Machine Learning-Based Angle of Arrival Estimation for Ultra-Wide Band Radios

Abstract

This letter analyzes the feasibility of deep convolutional neural networks (DCNN) for accurate ultra-wideband (UWB) angle of arrival estimation that is robust against hardware imperfections. To this end, a uniform linear array with four antenna elements is leveraged and a DCNN approach is proposed and compared with traditional approaches, such as MUSIC and phase difference of arrival estimators, for different environments, number of available channel impulse responses, and polarization mismatches, in terms of absolute value of error and computational complexity. The proposed approach outperforms the traditional approaches up to 80 degrees error reduction at a computational complexity increase of only 10% compared to MUSIC.