A Fast and Efficient Beamforming Algorithm Imitating Plant Growth Gene for Phased Array Antenna

Abstract

In this paper, a new beamforming algorithm for phased array antennas is proposed, the plant growth gene algorithm. The algorithm consists of three steps. Firstly, according to the excitation relation of the array unit before and after the local fine-tuning of the antenna radiation pattern, the model for solving the array unit excitation difference is established. Secondly, the Taylor series expansion is used to solve the model, and the growth model is established based on this, and the beam tuning network is designed to realize the growth model. Finally, based on the growth gene obtained by the neural network algorithm, the growth model is called multiple times for high-precision beamforming. This algorithm converts the complex optimization process of array antenna excitation by the classical optimization algorithm into a simple process of fine-tuning the gain at any angle on the beam to make it grow and approach the target pattern. The growth gene is used to weigh the target angle and gain to achieve beamforming, which greatly reduces the complexity of the algorithm and improves its accuracy of the algorithm. Taking a 1 × 16 linear array as an example, a cosecant square beam pattern with a coverage range of −31° to 31° and a maximum gain direction of 17° is designed using the algorithm proposed in this paper. The experimental results show that the proposed algorithm can easily fine-tune the gain of any angle to achieve precise beamforming. Importantly, the growth genes trained by the algorithm are universal to the phased array antenna with the same topology.