TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density
Lucie Klus,
Roman Klus,
Elena Simona Lohan,
Jari Nurmi,
Carlos Granell,
Mikko Valkama,
Jukka Talvitie,
Sven Casteleyn,
Joaquín Torres-Sospedra
Affiliations
Lucie Klus
Electrical Engineering Unit, Tampere University, 33720 Tampere, Finland; Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellόn de la Plana, Spain; Corresponding author.
Roman Klus
Electrical Engineering Unit, Tampere University, 33720 Tampere, Finland
Elena Simona Lohan
Electrical Engineering Unit, Tampere University, 33720 Tampere, Finland
Jari Nurmi
Electrical Engineering Unit, Tampere University, 33720 Tampere, Finland
Carlos Granell
Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellόn de la Plana, Spain
Mikko Valkama
Electrical Engineering Unit, Tampere University, 33720 Tampere, Finland
Jukka Talvitie
Electrical Engineering Unit, Tampere University, 33720 Tampere, Finland
Sven Casteleyn
Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellόn de la Plana, Spain
Joaquín Torres-Sospedra
Departament d'Informàtica, Universitat de València, 46100 Burjassot, València, Spain
Positioning in indoor scenarios using signals of opportunity is an effective solution enabling accurate and reliable performance in Global Navigation Satellite System (GNSS)-obscured scenarios. Despite the availability of numerous fingerprinting datasets utilizing various wireless signals, the challenge of device heterogeneity and sample density remains an unanswered issue. To address this gap, this work introduces TUJI1, an anonymized IEEE 802.11 Wireless LAN (Wi-Fi) fingerprinting dataset collected using 5 different commercial devices in a fine-grained grid. The dataset contains the matched fingerprints of Received Signal Strength Indicator (RSSI) measurements with the corresponding coordinates, split into training and testing subsets for effortless and fair reproducibility.
