An uncertainty expression method for network RTK ionospheric modelling based on user crowdsourcing

Abstract In GNSS Network Real-Time Kinematic (NRTK) positioning, the accuracy of ionospheric delay modeling directly determines positioning performance. During periods of high ionospheric activity, spatial gradients vary rapidly, challenging the planar modeling assumptions of conventional NRTK. To address this issue, this study proposes a crowdsourcing-based method for expressing ionospheric modeling uncertainty. The method introduces user feedback data to supplement the reference station network, forming an overdetermined model even in triangular network units and enabling statistically meaningful estimation of modeling residuals. A weighted interpolation algorithm that considers both angular and distance correlations is then applied to generate a continuous uncertainty field across the service area. Experimental results using Australian CORS data during active ionospheric periods show that the proposed method achieves a high correlation between the Ionospheric Residual Interpolation Uncertainty (IRIU) index and true modeling errors. When incorporated into the ionosphere-weighted RTK model, the adjusted IRIU improves ambiguity resolution success rates to over 95 % and enhances overall positioning accuracy by more than 50 %, demonstrating its effectiveness and robustness under challenging ionospheric conditions.