Multiple GPS fault detection and isolation using a Graph-SLAM framework

Autonomous vehicles operating in GNSS challenged urban environments are prone to uncorrelated multipath effects in multiple satellite channels. In addition, the increasing number of GNSS satellites due to the addition of new constellations increases the probability of multiple satellite faults caused by broadcast anomalies. We propose a graph-based Simultaneous Localization and Mapping (Graph-SLAM) framework to perform multiple GPS Fault Detection and Isolation (FDI), in particular, satellite faults due to broadcast anomalies and received signal faults due to multipath. SLAM is a well-known technnique in robotics. It utilizes sensor measurements to estimate the landmark features in an unknown 3-Dimensional (3D) map while simultaneously localizing the robot within it. Analogous to this, we design a Graph-SLAM framework, where the robot is the receiver, and the landmarks in the map are the GPS satellites. Utilizing the pseudoranges, receiver and satellite motion model, our SLAM-based FDI simultaneously estimates the position, velocity and time of both GPS receiver and satellites. Thereafter, we assess the probability of fault in each satellite by individually evaluating the corresponding test statistic against its empirical cumulative distribution calculated on-the-fly. We validate our algorithm via different experimental scenarios, namely, adding multiple simulated broadcast anomalies to the open-sky data collected using a ground vehicle; flying an aerial vehicle in an urban area prone to multipath. We demonstrate the capability of our algorithm in performing multiple FDI while accurately locating the receiver.