Expanded Ionospheric Estimation and Threat Model Algorithms for SBAS

Eugene Bang

Peer Reviewed

Abstract:	The largest contribution to the Vertical Protection Levels (VPLs) of Satellite-Based Augmentation Systems (SBAS) comes from the Grid Ionospheric Vertical Errors (GIVEs), which are confidence bounds for the vertical ionospheric delay estimates of SBAS. Thus, reducing GIVEs is a critical issue in improving the performance of SBAS. This paper introduces two expanded methodologies to improve the performance of SBAS by reducing the magnitudes of GIVEs. We initially propose an expanded kriging method which integrates ionospheric observables from a previous epoch to a current fit domain to improve the kriging fit performance. Secondly, we propose a new threat metric, Norm-based Angular Metric (NAM), which effectively captures the uniformity of the Ionospheric Pierce Point (IPP) distribution by measuring the angular distribution of the IPPs. We also construct an undersampled ionospheric irregularity threat model with a three-dimensional set of threat metrics by combining the newly developed metric and the existing Rfit and Relative Centroid Metric (RCM). The performance of the proposed algorithms is investigated by conducting availability simulations for SBAS in the Korean region. First, with the proposed kriging method, the coverage of 99.9% availability for APV-I service is increased by approximately 12% within South Korea. Second, the newly developed threat model widened the 99% availability coverage for APV-I service by approximately 13% when SBAS monitor stations are expanded to overseas locations.
Published in:	Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016) September 12 - 16, 2016 Oregon Convention Center Portland, Oregon
Pages:	1338 - 1349
Cite this article:	Bang, Eugene, "Expanded Ionospheric Estimation and Threat Model Algorithms for SBAS," Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), Portland, Oregon, September 2016, pp. 1338-1349. https://doi.org/10.33012/2016.14700
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