Wideband source localization using sparse learning via iterative minimization

• Two direction-of-arrival estimation algorithms for wideband sources are proposed.
• The algorithms are extensions of the SLIM algorithm designed for narrowband sources.
• The algorithms are extended to the localization scenario with vector-sensor arrays.
• The classical RELAX algorithm is incorporated to further refine the localization results.
• The wideband deterministic Cramer–Rao bound is derived.

In this paper, two extensions of the Sparse Learning via Iterative Minimization (SLIM) algorithm are presented for wideband source localization using a sensor array. The proposed methods exploit the joint sparse structure across all frequency bins, and estimate the spatial pseudo-spectra at various frequency bins jointly and iteratively. Via several numerical examples, we show that the proposed methods can provide high-resolution angle estimates and excellent source localization performance, and are able to resolve the left–right ambiguity problem, when used together with the vector sensor array technology.