ArticleScientific data2025
Comprehensive outdoor UWB dataset: Static and dynamic measurements in LOS/NLOS environments.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- MILUV: A Multi-UAV Indoor Localization dataset with UWB and Vision.The International journal of robotics research · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.