NDLI: Sparse LMS with segment zero attractors for adaptive estimation of sparse signals

Content Provider	IEEE Xplore Digital Library
Author	Jie Yang Sobelman, G.E.
Copyright Year	2010
Description	Author affiliation: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, 55455 USA (Jie Yang; Sobelman, G.E.)
Abstract	Adaptive sparse signal estimation is needed for obtaining accurate channel knowledge in communication systems where the system response can be assumed to contain many near-zero coefficients. For sparse filter design, the zero-attracting LMS (ZA-LMS) incorporates the l1 norm penalty function into the quadratic LMS cost function to promote the sparseness during the adaptation process. The reweighted ZA-LMS (RZA-LMS) is developed using reweighted zero attractors with better performance. In this paper, we propose two new sparse LMS algorithms with segment zero attractors, referred as Segment RZA-LMS and Discrete Segment RZA-LMS. The Segment RZA-LMS outperforms RZA-LMS by using a piece-wise approximation of the reciprocal in the iterative algorithm of RZA-LMS. The Discrete Segment RZA-LMS is further developed to achieve faster convergence speed and lower steady state error performance than Segment RZA-LMS.
Starting Page	422
Ending Page	425
File Size	292065
Page Count	4
File Format	PDF
ISBN	9781424474547
e-ISBN	9781424474561
DOI	10.1109/APCCAS.2010.5774742
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-06
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares approximation Adaptive filters Convergence Filtering algorithms Steady-state Signal processing algorithms Speech processing system identification compressive sensing Least Mean Square (LMS) l1 norm sparse signals
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Sparse LMS for system identification

Transform domain LMS algorithms for sparse system identification

Least mean square/fourth algorithm for adaptive sparse channel estimation

A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filtering Framework

Extra gain: Improved sparse channel estimation using reweighted ℓ1-norm penalized LMS/F algorithm

Speech processing for makhraj recognition

Adaptive identification of sparse systems with variable sparsity

Electrical signals parameter estimation using adaptive filtering: A comparative study

Performance analysis of L-LMS with Gaussian input signal

Sparse LMS with segment zero attractors for adaptive estimation of sparse signals

Similar Documents

Sparse LMS for system identification

Transform domain LMS algorithms for sparse system identification

Least mean square/fourth algorithm for adaptive sparse channel estimation

A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filtering Framework

Extra gain: Improved sparse channel estimation using reweighted ℓ1-norm penalized LMS/F algorithm

Speech processing for makhraj recognition

Adaptive identification of sparse systems with variable sparsity

Electrical signals parameter estimation using adaptive filtering: A comparative study

Performance analysis of L-LMS with Gaussian input signal

Sparse LMS with segment zero attractors for adaptive estimation of sparse signals