Methodology of automated ionosphere front velocity estimation for ground-based augmentation of GNSS
Article first published online: 18 NOV 2013
©2013. American Geophysical Union. All Rights Reserved.
Volume 48, Issue 6, pages 659–670, November/December 2013
How to Cite
2013), Methodology of automated ionosphere front velocity estimation for ground-based augmentation of GNSS, Radio Sci., 48, 659–670, doi:10.1002/rds.20066., and (
- Issue published online: 16 JAN 2014
- Article first published online: 18 NOV 2013
- Accepted manuscript online: 3 OCT 2013 09:55AM EST
- Manuscript Accepted: 26 SEP 2013
- Manuscript Revised: 15 SEP 2013
- Manuscript Received: 1 APR 2013
- NRF. Grant Number: 2012–0007550
- ionospheric anomaly;
 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.