Session A4: Positioning Technologies and Machine Learning

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

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	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.
2:12	Evaluation of (Un-)Supervised Machine-Learning-Based Detection, Classification, and Localization Methods of GNSS Interference in the Real World : Tobias Feigl, Fraunhofer Institute for Integrated Circuits (IIS), & Friedrich-Alexander-Universität (FAU); Tobias Brieger, Felix Ott, Fraunhofer IIS, & Ludwig-Maximilians-Universität (LMU); Jonathan Hansen, David Contreras Franco, Alexander Rügamer, Wolfgang Felber, Fraunhofer IIS
2:35	Ionosphere 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
2:58	A Deep Learning Approach for a Real-Time Ionospheric Delay Forecasting Map System : Andre L.A. Silva - Instituto Tecnológico de Aeronáutica; 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 Real-Time Precise Orbit Corrections : 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 Improving Smartphone GNSS Positioning : Adyasha Mohanty and Grace Gao, Stanford University
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, Mohamed Bochkati, Bundeswehr University Munich
Alternate Presentations 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.
1.	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.	Assessing Machine Learning Approach for GNSS Satellite Orbit Prediction : Kannan Selvan, Akpojoto Siemuri, Fabricio S. Prol, Petri Välisuo, Heidi Kuusniemi, University of Vaasa
3.	Inter-System Bias Estimation Using the MAFA Method : Dawid Kwasniak, Slawomir Cellmer, The University of Warmia And Mazury in Olsztyn
4.	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 Machine Learning-based Approach for Correcting Cooperative DGNSS Differential Corrections : Guoqiang Zeng, Hongbo Zhao, Chen Zhuang, Shan Hu, Beihang University
	A Robust RF Fingerprint Extraction Scheme for GNSS Spoofing Detection : Chengjun Guo, University of Electronic Science and Technology of China (UESTC), China; Zhongpei Yang, University of Electronic Science and Technology of China (UESTC)
	Efficient Graph Neural Network driven Reinforcement Learning for GNSS Position Correction : Zhenni Li, Zhuoyu Wu, Haoli Zhao, School of Automation, Guangdong University of Technology; Shengli Xie, Guangdong Key Laboratory of IoT Information Technology; Qianming Wang, Techtotop Microelectronics Technology Co. Ltd.
	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

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