Effective Approximate Maximum Likelihood Estimation of Angles of Arrival for Non-Coherent Sub-Arrays

Abstract

We consider the problem of estimating the angles of arrival (AOAs) of multiple sources from a single snapshot obtained by a set of non-coherent sub-arrays, i.e., while the antenna elements in each sub-array are coherent, each sub-array observes a different unknown phase. Previous relevant works are based on eigendecomposition of the sample covariance, which requires a large number of snapshots, or on combining the sub-arrays using non-coherent processing methods. In this paper, we propose a technique to estimate the sub-arrays phase offsets for a given AOAs hypothesis, which facilitates approximate maximum likelihood estimation of the AOAs from a single snapshot. Numerical experiments show that the proposed approach clearly outperforms non-coherent processing, and even attains the Cramér-Rao lower bound in various scenarios.