What question did this study set out to answer?

The aim is to improve direction-of-arrival (DOA) estimation by recovering joint-sparse representations of signals.

May 26, 2010IEEE Transactions on Signal Processing324 citations

Direction-of-Arrival Estimation Using a Mixed ₂, ₀ Norm Approximation

MHMd Mashud HyderDhaka University of Engineering & Technology KMKaushik MahataUppsala University

Key Points

The aim is to improve direction-of-arrival (DOA) estimation by recovering joint-sparse representations of signals.
Minimized a mixed l2,0 norm approximation for joint-sparse recovery.
Addressed both narrowband and broadband scenarios.
Developed an algorithm that handles more sources without prior knowledge of their number.
Algorithm resolves closely spaced sources effectively using a small number of snapshots.
Achieves significant improvement in resolution limit by relaxing half-wavelength spacing restriction.
Outperforms state-of-the-art algorithms in handling higher source counts.

Abstract

A set of vectors is called jointly sparse when its elements share a common sparsity pattern. We demonstrate how the direction-of-arrival (DOA) estimation problem can be cast as the problem of recovering a joint-sparse representation. We consider both narrowband and broadband scenarios. We propose to minimize a mixed ℓ 2,0 norm approximation to deal with the joint-sparse recovery problem. Our algorithm can resolve closely spaced and highly correlated sources using a small number of noisy snapshots. Furthermore, the number of sources need not be known a priori . In addition, our algorithm can handle more sources than other state-of-the-art algorithms. For the broadband DOA estimation problem, our algorithm allows relaxing the half-wavelength spacing restriction, which leads to a significant improvement in the resolution limit.

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