  This paper presents an efficient optimization technique for super-resolution
two-dimensional (2D) direction of arrival (DOA) estimation by introducing a new
formulation of atomic norm minimization (ANM). ANM allows gridless angle
estimation for correlated sources even when the number of snapshots is far less
than the antenna size, yet it incurs huge computational cost in 2D processing.
This paper introduces a novel formulation of ANM via semi-definite programming,
which expresses the original high-dimensional problem by two decoupled Toeplitz
matrices in one dimension, followed by 1D angle estimation with automatic angle
pairing. Compared with the state-of-the-art 2D ANM, the proposed technique
reduces the computational complexity by several orders of magnitude with
respect to the antenna size, while retaining the benefits of ANM in terms of
super-resolution performance with use of a small number of measurements, and
robustness to source correlation and noise. The complexity benefits are
particularly attractive for large-scale antenna systems such as massive MIMO
and radio astronomy.
