Abstract
Several superresolution source localization algorithms based on the sparse signal reconstruction framework have been developed in recent years. These methods also offer other advantages such as immunity to noise coherence and robustness to reduction in the number of snapshots. The application of these methods is mostly limited to the problem of one dimensional (1-D) direction-of-arrival estimation. In this paper, we have developed 2-D and 3-D versions of two sparse signal reconstruction methods, viz. $\ell_{1}$-SVD and re-weighted $\ell_{1}$-SVD, and applied them to the problem of 3-D localization of underwater acoustic sources. A vertical linear array is used for estimation of range and depth and a horizontal cross-shaped array is used for bearing estimation. It is shown that the $\ell_{1}$-SVD and re-weighted $\ell_{1}$-SVD processors outperform the widely used MUSIC and Bartlett processors.
Published Version
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