Convolutional Beamspace and Sparse Signal Recovery for Linear Arrays

Po Chih Chen, P. P. Vaidyanathan

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

1 Scopus citations

Abstract

The convolutional beamspace (CBS) method for DOA estimation using dictionary-based sparse signal recovery is introduced. Beamspace methods enjoy lower computational complexity, increased parallelism of subband processing, and improved DOA resolution. But unlike classical beamspace methods, CBS allows root-MUSIC and ESPRIT to be performed directly for ULAs without additional preparation since the Vandermonde structure for ULAs are preserved in the CBS output. Due to the same reason, it is shown in this paper that sparse signal representation problems can also be directly formulated on the CBS output. Significant reduction in computational complexity and higher probability of resolution are obtained by using CBS. It is also shown how the regularization parameter involved in the method should be chosen.

Original languageEnglish
Title of host publicationConference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages929-933
Number of pages5
ISBN (Electronic)9780738131269
DOIs
StatePublished - 1 Nov 2020
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: 1 Nov 20205 Nov 2020

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2020-November
ISSN (Print)1058-6393

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period1/11/205/11/20

Keywords

  • Convolutional beamspace
  • dictionaries
  • DOA estimation
  • linear sensor arrays
  • sparse signal recovery

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