Sequential ARAIM: Error Models, Algorithms, and Performance

This presentation describes advances in navigation safety monitoring that were made since our last meeting at the Research Training Group i.c.sens Leibniz Universität Hannover in 2019.  We will focus on an implementation of Advanced Receiver Autonomous Integrity Monitoring (ARAIM) that uses
measurements over time.  ARAIM is a cooperative research effort by the European Union and the United States to analyze the potential of future Global Navigation Satellite Systems (GNSS) to enable high-integrity navigation applications.  Unlike conventional “snapshot” ARAIM algorithms that use measurements at one instant in time, sequential ARAIM filters measurements over time.  Measurement filtering improves positioning accuracy and facilitates integration with inertial sensors.  In addition, sequential ARAIM exploits changes in satellite geometry at the cost of slightly higher computation and memory loads. For a user near the surface of the Earth, the motion of an individual GNSS satellite is small over short time intervals. But the accumulated geometry variations of redundant satellites from multiple GNSS can be substantial. In this presentation, we quantify the integrity performance benefits brought by satellite motion to ARAIM. We address the following challenges: (a) we define raw GNSS error models over time using power spectral density (PSD) bounding, (b) we design estimators and fault detectors exploiting geometric diversity, and (c) we formulate these algorithms in a computationally efficient implementation.  We carry out a
worldwide availability analysis for an example aircraft approach navigation to evaluate the performance of sequential ARAIM versus snapshot ARAIM.