Machine Learning-based UWB Error Correction Experiment in an Indoor Environment
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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 locationbased 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.
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 locationbased 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.
참고문헌 (9)
- Bocus, M. J., "Low Cost Localisation in Residential Environments using High Resolution CIR Information", IEEE Global Commun. Conf, pp.1~6 (2020)
- Chen, Y. -Y., "UWB System for Indoor Positioning and Tracking with Arbitrary Target Orientation, Optimal Anchor Location, and Adaptive NLOS Mitigation", IEEE Trans. Veh. Technol, 69, pp.9304~9314 (2020)[DOI]
- Goodfellow, I., "Deep learning" (2016), MIT press
- Jiang C., "UWB NLOS", LOS Classification Using Deep Learning Method, pp.24, 2226~2230
- Laoudias, C., "A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation", IEEE Commun. Surveys Tuts, 20, pp.3607~3644 (2018)[DOI]
- Maranò, S., "NLOS identification and mitigation for localization based on UWB experimental data", IEEE J. Sel. Areas in Commun, 28, pp.1026~1035 (2010)[DOI]
- Stahlke M., "NLOS Detection using UWB Channel Impulse Responses and Convolutional Neural Networks", 2020 Int. Conf. Localization GNSS (ICL-GNSS), pp.1~6 (2020)
- Vapnik, V. N., "The nature of statistical learning theory", pp.181~218 (2000), Springer science & business media
- Win, M. Z., "On the robustness of ultra-wide bandwidth signals in dense multipath environments", IEEE Commun. Lett, 2, pp.51~53 (1998)[DOI]