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Related Experiment Video

Updated: Jan 16, 2026

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

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

Zoran Stanković¹, Olivera Pronić-Rančić¹, Nebojša Dončov¹

¹Faculty of Electronic Engineering, University of Niš, A. Medvedeva 4, 18000 Niš, Serbia.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

A new hierarchical sectorized neural network module (HSNN-DoA) accurately estimates signal direction of arrival (DoA) for textile wearable antenna arrays, even in noisy environments. This method accounts for antenna variations due to wearer movement.

Keywords:

Root MUSIC SNR artificial neural network (ANN)direction of arrival (DoA)multilayer perceptron (MLP)textile wearable antenna array (TWAA)

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Last Updated: Jan 16, 2026

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

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Published on: May 2, 2018

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

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Published on: July 30, 2020

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

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Published on: May 1, 2018

Area of Science:

Electrical Engineering
Signal Processing
Machine Learning

Background:

Direction of Arrival (DoA) estimation is crucial for wireless communication systems.
Wearable antenna arrays face challenges like signal noise and physical deformation.
Existing DoA methods struggle with dynamic and noisy environments.

Purpose of the Study:

To introduce a novel Hierarchical Sectorized Neural Network module for Direction of Arrival estimation (HSNN-DoA).
To address the challenges of DoA estimation using Textile Wearable Antenna Arrays (TWAA) under strong noise and crumpling conditions.
To evaluate the performance of the proposed HSNN-DoA module against existing methods.

Main Methods:

Development of a two-phase HSNN-DoA module: sector identification and DoA estimation.
Utilizing the spatial correlation matrix of signals sampled by the TWAA.
Investigating different time window lengths for the HSNN-DoA architecture.
Comparing HSNN-DoA with MLP-based and Root-MUSIC DoA modules.

Main Results:

The HSNN-DoA module demonstrates robust performance under variable noise conditions.
The proposed module effectively accounts for antenna gain, spacing, and frequency variations.
HSNN-DoA shows competitive accuracy and improved speed compared to benchmark methods.

Conclusions:

The HSNN-DoA module offers a promising solution for fast and accurate DoA estimation in challenging wearable scenarios.
The hierarchical approach effectively handles environmental variations and noise.
This work advances DoA estimation techniques for mobile and wearable communication systems.