Hierarchical Sectorized ANN Model for DoA Estimation in Smart Textile Wearable Antenna Array Under Strong Noise Conditions

A novel hierarchical sectorized neural network module for a fast direction of arrival (DoA) estimation (HSNN-DoA) of the signal received by a textile wearable antenna array (TWAA) under strong noise conditions is presented. The developed DoA module accounts for variations in antenna element gain, inter-element spacing, and resonant frequencies under the conditions of textile crumpling caused by the motion of the TWAA wearer. The proposed model consists of a sector identification phase, which aims to determine the spatial sector in which the radio gateway (RG) is currently located based on the elements of the spatial correlation matrix of the signal sampled by the TWAA, and a DoA estimation phase, which aims to accurately determine the angular position of the RG in the azimuthal plane. The architecture of the HSNN-DoA module, with different time window lengths in which angular position of RG is recorded, is investigated and compared with the DoA module based on a stand-alone MLP network and the corresponding Root-MUSIC DoA module in terms of accuracy and speed of DoA estimation under variable noise conditions.