Two Nonparametric Direction-of-Arrival Estimation Approaches for Coprime Arrays via Iteration

Abstract

Coprime arrays can highly improve resolution and increase degrees-of-freedom (DOFs) by exploiting the corresponding virtual array. In the context of direction-of-arrival (DOA) estimation using coprime arrays, a number of sparsity-based approaches have been developed, but the tedious process of selecting a regularization parameter is inevitable. To avoid this problem, in this paper, we apply two nonparametric and hyperparameter-free approaches named, respectively, iterative adaptive approach (IAA) and sparse iterative covariance-based estimation approach (SPICE) to DOA estimation. Numerical experiments validate the effectiveness of the two iterative algorithms when turning to virtual array domain, and their superiority is demonstrated in term of spatial spectrum, estimation accuracy and computational complexity.