An Enhanced RANSAC-RTK Algorithm in GNSS-Challenged Environments

Abstract

Abstract Outliers significantly affect the accuracy and reliability of global navigation satellite system (GNSS). The outliers must be properly handled in real-time kinematic (RTK) positioning, particularly in GNSS-challenged environments. Otherwise, the accuracy and reliability of RTK positioning solutions cannot be guaranteed in these environments. To improve the usability of RTK positioning in GNSS-challenged environments, we propose an enhanced RANdom SAmple Consensus RTK (RANSAC-RTK) algorithm to handle multiple and continuous outliers. In the enhanced RANSAC algorithm, the threshold setting, sample prescreening and sample checking methods are improved considering the characteristics of GNSS data. Experiments are conducted using GNSS data collected in a GNSS-challenged environment with simulated continuous outliers for multiple satellites. The experimental results show that the standard RTK algorithm is vulnerable to outliers. In contrast, the enhanced RANSAC-RTK algorithm can effectively deal with multiple and continuous outliers, and the ambiguity fixing rate is increased by 33%. Therefore, it can significantly improve RTK performance in GNSS-challenged environments.