Improved Iono PHMI Calculation for SBAS Systems

Abstract

Besides the wide-area augmentation system (WAAS) in the US, an increasing number of space-based augmentation systems (SBAS) are being developed or planned, such as the and the European Geostationary Navigation Overlay System (EGNOS) in Europe, the Multi-functional Satellite Augmentation System (MSAS) system in Japan, and the future Ground-based Regional Augmentation System (GRAS) in Australia and the GPS and Geo Augmented Navigation (GAGAN) system covering the Indian subcontinent. The integrity analysis a given SBAS system includes calculating the probability of hazardous misleading information (PHMI) for the vertical ionospheric delays. In the way this calculation is usually done, there is a fair amount of conservatism built in. An important part of the PHMI calculation for a given SBAS system is the careful analysis and separation of the measurement noise from process noise, which is caused by the ionospheric de-correlation. Since the ionospheric de-correlation function depends on the state of the ionosphere, active ionospheric conditions are taken into account by establishing a model for the process noise. In the process noise model, the state of the ionosphere is parameterized by a single variable; a value of unity corresponds to nominal ionospheric conditions, values greater than unity indicate active ionospheric conditions. In the WAAS system the process noise model used in the PHMI analysis supposes a linear relationship between the process noise for active ionospheric conditions and the process noise under nominal ionospheric conditions. We show that the built-in conservatism can be significantly reduced by using a more general model for the ionospheric process noise, i.e. we consider generalized models of the process noise, where the process noise is allowed to depend non-linearly on the nominal ionospheric de-correlation function. As a consequence, we obtain a more realistic dependence of PHMI on the ionospheric state, and the additional degrees of freedom of the process noise model can be used to optimize the system for availability under disturbed ionospheric conditions.