Abstract
A reweighted zero-attracting/repelling least-mean-squares (LMS) algorithm is proposed in this paper for sparse system identification, which attracts zero and near-zero coefficients towards zero, and repels large coefficients against zero. Specifically, an l 1 -norm constraint on the deviation between the unknown coefficient vector and its estimate is integrated into the original cost function of the LMS algorithm. This generates a correction term in the update equation, which always attracts the coefficients in the estimate towards their optimal values, instead of zero, as is the case in the existing zero-attracting type LMS algorithms. Simulation results demonstrate that the proposed algorithm outperforms the existing l 1 -norm-constraint sparsity-aware LMS algorithms in the identification of sparse systems.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
