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.
