V-Shaped Sparse Arrays For 2-D DOA Estimation

TL;DR

This paper introduces V-shaped sparse array geometries, VCA and VNA, for efficient 2-D DOA estimation, reducing sensor count while maintaining high resolution and computational efficiency.

Contribution

It proposes novel V-shaped sparse array structures for 2-D DOA estimation, enhancing degrees of freedom and reducing sensors needed compared to traditional arrays.

Findings

VCA resolves up to MN sources with 2M+N-1 sensors.

VNA can resolve O(N^2) sources with 2N sensors.

The method reduces computational complexity by avoiding 2-D grid search.

Abstract

This paper proposes a new sparse array geometry for 2-D (azimuth and elevation) DOA (direction-of-arrival) estimation. The proposed array geometry is V-shaped sparse array and it is composed of two linear portions which are crossing each other. The degrees of freedom of the sparse array is enhanced by sparse sampling property. In this respect, V-shaped coprime (VCA) and V-shaped nested array (VNA) structures are developed. VCA can resolve both azimuth and elevation angles up to MN sources with 2M + N -1 sensors in each portion and the total number of sensors is 4M+2N-3. VNA can resolve O(N^2) sources with 2N sensors. Instead of 2-D grid search, the proposed method computes 1-D search for azimuth and elevation angle estimation in a computational efficient way. In order to solve the pairing problem in 2-D scenario, the cross-covariance matrix of two portion is utilized and 2-D paired DOA estimation is performed. The performance of the proposed method is evaluated with numerical simulations and it is shown that the proposed array geometries VCA and VNA can provide much less sensors as compared to the conventional coprime planar arrays.