Register    Attendee Sign In Sign in to access papers, presentations, photos and videos

Session A4: Positioning Technologies and Machine Learning


Date: Thursday, September 14, 2023
Time: 1:45 p.m. - 5:30 p.m.
Location: Capitol Ballroom 1-3 (Fourth Floor)
In-Person presenters in this session provide pre-recorded presentations for viewing by registered attendees on Wednesday, September 13.

Session Chairs

Dr. Diana Fontanella
AirBus Defence and Space

Dr. Naser El-Sheimy
University of Calgary

Track Chair

Deborah Lawrence
Federal Aviation Administration

Sign in to:

  • Add papers to your schedule
  • Download full papers and presentations

In-Person Presentations
These presentations will be given in-person at the conference. Presenters will provide a pre-recorded presentation for on-demand viewing by virtual attendees.
1:50 High-solar Activity Ionospheric Modelling Using Machine Learning: A comparison Against Classical Models
Shishir Priyadarshi, Wahyudin P. Syam, Andrés Abelardo García Roqué, Alejandro Pérez Conesa, GMV; Guillaume Buscarlet, Raül Orús Pérez, Mickael Dall’Orso, European Space Agency (ESA)
2:12 Assessing Machine Learning Approach for GNSS Satellite Orbit Prediction
Kannan Selvan, Akpojoto Siemuri, Fabricio S. Prol, Petri Välisuo, Heidi Kuusniemi, University of Vaasa
2:35 Ionospheric VTEC Map Forecasting based on Graph Neural Network with Transformers
Ruirui Liu, Yiping Jiang, Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
2:58 A Deep Learning Approach for an Online Ionospheric Delay Forecasting Map System
Andre L.A. Silva, Moises S. Freitas, Instituto Tecnológico de Aeronáutica; Clodoaldo Faria Jr., Universidade Estadual Paulista; Paulo R.P. Silva, Instituto Tecnológico de Aeronáutica; Alison O. Moraes, Instituto de Aeronáutica e Espaço; Bruno C. Vani, Instituto Federal de Educação, Ciência e Tecnologia de São Paulo; Jonas Sousasantos, The University of Texas at Dallas; João F.G. Monico, Universidade Estadual Paulista
3:25 - 3:55, Break. Refreshments in Exhibit Hall
4:00 Transformer Deep Learning for Accurate Orbit Corrections in Real-Time
Wahyudin P. Syam, Shishir Priyadarshi, Andrés Abelardo García Roqué, Alejandro Pérez Conesa, GMV; Guillaume Buscarlet, Mickael Dall’Orso, European Space Agency (ESA)
4:23 Deep Learning in GNSS Orbit and Clock Extended Predictions to Improve the Accuracy and Robustness of Positioning
Li-Hsiang Chu, Yin-Lien Lo, Yao-cheng Lin, Shi-Xian Yang, AIROHA Technology (Company of MediaTek Group)
4:46 Tightly Coupled Graph Neural Network and Kalman Filter for Smartphone Positioning
Adyasha Mohanty and Grace Gao, Stanford University Best Presentation
5:08 First Real-World Results of a Deep Neural Network Assisted GNSS/INS Kalman-Filter with MEMS Inertial Sensors for Autonomous Vehicle
Shuo Li, Robert Bosch GmbH and Bundeswehr University Munich; Maxim Mikhaylov, ITMO University; Nikolay Mikhaylov, Robert Bosch GmbH; Thomas Pany, Bundeswehr University Munich
Alternate Presentation
Alternate presentations may be given in-person at the conference if other authors are unable to present. Alternate Presenters will provide a pre-recorded presentation for on-demand viewing by virtual attendees.
2. GSSC Now: Data-Centric Digital Platform to Boost Exploitation of GNSS Science Opportunities
Vicente Navarro, ESA; Sara del Rio, Luis Mendes, Jordi Prados, Emilio Fraile, RHEA for ESA; Maria del Mar Millán, Alain Messina, Miguel Barragán, Marcos Castro, GMV; Javier Ventura-Traveset, ESA
Virtual Presentations
Pre-recorded presentations will become available for viewing by registered attendees on Wednesday, September 13.
  A Robust RF Fingerprint Extraction Scheme for GNSS Spoofing Detection
Chengjun Guo, Zhongpei Yang, University of Electronic Science and Technology of China (UESTC)
  Effectiveness of Neural Network Approaches for the Acquisition of Non-Periodic Spreading Codes
Marco Trombini, Davide Leone, Angelo Bruno, Marco D’Addezio, Gianluca Falco, Emanuela Falletti, Leonardo S.p.A.
  Efficient Graph Neural Network Driven Recurrent Reinforcement Learning for GNSS Position Correction
Haoli Zhao, Jianhao Tang, Zhenni Li, Zhuoyu Wu, School of Automation, Guangdong University of Technology; Shengli Xie, Guangdong Key Laboratory of IoT Information Technology; Zhaofeng Wu, Techtotop Microelectronics Technology Co. Ltd; Ming Liu, Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, China; Banage T.G.S. Kumara, Department of Computing and Information Systems, Sabaragamuwa University of Sri Lanka
  Incremental Learning for LOS/NLOS Classification of Global Navigation Satellite System
Yuan Sun, Shang Li, Zhongliang Deng, School of Electronics Engineering, Beijing University of Posts and Telecommunications