Year: 2024
Type: article
Abstract: Recently, wireless communication-based localization has been extensively studied due to the increasing demand for various indoor location-based services. However, this technology experiences performance degradation due to obstacles that block the line-of-sight (LOS) path between devices. This study introduces a machine learning algorithm designed to enhance the performance of ultra-wideband (UWB)-based vehicle key localization services. Through experiments conducted in simulated vehicle environments using the collected UWB signal strengths, a deep neural network (DNN)-based deep learning model was developed, and its performance was evaluated against existing algorithms. In scenarios with obstacles in the communication path, the machine-learning-based algorithm demonstrated superior estimation accuracy compared to conventional algorithms. Thus, it was confirmed that machine learning, which focuses on recognizing the features of location-specific signal patterns, can significantly improve localization accuracy over traditional methods.
Authors: Suah Park, Hyunwoo Park, Kyeong-Ju Cha, Youngkyun Oh, Sunwoo Kim
Institution: Hanyang University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Award: G6650184477
OpenAlex ID: W4410176403