A Sparse Representation Based Method for DOA Estimation Based in Nonuniform Noise

Abstract

In this paper, a new method for direction-of-arrival (DOA) estimation in unknown nonuniform noise based on iterative noise covariance and noise-free covariance matrix estimation and sparse representation is proposed. More specifically, in the first stage, the noise covariance matrix and noise-free covariance matrix are iteratively estimated through a weighted least square (WLS) minimization problem. Next, the DOA estimation problem is reduced to a sparse reconstruction problem with nonnegativity constraint by exploiting the sparsity of the prewhitened noise- free covariance matrix after vectorization. Numerical examples are conducted to validate the effectiveness and superior performance of the proposed approach over the existing sparsity-aware methods we have tested.