Modeling Ionospheric Spatial Threat Based on Dense Observation Datasets for MSAS

Takeyasu Sakai, Keisuke Matsunaga, Kazuaki Hoshinoo, Todd Walter

Abstract:	The ionospheric correction procedure of the current MSAS, the Japanese version of SBAS/WAAS, is built on the algorithm named the ‘planar fit.’ It estimates the ionospheric propagation delays at IGPs using a first order estimator, and the associated confidence bound broadcast for computation of protection levels is based on the formal variance of the estimate. In such an estimation process, there are threats regarding mismodeling of the ionosphere and undersampling so it is necessary to inflate the confidence bound (GIVE values) to ensure that the integrity requirement is met. This inflation leads to relatively large GIVE values and thus lowers availability of the system for flight modes with vertical guidance. The ionospheric spatial threat model has been developed in order to make an inflation so to protect users against threats due to undersampled irregularities. The threat model must take care of spatial undersampled threats which means the possibility of ionospheric irregularities not well-sampled by the monitor network. Error bounds in SBAS broadcast messages must overbound the largest error at all possible user locations in the service volume even though user receivers may use ranging signals that pass through areas of the ionosphere not sampled by the monitor network. The threat model for MSAS was created by data deprivation (i.e., assuming that certain monitor station measurements were not available to the MSAS algorithm that generates GIVEs). This methodology provides the worst case scenario based on the ionospheric storm datasets observed by the monitor network which consists of 6 domestic stations. However, the resulting threat model seems to be too conservative, reflecting unrealistic conditions derived artificially by deprivation. And, more importantly, there is a certain possibility that some irregularities are not sampled properly in the datasets. In order to avoid such problems, the authors have developed a new methodology called oversampling to create a threat model. The basic idea is defining an ionospheric spatial threat model based on dense measurements which capture any irregularities in severe storm conditions. The authors propose to create a threat model based on combination of oversampling and some realistic deprivation methodologies. In our case, we can utilize a dense continuous observation network called GEONET for oversampling purposes. The malicious and station deprivation schemes should be employed to create a realistic threat model. Additionally, two candidate metrics working as a measure of goodness of IPP geometry are investigated. A new threat model for MSAS was created based on the proposed methodology. The authors have also implemented the new threat model into our prototype SBAS software in order to verify the performance of the model. The proposed methodology to create a threat model can be applied to any new ionospheric algorithms investigated to improve the performance of MSAS for approach with vertical guidance flight modes.
Published in:	Proceedings of the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2008) September 16 - 19, 2008 Savannah International Convention Center Savannah, GA
Pages:	1918 - 1928
Cite this article:	Sakai, Takeyasu, Matsunaga, Keisuke, Hoshinoo, Kazuaki, Walter, Todd, "Modeling Ionospheric Spatial Threat Based on Dense Observation Datasets for MSAS," Proceedings of the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2008), Savannah, GA, September 2008, pp. 1918-1928.
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