Descriptor: A Hybrid Indoor and Indoor-Outdoor Positioning Multi-Technology Dataset (HYMN)

TL;DR

HYMN is a comprehensive, multi-technology dataset capturing synchronized indoor-outdoor localization signals from five systems, supporting advanced multi-sensor fusion research.

Contribution

It introduces a novel multi-system, synchronized dataset covering indoor-outdoor transitions, enabling research on seamless multi-technology localization.

Findings

Includes measurements from UWB, BLE, WiFi, 5G, and GNSS in a single dataset.

Supports multi-sensor fingerprinting, cross-technology fusion, and seamless positioning.

Facilitates investigation of heterogeneous signals to improve localization robustness.

Abstract

This article introduces the HYMN (HYbrid Multi-technology Navigation) dataset: a multi-system, and time synchronized dataset for localization research based on opportunistic signals collected in an indoor-outdoor scenario. HYMN comprises measurement data collected in an industrial hall setting for five different positioning systems including Ultra-Wideband (UWB), Bluetooth Low Energy (BLE), WiFi, 5G, and Global Navigation Satellite System (GNSS). Unlike existing datasets that focus on single technologies or purely indoor/outdoor scenarios, HYMN combines five positioning technologies with explicit coverage of indoor-outdoor transitions, enabling multi-sensor fusion research for seamless localization. Each instance of data is identified through a unique measurement id and it represents time-stamped observations relevant for each system respectively along with the ground truth information. HYMN is designed to support a wide range of localization tasks including multi-sensor fingerprinting, cross-technology fusion, and seamless indoor-outdoor positioning. The synchronized measurements from GNSS and other terrestrial systems enable researchers to investigate how heterogeneous signals complement each other to overcome individual technology limitations such as GNSS degradation in covered areas or terrestrial system variability in dynamic environments.