[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.11003/JPNT.2022.11.1.45

Machine Learning-based UWB Error Correction Experiment in an Indoor Environment

Moon, Jiseon (Department of Electronic Engineering, Hanyang University)
Kim, Sunwoo (Department of Electronic Engineering, Hanyang University)

Publication Information

Journal of Positioning, Navigation, and Timing / v.11, no.1, 2022 , pp. 45-49 More about this Journal

Abstract

In this paper, we propose a method for estimating the error of the Ultra-Wideband (UWB) distance measurement using the channel impulse response (CIR) of the UWB signal based on machine learning. Due to the recent demand for indoor location-based services, wireless signal-based localization technologies are being studied, such as UWB, Wi-Fi, and Bluetooth. The constructive obstacles constituting the indoor environment make the distance measurement of UWB inaccurate, which lowers the indoor localization accuracy. Therefore, we apply machine learning to learn the characteristics of UWB signals and estimate the error of UWB distance measurements. In addition, the performance of the proposed algorithm is analyzed through experiments in an indoor environment composed of various walls.

Keywords

Ultra-wideband (UWB); machine learning;

Citations & Related Records

Reference

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