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<i title="Acoustical Society of America (ASA)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hwn3tbm3t5cpnjcflcmmwnprc4" style="color: black;">Journal of the Acoustical Society of America</a>
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<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1121/1.4916269">doi:10.1121/1.4916269</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25920844">pmid:25920844</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/244xdnlqbfholnltqonkswxdsi">fatcat:244xdnlqbfholnltqonkswxdsi</a> </span>
more »... nuous 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.
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