IEEE JOURNAL OF

Indoor and Seamless Positioning
and Navigation (J-ISPIN)

The IEEE Journal of Indoor and Seamless Positioning and Navigation (J-ISPIN) publishes original research in the fields of localization and tracking of people, robots and objects. It covers all aspects of localization systems, including sensing, communications, location-based services, mapping, protocols, human interfaces and standards. The scope includes methods and systems addressing indoor environments as well as those enabling seamless transition between heterogeneous indoor contexts or between indoor and outdoor environments, for example where Global Navigation Satellites Systems are underperforming or unavailable.

Recent News

J-ISPIN has awarded the winners of the IPIN conference with the best paper award and looks forward to publishing their papers in the IEEE open access journal shortly.

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J-ISPIN is happy to announce that the ūü•á 3rd place for the Best Paper of the IPIN23 conference is awarded to Haige Chen and Ashutosh Dhekne of the Georgia Institute of Technology

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J-ISPIN is proud to be a technical sponsor of the Indoor Positioning and Indoor Navigation Conference and to offer publication fees to authors of top 6 papers

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Chiara Pileggi was awarded Best Early Stage Research at the European Navigation Conference (ENC) 2023

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To spread the word

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Fee waiver for the best paper in J-ISPIN scope at ENC23

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Recent Early Access Articles

Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

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Towards Low-Cost Passive Motion Tracking with One Pair of Commodity Wi-Fi Devices

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Multi-hop Self-calibration Algorithm for ultra-wideband (UWB) Anchor Node Positioning

Twentythree ultra-wideband anchors collaborate to calibrate themselves in challenging industrial conditions with limited connectivity. The algorithm is able to localize the anchors with an mean absolute error of only 21.6 cm.

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Toward Seamless Localization: Situational Awareness using UWB Wearable Systems and Convolutional Neural Networks

Necessity of environment detection in a seamless localization scenario, where the mobile object moves in different environments and uses various positioning methods. For example:
1) crowded urban area, where (s)he uses 5G signals for positioning,
2) indoor building, where (s)he utilizes camera and map-based method for localization,
3) open area, where the pedestrian deploys GNSS receivers to locate itself.

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Analysis of the Recent AI for Pedestrian Navigation With Wearable Inertial Sensors

AI improves wearable inertial device-based pedestrian navigation. This paper classifies these AI methods into two categories based on signal segmentation methods: human gait and sampling frequency-driven. To complete the analysis, SELDA (category 1), RONIN (category 2) and a non-AI gait-driven method SmartWalk were evaluated in a 2,17 km long open access dataset, representative of the diversity of pedestrians’ mobility surroundings.

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Editorial Board

Valérie Renaudin
Editor in Chief

Univ. Gustave Eiffel
Bouguenais, France

Francesco Potortì
Associate Editor in Chief

ISTI-CNR
Pisa, Italy

Crivello Antonino
Editor

ISTI-CNR
Pisa, Italy

Paolo Barsocchi
Editor

ISTI-CNR
Pisa, Italy

Valérie Gyselinck
Editor

Univ. Gustave Eiffel
Versailles, France

Suining He
Editor

University of Connecticut
United States

Itzik Klein
Editor

University of Haifa
Israel

Elena Simona Lohan
Editor

Tampere University
Tampere, Finland

Luca De Nardis
Editor

Sapienza Università di Roma
Roma, Italy

Kyle O’keefe
Editor

University of Calgary
Calgary, AB, Canada

Neal Patwari
Editor

Washington University St. Louis
Missouri, USA

Ling Pei
Editor

Shanghai Jiao Tong University
Shanghai, China

Isaac Skog
Editor

Linköping University
Linköping, Sweden

Joaquín Torres Sospedra
Editor

Universidade do Minho
Guimar√£es, Portugal

Masanori Sugimoto
Editor

Hokkaido University
Sapporo, Japan

Niki Trigoni
Editor

University at Oxford
Oxford, United Kingdom

Jes√ļs Ure√Īa Ure√Īa
Editor

University of Alcal√°
Madrid, Spain

Dongyan Wei
Editor

AIR Chinese Academy of Sciences (CAS)
Beijing, China

Baoding Zhou
Editor

Shenzhen University
Shenzhen, China

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