A Computationally Efficient and Aliasing-Free Broadband DOA Estimation for Arbitrary Sparse Linear Arrays

Yang Long, Yang Yixin, Yu Mengling, Bangjie Zhou

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

Abstract

Spatial aliasing is an inevitable issue for sparse linear arrays with the intersensory spacing larger than the half wavelength of the incident signals. Here, a computationally efficient and aliasing-free broadband DOA estimation algorithm is proposed. The array output is first multiplied by a designed diagonal matrix. In this manner, the frequency of the broadband signals is shifted to a given frequency which approximatively satisfies the spatial Nyquist sampling theorem. Thus, the spatial aliasing is avoided. Then the proposed method is demonstrated to be applicable to arbitrary sparse linear arrays. Simulation results are included to show the anti aliasing property and the computational efficiency of the proposed method.

Original languageEnglish
Title of host publicationOCEANS 2023 - Limerick, OCEANS Limerick 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350332261
DOIs
StatePublished - 2023
Event2023 OCEANS Limerick, OCEANS Limerick 2023 - Limerick, Ireland
Duration: 5 Jun 20238 Jun 2023

Publication series

NameOCEANS 2023 - Limerick, OCEANS Limerick 2023

Conference

Conference2023 OCEANS Limerick, OCEANS Limerick 2023
Country/TerritoryIreland
CityLimerick
Period5/06/238/06/23

Keywords

  • aliasing-Free DOA estimation
  • arbitrary sparse linear arrays
  • broadband signals
  • spatial Nyquist sampling theorem

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