Sparse planar arrays for azimuth and elevation using experimental data

Santosh Nannuru*, Peter Gerstoft, Guoli Ping, Efren Fernandez-Grande

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Sparse arrays are special geometrical arrangements of sensors which overcome some of the drawbacks associated with dense uniform arrays and require fewer sensors. For direction finding applications, sparse arrays with the same number of sensors can resolve more sources while providing higher resolution than a dense uniform array. This has been verified numerically and with real data for one-dimensional microphone arrays. In this study the use of nested and co-prime arrays is examined with sparse Bayesian learning (SBL), which is a compressive sensing algorithm, for estimating sparse vectors and support. SBL is an iterative parameter estimation method and can process multiple snapshots as well as multiple frequency data within its Bayesian framework. A multi-frequency variant of SBL is proposed, which accounts for non-flat frequency spectra of the sources. Experimental validation of azimuth and elevation [two-dimensional (2D)] direction-of-arrival (DOA)estimation are provided using sparse arrays and real data acquired in an anechoic chamber with a rectangular array. Both co-prime and nested arrays are obtained by sampling this rectangular array. The SBL method is compared with conventional beamforming and multiple signal classification for 2D DOA estimation of experimental data.

Original languageEnglish
JournalJournal of the Acoustical Society of America
Volume149
Issue number1
Pages (from-to)167-178
ISSN0001-4966
DOIs
Publication statusPublished - 1 Jan 2021

Bibliographical note

Funding Information:
This work was supported by the Office of Naval Research, Grant No. N00014-18-1-2118 (P. G.) and Department of Science and Technology INSPIRE Faculty Award, IFA-17-ENG-236 (S. N.).

Publisher Copyright:
© 2021 Acoustical Society of America.

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