LMS). The ZA-LMS is derived via combining a ℓ1 norm penalty on the coefficients into the quadratic LMS cost function, which generates a zero attractor in the LMS iteration. The zero attractor promotes sparsity in taps during the filtering process, and therefore accelerates convergence when identifying sparse systems. We prove that the ZA-LMS can achieve lower mean square error than the standard LMS. To further improve the filtering performance, the RZA-LMS is developed using a reweighted zero attractor. The performance of the RZA-LMS is superior to that of the ZA-LMS numerically. Experiments demonstrate the advantages of the proposed filters in both...
Yilun Chen, Yuantao Gu, Alfred O. Hero III
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Authors Yilun Chen, Yuantao Gu, Alfred O. Hero III
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