Automated Ionospheric Front Velocity Estimation Algorithm for Ground-Based Augmentation Systems


Ionospheric anomalies, which may occur during severe ionospheric storms, could pose integrity threats to Ground-based Augmentation System (GBAS) users. The ionospheric threat for a Local Area Augmentation System (LAAS), a GBAS developed by the U.S. Federal Aviation Administration (FAA), was modeled as a spatially linear, semi-infinite “front” (like a weather front) with constant propagation speed. The model is parameterized by the slope (or gradient) of the front, its width, and its ground speed. Along with the magnitude of ionospheric gradients, the speed of the fronts in which these gradients are embedded is an important parameter for GBAS integrity analysis.

This paper proposes an automated velocity estimation algorithm for anomalous ionospheric fronts. To examine the performance of this automated algorithm, we obtained estimation results for the points of the current Conterminous U.S (CONUS) threat space and compared these estimates to those manually computed previously.

This new algorithm proposed in this paper is shown to be robust to faulty measurement and modeling errors. In addition, this algorithm is used to populate the current threat space with newly-generated threat points obtained from the Long-Term Ionospheric Anomaly Monitoring tool. A larger number of velocity estimates helps to better understand the motion of ionospheric fronts under geomagnetic storm conditions.

Eugene Bang and Jiyun Lee
Jiwon Seo, Sam Pullen, and Sigrid Close

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