Experimental Investigation of Body-Centric Indoor Localization Using Compact Wearable Antennas and Machine Learning Algorithms

A simple and effective body-centric localization algorithm has been proposed in this article using ultra-wideband (UWB) wearable technology. The algorithm has been validated through measurement campaigns with a human subject volunteer in an indoor environment. The algorithm takes into account statis...

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Bibliographic Details
Published in:IEEE transactions on antennas and propagation Vol. 70; no. 2; pp. 1344 - 1354
Main Authors: Bharadwaj, Richa, Alomainy, Akram, Koul, Shiban K.
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Body-centric communication
channel characterization
Classification
Classification algorithms
Data integration
Delays
Human subjects
Indoor environment
Indoor environments
indoor localization
Localization
Location awareness
Machine learning
machine learning (ML)
Machine learning algorithms
Parameters
Support vector machines
Ultra wideband antennas
Ultrawideband
wearable antennas
Wearable technology
Wireless communication
ISSN:0018-926X, 1558-2221
Online Access:Get full text
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Summary:A simple and effective body-centric localization algorithm has been proposed in this article using ultra-wideband (UWB) wearable technology. The algorithm has been validated through measurement campaigns with a human subject volunteer in an indoor environment. The algorithm takes into account statistical channel parameter analysis, machine learning (ML) algorithms, and time-of-arrival (TOA)-based range estimation/data fusion techniques. Two channel parameters namely path loss magnitude and rms delay spread are proposed as classification features to be applied to the ML algorithm to accurately classify the off-body channel links into LOS, PNLOS, and NLOS scenarios. Multiclass support vector machine (MC-SVM) classifier along with SMOTE algorithm to take into account class imbalance is applied with the channel classification accuracy of 98.63%. Threshold-based range estimation algorithms are applied in order to mitigate NLOS scenarios caused mainly due to the presence of the human subject. Results report human localization accuracy in 0.5-3 cm range using TDOA data fusion technique for target estimation. Further validation is presented considering wide range of Tx-Rx distance, presence of another obstruction between the Tx and Rx links, and performance in different environment which shows the suitability of the proposed methodology.
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ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2021.3111308