Multiple and single snapshot compressive beamforming

Peter Gerstoft, Angeliki Xenaki, Christoph F. Mecklenbrauker

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

For a sound field observed on a sensor array, compressive sensing (CS) reconstructs the direction of arrival (DOA) of multiple sources using a sparsity constraint. The DOA estimation is posed as an underdetermined problem by expressing the acoustic pressure at each sensor as a phase-lagged superposition of source amplitudes at all hypothetical DOAs. Regularizing with an ‘1-norm constraint renders the problem solvable with convex optimization, and promoting sparsity gives highresolution DOA maps. Here the sparse source distribution is derived using maximum a posteriori estimates for both single and multiple snapshots. CS does not require inversion of the data covariance matrix and thus works well even for a single snapshot where it gives higher resolution than conventional beamforming. For multiple snapshots, CS outperforms conventional high-resolution methods even with coherent arrivals and at low signal-to-noise ratio. The superior resolution of CS is demonstrated with vertical array data from the SWellEx96 experiment for coherent multi-paths.
Original languageEnglish
JournalJournal of the Acoustical Society of America
Volume138
Issue number4
Pages (from-to)2003–2014
ISSN0001-4966
DOIs
Publication statusPublished - 2015

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