Nonuniform norm based method for sparse signal recovery

Fei Yun Wu, Kunde Yang, Feng Tong, Yang Hu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Nonuniform norm constraint (NNC)-based methods have numerous potential applications for sparse signal recovery from a small number of measurements. In this study, we propose a novel NNC sparse recovery algorithm. First, a particular solution is attained by the gradient-descent-like method, which searches for the minimum NNC solution. Second, general solutions can be derived in the framework of underdetermined linear systems, wherein the pseudo-inverse matrix can be obtained using the QR decomposition via Givens rotations. Numerical simulations are conducted to verify the superior results with regard to sparsity adaptability, computation time, and recovered signal-to-noise ratio.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538631409
DOIs
StatePublished - 29 Dec 2017
Event7th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2017 - Xiamen, Fujian, China
Duration: 22 Oct 201725 Oct 2017

Publication series

Name2017 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2017
Volume2017-January

Conference

Conference7th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2017
Country/TerritoryChina
CityXiamen, Fujian
Period22/10/1725/10/17

Keywords

  • gradient projection
  • Nonuniform norm
  • sparse signal recovery

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