Abstract
The problem of source localization is addressed for sparse arrays, which have the special array geometry to increase the degree of freedom (DOF), and a nonnegative sparse signal recovery (SSR) problem is formulated for the virtual array response model of sparse arrays. A novel method is developed in the framework of nonnegative sparse Bayesian learning (NNSBL), which obviates presetting any hyperparameter, and an expectation-maximization (EM) algorithm is exploited for solving this NNSBL problem. Without a priori knowledge of the source number, the proposed method yields superior performances in the underdetermined condition illustrated by numerical simulations.
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