Atomic Norm Minimization Methods for Continuous DOA estimation in Colored Noise
Yuying Zhang, Gong Zhang, Henry Leung · 2019
This paper aims to solve the problem of continuous direction-of-arrival (DOA) estimation, and is focused on developing grid-less sparse methods to colored noise environment. We propose a two-stage grid-less model based on noise suppression and sparse representation. Then the simplified two-dimensional atomic norm minimization algorithm is proposed to estimate parameters. We further extend a reduced-complexity algorithm to the reconstruction of difference covariance matrix. The unknown DOAs are retrieved from the recovered matrix and the source number can be obtained as a byproduct. Numerical simulations lastly validate the computational efficiency of the proposed algorithms.