Session C5: Positioning Technologies and Machine Learning

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Date: Friday, September 23, 2022
Time: 8:30 a.m. - 12:15 p.m.
In-Person presenters in this session may provide pre-recorded presentations for viewing by registered attendees.

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.
8:35	A Hybrid Numerical-Machine Learning Approach for LEO Satellite Orbit Prediction : Jamil Haidar-Ahmad and Zak (Zaher) M. Kassas, University of California, Irvine
8:57	GPS Spoofing Detection Using Machine Learning and Singular Value Decomposition : Logan L. Maynard, University of Colorado Colorado Springs
9:20	Machine Learning-assisted GNSS Interference Monitoring through Crowd-sourcing : Nisha Lakshmana Raichur, Tobias Brieger, Fraunhofer Institute for Integrated Circuits (IIS); Dorsaf Jdidi, Friedrich-Alexander-University (FAU), IIS; Carlo Schmitt, IIS; Tobias Feigl, IIS, FAU; J. Rossouw van der Merwe, Birendra Ghimire, Felix Ott, Alexander Ruegamer, Wolfgang Felber, IIS
9:43	Machine Learning Compression for GNSS Interference Analysis : Dorsaf Jdidi, Fraunhofer Institute for Integrated Circuits (IIS), Friedrich-Alexander-University (FAU), Germany; Luca Reeb, IIS, Germany; Tobias Brieger, IIS, FAU, Germany; Tobias Feigl, IIS, Germany; David Contreras Franco, IIS, FAU, Germany; J. Rossouw van der Merwe, Alexander Rügamer, and Wolfgang Felber, IIS, Germany
10:40	Leveraging Machine Learning Power in the Low-Cost Mass-Market Allystar GNSS Chip : Marco Mendonca, Altti Jokinen, Allystar Technology (Canada) Ltd.; Ryan Yang, Gary Hau, and Yi-Fen Tseng, Allystar Technology Co., Ltd.
11:03	Nonlinear Regression-based GNSS Multipath Map Construction and Its Dynamic Application in Deep Urban Area : Byungwoon Park and Yongjun Lee, Sejong University
11:26	CNN-based GNSS Direct Positioning in Urban Areas with Residual Maps : Penghui Xu, Guohao Zhang, Dept. of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University; Bo Yang, The Dept. of Computing, The Hong Kong Polytechnic University; Li-Ta Hsu, Dept. of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University
11:48	Designing Deep Neural Networks for Sequential GNSS Positioning : Shubh Gupta, Ashwin V. Kanhere, Akshay Shetty, and Grace Gao, Stanford University
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.	The Development and Evaluation of a Hybrid Vision-LiDAR Mapping and Localization Sensor for Resource-constrained Autonomous Systems : Aaron Hunter, Pavlo Vlastos, Carlos Espinosa, Renwick Curry, and Gabriel Elkaim, University of California, Santa Cruz
2.	Deep Learning-based Multi-camera Homography Estimation for Autonomous Vehicles : Sara Baldoni, Roma Tre University; Federica Battisti, University of Padova; Michele Brizzi, Michael Neri, Roma Tre University; Alessandro Neri, Roma Tre University and RadioLabs

Virtual Presentations Pre-recorded presentations will become available for viewing by registered attendees on Wednesday, September 21.
	A Robust RGB-D SLAM using Deep Learning for Depth Map Improvement : Hao Zhang and Hongyang Yu, University of Electronic Science and Technology of China
	How Next-Gen GNSS is Positioned to Power Up the World of IoT : Christopher Coromelas, Telit
	Machine Learning-Based Vehicle/Human Motion Detection of Low-Cost IMU for Two-Wheeler DR Navigation : Guang-Je Tsai , Song-Ying Li, You-Liang Chen, Tzu-Yin Chen, Shi-Xian Yang, AIROHA (Company of MediaTek Group)
	Remote Sensing Image Target Detection Based on Improved Faster R-CNN : Dong Zhou, Ruihao Zhou, Chengjun Guo, University of Electronic Science and Technology of China
	Satellite Video Target Tracking Based on Improved YOLOv3 and Deep Sort : Dong Zhou, Ruihao Zhou, Chengjun Guo, University of Electronic Science and Technology of China

Awards Luncheon • 12:15 p.m. - 1:30 p.m. (Lunch served until 12:30 p.m.; late arrivals will not be served)

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