Abstract
ABSTRACT Outliers can significantly impact the accuracy and reliability of Global Navigation Satellite System (GNSS) real-time kinematic (RTK) positioning. To enhance the robustness of RTK in GNSS-challenged environments, we propose an enhanced random sample consensus RTK (RANSAC-RTK) algorithm capable of effectively handling multiple and continuous outliers. The enhancements to the RANSAC algorithm include threshold setting, pre-screening samples and sample verification tailored to the characteristics of GNSS data. The experimental results indicate that the standard RTK algorithm is vulnerable to outliers. By contrast, the enhanced RANSAC-RTK algorithm can effectively handle multiple and continuous outliers, resulting in 33% increase in the ambiguity fixing rate and 15% improvement in positioning accuracy.
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