Abstract
Sparse direction of arrival (DOA) estimation has become highly popular especially after the development of compressive sensing (CS) theory. For hardware and/or software efficiency, i t i s h ighly c ommon t o u ndersample s ensor array measurements using measurement matrices, and perform the DOA estimation on the compressed sensor array data. This paper demonstrates a comparative analysis among sparse DOA estimation, Multiple Signal Classification (MUSIC) and Capon's beamformer using a real acoustic data corpus. Although it operates on the compressed data, the sparse DOA estimation algorithm outperforms MUSIC and Capon's beamformer when an adaptive measurement matrix is used. The findings o f this study verify the feasibility of sparse DOA estimation algorithms in practical applications.
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