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综合户外超宽带数据集:视距/非视距环境中的静态和动态测量

Comprehensive outdoor UWB dataset: Static and dynamic measurements in LOS/NLOS environments.

作者信息

Lee Byeonghyun, Choi Jeik, Jeong Siheon, Choi JungHun, Oh Ki-Yong

机构信息

Department of Mechanical Convergence Engineering, Hanyang University, 222 Wangsimni-ri, Seongdong-gu, Seoul, 04763, Republic of Korea.

ZERO1NE Incubation Team 'WhereB Project', Hyundai Motor Company, 37 Cheoldobangmulgwan-ro, Uiwang-si, Gyeonggi-do, 16082, Republic of Korea.

出版信息

Sci Data. 2025 Sep 2;12(1):1532. doi: 10.1038/s41597-025-05887-9.

Abstract

This study provides a comprehensive outdoor ultra-wideband (UWB) dataset to examine the multipath effects in line-of-sight and non-line-of-sight (NLOS) environments for real-time localization. Specifically, the dataset comprises static and dynamic datasets designed to capture discrete multipaths affected by antenna height, obstructions, and time-varying environments. A static dataset varies the antenna height and distance to analyze the multipath interference on the received signal strength and ranging error with a UWB pair and walls to replicate NLOS environments. These measurements reveal ranging errors from ground reflections and Fresnel zone signal attenuations depending on the antenna height, which should be prevented in anchor deployment. The dynamic dataset includes IMU, GNSS, and UWB measurements collected using a unique mobile robot system under various anchor configurations. This dataset aims to facilitate the evaluation of localization methods against high-accuracy RTK-GNSS. Two representative methods, least-squares and an error-state Kalman filter, are provided to support localization improvements. These datasets have accelerated the study of real-time localization because these datasets are the novel datasets measured in various outdoor environments.

摘要

本研究提供了一个全面的室外超宽带(UWB)数据集,用于研究视距和非视距(NLOS)环境中的多径效应,以实现实时定位。具体而言,该数据集包括静态和动态数据集,旨在捕获受天线高度、障碍物和时变环境影响的离散多径。静态数据集改变天线高度和距离,以分析超宽带天线对接收信号强度的多径干扰以及与墙壁的测距误差,以复制非视距环境。这些测量揭示了取决于天线高度的地面反射和菲涅尔区信号衰减所导致的测距误差,在锚点部署中应避免这种情况。动态数据集包括使用独特的移动机器人系统在各种锚点配置下收集的惯性测量单元(IMU)、全球导航卫星系统(GNSS)和超宽带测量数据。该数据集旨在促进针对高精度实时动态(RTK)-GNSS的定位方法评估。提供了两种代表性方法,即最小二乘法和误差状态卡尔曼滤波器,以支持定位改进。这些数据集加速了实时定位的研究,因为这些数据集是在各种室外环境中测量的新颖数据集。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59e4/12405495/84cd24a78d0c/41597_2025_5887_Fig1_HTML.jpg

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