Decoupled Azimuth Elevation AoA Estimation Exploiting Kronecker Separable Steering Matrices

TL;DR

This paper introduces a novel subspace decoupling framework for 2D AoA estimation that leverages the Kronecker structure of array steering matrices, improving accuracy and efficiency over existing methods.

Contribution

It develops an economical decoupling scheme exploiting array steering matrix structure, enabling independent 1D algorithms for 2D AoA estimation with higher accuracy and fewer snapshots.

Findings

Achieves higher accuracy than state-of-the-art methods in simulations.

Requires fewer snapshots, improving spectral efficiency.

Effective for medium- and large-scale arrays.

Abstract

Uniform rectangular arrays (URA), structured non-uniform rectangular arrays (NURA), and parallelogram shaped (UPgA and NUPgA) arrays admit steering vectors that can be expressed as the Kronecker product of azimuth and elevation steering vectors. Accordingly, the full steering matrix can be represented as the Khatri Rao product of the corresponding azimuth and elevation steering matrices. This paper exploits this structure to develop an economical subspace decoupling framework for two dimensional angle of arrival (AoA) estimation. The proposed method first extracts the joint signal subspace from the spatial covariance matrix. Then it applies a low complexity decoupling scheme to recover the column spaces of the azimuth and elevation steering matrices. With the estimated decoupled subspaces, conventional one dimensional algorithms such as MUSIC, root MUSIC, and ESPRIT can be applied independently along each dimension, followed by pairing through a two dimensional spectral function. Monte Carlo simulations show that the proposed approach achieves higher accuracy than state of the art methods, i.e., two dimensional MUSIC, reduced-dimension MUSIC, and two-dimensional ESPRIT, for medium- and large scale arrays while requiring fewer snapshots, consequently with improved spectral efficiency.