Global Navigation Satellite System
Global Navigation Satellite Systems (GNSS) provide crucial location information for a vast array of applications, with current research focusing on improving accuracy and robustness in challenging environments. This involves developing advanced sensor fusion techniques, often employing factor graph optimization or Kalman filtering, and integrating machine learning models (like deep neural networks, including transformers and LSTMs) for tasks such as interference detection, multipath mitigation, and ionospheric scintillation prediction. These advancements are vital for enhancing the reliability of GNSS-dependent systems in autonomous vehicles, robotics, and precision agriculture, among other fields.
Papers
Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning
Wenqi Bai, Xiaohui Zhang, Shiliang Zhang, Songnan Yang, Yushuai Li, Tingwen Huang
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
Nishant S. Gaikwad, Lucas Heublein, Nisha L. Raichur, Tobias Feigl, Christopher Mutschler, Felix Ott
pyrtklib: An open-source package for tightly coupled deep learning and GNSS integration for positioning in urban canyons
Runzhi Hu, Penghui Xu, Yihan Zhong, Weisong Wen
UniMSF: A Unified Multi-Sensor Fusion Framework for Intelligent Transportation System Global Localization
Wei Liu, Jiaqi Zhu, Guirong Zhuo, Wufei Fu, Zonglin Meng, Yishi Lu, Min Hua, Feng Qiao, You Li, Yi He, Lu Xiong