NDLI: Distributed Sparse Recursive Least-Squares Over Networks

Content Provider	IEEE Xplore Digital Library
Author	Zhaoting Liu Ying Liu Chunguang Li
Copyright Year	1991
Abstract	Distributed estimation over networks has received much attention in recent years due to its broad applicability. Many signals in nature present high level of sparsity, which contain only a few large coefficients among many negligible ones. In this paper, we address the problem of in-network distributed estimation for sparse vectors, and develop several distributed sparse recursive least-squares (RLS) algorithms. The proposed algorithms are based on the maximum likelihood framework, and the expectation-maximization algorithm, with the aid of thresholding operators, is used to numerically solve the sparse estimation problem. To improve the estimation performance, the thresholding operators related to l0- and l1-norms with real-time self-adjustable thresholds are derived. With these thresholding operators, we can exploit the underlying sparsity to implement the distributed estimation with low computational complexity and information exchange amount among neighbors. The sparsity-promoting intensity is also adaptively adjusted so that a good performance of the sparse solution can be achieved. Both theoretical analysis and numerical simulations are presented to show the effectiveness of the proposed algorithms.
Sponsorship	IEEE Signal Processing Society
Starting Page	1386
Ending Page	1395
Page Count	10
File Size	3130749
File Format	PDF
ISSN	1053587X
Volume Number	62
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Signal processing algorithms Vectors Maximum likelihood estimation Least squares approximations Computational complexity Algorithm design and analysis expectation-maximization algorithm Wireless sensor network distributed estimation sparsity adaptive signal processing recursive least square
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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

Distributed Sparse Total Least-Squares Over Networks

Distributed Frequency Estimation Over Sensor Network

Distributed Local Linear Parameter Estimation Using Gaussian SPAWN

Diffusion Sparse Least-Mean Squares Over Networks

Information-Driven Distributed Maximum Likelihood Estimation Based on Gauss-Newton Method in Wireless Sensor Networks

Distributed Recursive Least-Squares: Stability and Performance Analysis

Distributed Recursive Least-Squares for Consensus-Based In-Network Adaptive Estimation

A Diffusion-Based EM Algorithm for Distributed Estimation in Unreliable Sensor Networks

Adaptive Distributed Estimation Based on Recursive Least-Squares and Partial Diffusion

Distributed Sparse Recursive Least-Squares Over Networks

Similar Documents

Distributed Sparse Total Least-Squares Over Networks

Distributed Frequency Estimation Over Sensor Network

Distributed Local Linear Parameter Estimation Using Gaussian SPAWN

Diffusion Sparse Least-Mean Squares Over Networks

Information-Driven Distributed Maximum Likelihood Estimation Based on Gauss-Newton Method in Wireless Sensor Networks

Distributed Recursive Least-Squares: Stability and Performance Analysis

Distributed Recursive Least-Squares for Consensus-Based In-Network Adaptive Estimation

A Diffusion-Based EM Algorithm for Distributed Estimation in Unreliable Sensor Networks

Adaptive Distributed Estimation Based on Recursive Least-Squares and Partial Diffusion

Distributed Sparse Recursive Least-Squares Over Networks