Grid-free compressive beamforming

Abstract

The direction-of-arrival (DOA) estimation problem involves the localization of a few sources from a limited number of observations on an array of sensors, thus it can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve high-resolution imaging. On a discrete angular grid, the CS reconstruction degrades due to basis mismatch when the DOAs do not coincide with the angular directions on the grid. To overcome this limitation, a continuous formulation of the DOA problem is employed and an optimization procedure is introduced, which promotes sparsity on a continuous optimization variable. The DOA estimation problem with infinitely many unknowns, i.e., source locations and amplitudes, is solved over a few optimization variables with semidefinite programming. The grid-free CS reconstruction provides high-resolution imaging even with non-uniform arrays, single-snapshot data and under noisy conditions as demonstrated on experimental towed array data.

14 pages, 8 figures, journal paper

Cited by in corpus (14)

• A Survey of Sound Source Localization with Deep Learning Methods
• Multi Snapshot Sparse Bayesian Learning for DOA Estimation
• Gridless DOA Estimation and Root-MUSIC for Non-Uniform Arrays
• Gridless DOA Estimation with Multiple Frequencies
• Single Snapshot Super-Resolution DOA Estimation for Arbitrary Array Geometries
• Multiple and single snapshot compressive beamforming
• Inverse point source location with the Helmholtz equation on a bounded domain
• Alternating projections gridless covariance-based estimation for DOA
• Using the LASSO's Dual for Regularization in Sparse Signal Reconstruction from Array Data
• Sparse time-frequency representation via atomic norm minimization
• Localization of Sound Sources in a Room with One Microphone
• Efficient atom selection strategy for iterative sparse approximations
• Off-grid Multi-Source Passive Localization Using a Moving Array
• Super-Resolution DOA Estimation for Arbitrary Array Geometries Using a Single Noisy Snapshot