Low-complexity Sparse Array Synthesis Based on Off-grid Compressive Sensing

TL;DR

This paper introduces a low-complexity off-grid compressive sensing method for sparse array synthesis that optimizes element positions and excitations while respecting physical constraints, improving computational efficiency and performance.

Contribution

It proposes novel off-grid refinement algorithms, OMP and LAOMP, for sparse array synthesis that address position optimization and physical constraints more effectively.

Findings

The proposed algorithms outperform existing methods in computational complexity.

The schemes achieve better synthesis performance in simulations.

The methods successfully incorporate minimum inter-element spacing constraints.

Abstract

A novel sparse array synthesis method for non-uniform planar arrays is proposed, which belongs to compressive sensing (CS)-based systhesis. Particularly, we propose an off-grid refinement technique to simultaneously optimize the antenna element positions and excitations with a low complexity, in response to the antenna position optimization problem that is difficult for standard CS. More importantly, we take into account the minimum inter-element spacing constraint for ensuring the physically realizable solution. Specifically, the off-grid Orthogonal Match Pursuit (OMP) algorithm is first proposed with low complexity and then off-grid Look Ahead Orthogonal Match Pursuit (LAOMP) is designed with better synthesis performance but higher complexity. In addition, simulation results have shown the proposed schemes have more advantages in computational complexity and synthesis performances compared with the related method.