@inproceedings{1f64502126a344f39b531cb43c500e21,
title = "Locate UWB Smart Keys: Smart and Faster",
abstract = "This poster proposes an Ultra-wideband localization scheme for the smart key system based on a machine learning approach. Previous studies try to use the channel impulse response measurement and apply machine learning to improve the localization accuracy. However, it was found difficult to perform real-time localization in the key fob hardware as these studies require complex computation. In addition, localization accuracy is severely degraded due to frequent NLOS conditions in vehicular environments. In order to solve these problems, we propose vehicular features based on machine learning approach to improve localization accuracy and to support the real-time operation. We evaluate the proposed scheme by deploying Decawave UWB transceivers on an actual vehicle. The proposed scheme showed more than 98\% localization accuracy and an average prediction delay of 7.4 milliseconds.",
keywords = "UWB localization, location based service, smart key",
author = "Park, \{Ji Woong\} and Choi, \{Hong Beom\} and Ko, \{Young Bae\} and Lim, \{Keun Woo\}",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 22nd International Workshop on Mobile Computing Systems and Applications, HotMobile 2021 ; Conference date: 24-02-2021 Through 26-02-2021",
year = "2021",
month = feb,
day = "24",
doi = "10.1145/3446382.3448884",
language = "English",
series = "HotMobile 2021 - Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications",
publisher = "Association for Computing Machinery, Inc",
pages = "168--170",
booktitle = "HotMobile 2021 - Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications",
}