LocaRDS: A Localization Reference Data Set
Matthias Sch\"afer, Martin Strohmeier, Mauro Leonardi, Vincent Lenders

TL;DR
LocaRDS is an extensive open dataset of real-world wireless signals designed to facilitate the evaluation and comparison of localization techniques, especially for aircraft positioning in crowdsourced sensor networks.
Contribution
This work introduces LocaRDS, a large-scale, real-world dataset for localization research, along with a reference implementation and evaluation framework.
Findings
LocaRDS contains over 222 million measurements from 50 million transmissions.
The dataset enables direct comparison of different localization methods.
Demonstrated effectiveness in aircraft localization using crowdsourced sensors.
Abstract
The use of wireless signals for purposes of localization enables a host of applications relating to the determination and verification of the positions of network participants, ranging from radar to satellite navigation. Consequently, it has been a longstanding interest of theoretical and practical research in mobile networks and many solutions have been proposed in the scientific literature. However, it is hard to assess the performance of these in the real world and, more severely, to compare their advantages and disadvantages in a controlled scientific manner. With this work, we attempt to improve the current state of the art in localization research and put it on a solid scientific grounding for the future. Concretely, we develop LocaRDS, an open reference dataset of real-world crowdsourced flight data featuring more than 222 million measurements from over 50 million transmissions…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Target Tracking and Data Fusion in Sensor Networks · Underwater Vehicles and Communication Systems
