|Title||Multiple and single snapshot compressive beamforming|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Gerstoft P, Xenaki A., Mecklenbrauker C.F|
|Journal||Journal of the Acoustical Society of America|
|Type of Article||Article|
|Keywords||frequency-domain; inversion; lasso; location; path; selection; shallow-water; source localization; sparsity|
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 l(1)-norm constraint renders the problem solvable with convex optimization, and promoting sparsity gives high-resolution 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. (C) 2015 Acoustical Society of America.