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.
Location: Mineral Hall DE (Third Floor)
In-Person presenters in this session provide pre-recorded presentations for viewing by registered attendees on Wednesday, September 21.

Session Chairs

Dr. Michael Veth
Veth Research Associates, LLC


Dr. Damien Dusha
Google Inc.

 
Track Chair

Dr. Mohammed Khider
Google

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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 Nadim Khairallah, University of California, Irvine; Zak (Zaher) M. Kassas, The Ohio State University
8:57 GNSS Spoofing Detection Using Machine Learning and Truncated Singular Value Decomposition : Logan L. Maynard, University of Colorado Colorado Springs
9:20 Machine Learning-assisted GNSS Interference Monitoring through Crowdsourcing : Nisha Lakshmana Raichur, Tobias Brieger, Dorsaf Jdidi, Tobias Feigl, J. Rossouw van der Merwe, Birendra Ghimire, Felix Ott, Alexander Rügamer, and Wolfgang Felber, Fraunhofer Institute for Integrated Circuits IIS, Nuremberg, Germany
9:43 Unsupervised Disentanglement for PostIdentification of GNSS Interference in the Wild : Dorsaf Jdidi, Tobias Brieger, Friedrich-Alexander-University (FAU) & Fraunhofer Institute for Integrated Circuits IIS; Tobias Feigl, David Contreras Franco, J. Rossouw van der Merwe, Alexander Rügamer, Jochen Seitz, and Wolfgang Felber, Fraunhofer Institute for Integrated Circuits IIS
10:05-10:35, Break. Refreshments served outside of session rooms
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 : Yongjun Lee and Byungwoon Park, Sejong University
11:26 PositionNet: CNN-based GNSS Positioning in Urban Areas with Residual Maps : Penghui Xu, Guohao Zhang, Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University; Bo Yang, The Department of Computing, The Hong Kong Polytechnic University; Li-Ta Hsu, Department 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 Best Presentation
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, Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China (UESTC)
  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 : Ruihao Zhou, Dong 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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