DOA Estimation and Beamforming Using Spatially Under-Sampled AVS Arrays

K. Nambur Ramamohan, M.A. Coutiño Minguez, S.P. Chepuri, D. Fernandez Comesana, G. Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

In this paper, we show the advantages of spatially under-sampled acoustic vector sensor (AVS) arrays over conventional acoustic pressure sensor (APS) arrays for performing direction-of-arrival (DOA) estimation and interference cancellation. We provide insights into the theoretical performance of an under-sampled AVS array with respect to its DOA estimation performance using the Cramér-Rao lower bound (CRLB). We also show that the minimum variance distortionless response (MVDR) beamformer suppresses the grating lobes considerably as compared to the classical (or Bartlett) beamformer leading to unambiguous DOA estimates. Finally, through zero-forcing (ZF) and minimization of maximum side lobe beamformers, the advantages of an under-sampled AVS array for interference cancellation are presented.
Original languageEnglish
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Place of PublicationPiscataway
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-5386-1251-4
ISBN (Print)978-1-5386-1252-1
DOIs
Publication statusPublished - Dec 2017
Event2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Willemstad, Curaçao
Duration: 10 Dec 201713 Dec 2017
Conference number: 7
http://www.cs.huji.ac.il/conferences/CAMSAP17/

Workshop

Workshop2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated titleCAMSAP
Country/TerritoryCuraçao
CityWillemstad
Period10/12/1713/12/17
Internet address

Keywords

  • acoustic vector sensor (AVS)
  • Cramér-Rao lower bound (CRLB
  • direction-of-arrival (DOA)
  • far-field narrow-band acoustic sources
  • spatial under-sampling

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