A genetic algorithm based superdirective beamforming method under excitation power range constraints

TL;DR

This paper introduces a genetic algorithm-based superdirective beamforming method that achieves high directivity within excitation power constraints, using a closed-form solution derived from array electric field data.

Contribution

It presents a novel genetic algorithm approach with excitation range constraints for superdirective beamforming, improving directivity and beamwidth control.

Findings

Achieves greater directivity than traditional methods

Narrower beamwidth under power constraints

Validated by full-wave electromagnetic simulations

Abstract

The array gain of a superdirective antenna array can be proportional to the square of the number of antennas. However, the realization of the so-called superdirectivity entails accurate calculation and application of the excitations. Moreover, the excitations require a large dynamic power range, especially when the antenna spacing is smaller. In this paper, we derive the closed-form solution for the beamforming vector to achieve superdirectivity. We show that the solution only relies on the data of the array electric field, which is available in measurements or simulations. In order to alleviate the high requirement of the power range, we propose a genetic algorithm based approach with a certain excitation range constraint. Full-wave electromagnetic simulations show that compared with the traditional beamforming method, our proposed method achieves greater directivity and narrower beamwidth with the given range constraints.