Advanced ANN model for DoA estimation in smart textile wearable antenna array subsystem

Abstract

AbstractArtificial neural networks‐based module for fast direction‐of‐arrival (DoA) estimation of the signal received by multi‐element textile wearable antenna array (TWAA) is proposed in this paper. The developed multilayer perceptron (MLP) DoA module considers the effects of changing the gain of the antenna elements, the distance between the antenna elements and their resonant frequencies during the movement of the TWAA wearer and the crumpling of the textile. The inputs of the MLP DoA module are the elements of the spatial correlation matrix of the signal sampled by the TWAA and the output is the angular position of the signal source in the azimuthal plane. The influence of the number of TWAA elements on the accuracy of the MLP DoA module in DoA estimation under textile crumple conditions is investigated. The performances of the MLP DoA module under increased noise conditions are investigated, as well as its behavior under different degrees of textile crumpling. A comparison was made between the proposed module and the corresponding root MUSIC DoA module in terms of accuracy and speed of DoA estimation.