[1] Extreme ionospheric anomalies occurring during severe ionospheric storms can pose integrity threats to Global Navigation Satellite System (GNSS) Ground-Based Augmentation Systems (GBAS). Ionospheric anomaly threat models for each region of operation need to be developed to analyze the potential impact of these anomalies on GBAS users and develop mitigation strategies. Along with the magnitude of ionospheric gradients, the speed of the ionosphere "fronts" in which these gradients are embedded is an important parameter for simulation-based GBAS integrity analysis. This paper presents a methodology for automated ionosphere front velocity estimation which will be used to analyze a vast amount of ionospheric data, build ionospheric anomaly threat models for different regions, and monitor ionospheric anomalies continuously going forward. This procedure automatically selects stations that show a similar trend of ionospheric delays, computes the orientation of detected fronts using a three-station-based trigonometric method, and estimates speeds for the front using a two-station-based method. It also includes fine-tuning methods to improve the estimation to be robust against faulty measurements and modeling errors. It demonstrates the performance of the algorithm by comparing the results of automated speed estimation to those manually computed previously. All speed estimates from the automated algorithm fall within error bars of ± 30% of the manually computed speeds. In addition, this algorithm is used to populate the current threat space with newly generated threat points. A larger number of velocity estimates helps us to better understand the behavior of ionospheric gradients under geomagnetic storm conditions. Citation: Bang, E., and J. Lee (2013), Methodology of automated ionosphere front velocity estimation for ground-based augmentation of GNSS, Radio Sci., 48, 659-670,

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The requirement for ARAIM continuity risk due to the monitor false alarm has been outlined in earlier works for ARAIM development (WG-C 2016). However, the expected continuity risk comes from an underlying conservative assumption that the correlation between multiple monitors for fault detection is negligible. Thus, we investigate the effect of the cross-correlation across ARAIM solution separation tests on the monitor false alarm probability ( by presenting a higher fidelity methodology to evaluate the based on highly correlated fault detection tests. We carry out a preliminary assessment of ARAIM false alarm performance by using the proposed method. It was found that considering the cross-correlation amongst monitor test statistics reduces the predicted by up to approximately 50% of the predefined requirement (e.g., 10 ) when triple satellite faults were considered. Despite such improvement, the baseline ARAIM implementation does not appear to be overly conservative.

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