Error Analysis of Convolutional Beamspace Algorithms

Po Chih Chen*, P. P. Vaidyanathan

*Corresponding author for this work

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

1 Scopus citations

Abstract

Beamspace processing for DOA estimation offers low computational complexity and high DOA resolution. Moreover, unlike classical beamspace methods, convolutional beamspace (CBS) preserves the Vandermonde structure of uniform linear array output, so no additional preparation is needed to apply root-MUSIC. In this paper, theoretical MSE of CBS is given when MUSIC or root-MUSIC is used. Error variance can be derived from the asymptotic probability distribution of the eigenvectors of an average finite-snapshot covariance matrix. Meanwhile, the bias due to the filtered stopband sources is given by first-order perturbation analysis. Known advantages of CBS are confirmed by the MSE analysis. For example, CBS yields smaller MSE for correlated sources than element-space. The theoretical results are verified by simulations.

Original languageEnglish
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728163277
DOIs
StatePublished - 2023
Event48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2023-June
ISSN (Print)1520-6149

Conference

Conference48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/TerritoryGreece
CityRhodes Island
Period4/06/2310/06/23

Keywords

  • Convolutional beamspace
  • DOA estimation
  • MSE
  • MUSIC

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