Global navigation satellite systems (GNSS) are core navigation technologies using multiple satellite constellations. However, positioning performance varies depending on the internal estimation algorithms and configuration settings of GNSS receivers, resulting in discrepancies in positioning accuracy even in identical environments. Standardized procedures have not been fully established for consistent evaluation of quantitative GNSS accuracy under dynamic driving conditions, and human-induced operational errors further amplify positioning errors. To address these limitations, this study proposes a position-correction algorithm that utilizes constellation-specific position estimates and real-time error information. The corrected position is determined by assigning weights based on the error covariance outputs provided for each constellation by the GNSS receiver. The performance of the proposed algorithm is validated through real-world driving experiments using an autonomous vehicle platform. Experimental results demonstrate a reduction in position variability even in environments with discontinuous signal conditions. Furthermore, inter-constellation error discrepancies remain stable, and outliers are effectively detected and removed, confirming the robustness and reliability of the proposed approach. The proposed method achieves up to a 56% improvement in positioning accuracy in terms of CEP50, demonstrating its effectiveness for reliable GNSS positioning in dynamic environments.
Ryu et al. (Tue,) studied this question.
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