Improving GNSS Positioning Correction Using Deep Reinforcement Learning with an Adaptive Reward Augmentation Method

Improving GNSS Positioning Correction Using Deep Reinforcement Learning with an Adaptive Reward Augmentation Method

High-precision global navigation satellite system (GNSS) positioning for automatic driving in urban environments remains an unsolved problem because of the impact of multipath interference and non-line-of-sight reception. Recently, methods based on data-driven deep reinforcement learning (DRL), whic...

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Bibliographic Details
Main Authors: Jianhao Tang, Zhenni Li, Kexian Hou, Peili Li, Haoli Zhao, Qianming Wang, Ming Liu, Shengli Xie
Format: Article
Language:English
Published: Institute of Navigation 2024-11-01
Series:Navigation
Online Access:https://navi.ion.org/content/71/4/navi.667
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