Road to enhanced SBAS

Abstract

The Global Navigation Satellite System (GNSS) has the potential to become the primary navigation sensor for aviation. However, as GNSS does not provide integrity bounds on the position errors, it lacks a fundamental feature for a safety critical system. Although most of the time the positioning accuracy is high, the position error can significantly increase without any warning to the user. Consequently, Satellite-Based Augmentation Systems (SBASs) for GNSS have been developed to provide corrections and confidence bounds on the user position errors, which are called Protection Level (PL). Among the error sources of GNSS positioning, the ionosphere is the largest and most unpredictable. Thus, SBAS broadcasts ionospheric delay estimates to the users along with the corresponding confidence bounds on the estimates, known as Grid Ionospheric Vertical Errors (GIVEs). At any given time, ground stations distributed across the operational region can observe the ionosphere through only a set of ionospheric delay measurements from those stations. From this limited and randomly scattered data, the master station must generate real time estimates of the ionospheric delays and the corresponding hard bounds, which are valid for any user within the range of the ground stations. The variability of the ionospheric behavior and the stringent integrity requirements have caused the confidence bounds corresponding to the ionosphere (i.e., GIVEs) to be very large. In particular, the magnitude of GIVEs in the peripheral regions, where the coverage of ionospheric measurements is very poor, significantly affects the SBAS service volume. In addition, if system deployment is considered for the region where the number and distribution of reference stations are limited, the system may not always be available. In order to increase the SBAS availability, we need to reduce the uncertainty bounds corresponding to ionosphere (i.e., GIVEs) while maintaining integrity.

This thesis presents spatio-temporally expanded ionospheric estimation and error bounding method to improve the performance of SBAS by reducing the magnitudes of GIVEs. The thesis initially proposes an expanded kriging estimation algorithm that integrates ionospheric observables from a previous epoch to a current fit domain to improve the kriging fit performance. We implement a $1^{st}$ order Gauss-Markov Kalman filter to incorporate ionospheric measurements from the previous epoch into the current fit domain. We also design the monitor based on the Kalman filter innovation test to assure the safety of using newly generated ionospheric observables. In addition, we define the new covariance matrix that describes ionospheric covariance between measurements so that the newly generated measurements can be successfully incorporated into the current kriging estimation frame.

Second, a new threat model methodology is proposed by developing a new threat model metric, Norm-based Angular Metric (NAM). The metric 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 R fit and Relative Centroid Metric (RCM).

This thesis demonstrates the performance of the proposed algorithms by conducting availability simulations for SBAS in the Korean region. First, with the proposed kriging method, the coverage of 99.9% availability for Approach Procedure with Vertical guidance (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. Those performance improvements are achieved without critical changes in the hardware and without significantly increasing the complexity of the existing algorithm.