Neural Network-Based Phase Estimation for Antenna Array Using Radiation Power Pattern
Tetsuya Iye, Pieter van Wyk, Takahiro Matsumoto, Yuki Susukida, Shohei Takaya, Yoshimi Fujii
Abstract
In this letter, a neural network-based interelement phase estimation method using radiation power pattern of the linear phased array is proposed. To validate the proposed method, a radiation pattern measured in an anechoic chamber is input to the neural network to estimate the initial phase errors, and to confirm practical estimation accuracy. The proposed method requires only single radiation pattern measurement and no additional measurements only for estimation. This indicates the proposed method is significantly more time-saving, compared to other conventional techniques. Furthermore, we propose a method to suppress the failure rate of estimation by recursively reinputting patterns into the neural network, and discuss its effectiveness. These results show that the proposed methods useful for phase estimation of the linear array in experiments.