DOA estimation in the presence of unknown nonuniform noise without knowing the number of sources

Abstract

A new direction-of-arrival (DOA) estimation method in the presence of unknown nonuniform noise is presented. In this scheme, we first construct a noise-free vector data model by sum-average arithmetic and removing operation in second-order statistics domain. And then, we obtain the DOA estimation by finding the sparsest coefficients of the constructed vector data model utilising reweighed -norm minimisation in an overcomplete basis. We utilise a statistical technique called leave-one-out cross-validation to select the regularisation parameter properly. Compared with the existing methods, the proposed method avoids the estimation of noise covariance matrix and gains the salient advantages, including high resolution and good robustness to noise. Furthermore, the number of sources need not be known a prior. Simulation results validate the effectiveness of the proposed method and show that it is better suited for dealing with unknown nonuniform noise.