Exploiting Satellite Motion in ARAIM: Measurement Error Model Refinement Using Experimental Data Mathieu Joerger * and Boris Pervan ** * The University of Arizona, Tucson, AZ ** Illinois Institute of Technology, Chicago, IL BIOGRAPHIES Dr. Mathieu Joerger obtained a Master in Mechatronics from the National Institute of Applied Sciences in Strasbourg, France, in 2002, and a M.S. and a Ph.D. in Mechanical and Aerospace Engineering from the Illinois Institute of Technology (IIT), in 2002 and 2009 respectively. He is the 2009 recipient of the Institute of Navigation (ION) Bradford Parkinson award, and the 2014 recipient of the ION Early Achievement Award. He is currently a research assistant professor at IIT, working on multi-sensor integration, on sequential fault-detection for multi-constellation navigation systems, and on safety of sense and avoid for unmanned aircraft systems. Dr. Boris Pervan is a Professor of Mechanical and Aerospace Engineering at IIT, where he conducts research on advanced navigation systems. Prior to joining the faculty at IIT, he was a spacecraft mission analyst at Hughes Aircraft Company (now Boeing) and a postdoctoral research associate at Stanford University. Prof. Pervan received his B.S. from the University of Notre Dame, M.S. from the California Institute of Technology, and Ph.D. from Stanford University. He is an Associate Fellow of the AIAA, a Fellow of the Institute of Navigation (ION), and Editor-in-Chief of the ION journal NAVIGATION. He was the recipient of the IIT Sigma Xi Excellence in University Research Award (2011, 2002), Ralph Barnett Mechanical and Aerospace Dept. Outstanding Teaching Award (2009, 2002), Mechanical and Aerospace Dept. Excellence in Research Award (2007), University Excellence in Teaching Award (2005), IEEE Aerospace and Electronic Systems Society M. Barry Carlton Award (1999), RTCA William E. Jackson Award (1996), Guggenheim Fellowship (Caltech 1987), and Albert J. Zahm Prize in Aeronautics (Notre Dame 1986). ABSTRACT In this work, a new time-sequential positioning and fault detection method is derived and analyzed for dual- frequency, multi-constellation Advanced Receiver Autonomous Integrity Monitoring (ARAIM). Unlike conventional ‘snapshot’ ARAIM, the sequential approach exploits changes in satellite geometry at the cost of slightly higher computation and memory loads. From the perspective of users on earth, the motion of any given GNSS satellite is small over short time intervals. But, the accumulated effect geometry variations of redundant satellites from multiple GNSS constellations can be substantial. This paper quantifies the potential performance benefit brought by satellite motion to ARAIM. It specifically addresses the following research challenges: (a) defining and experimentally validating raw GNSS code and carrier error models over time, consistent with established ARAIM assumptions, (b) designing estimators and fault-detectors capable of exploiting satellite motion for positioning, carrier phase cycle ambiguity estimation, and integrity evaluation, and (c) formulating these processes in a computationally-efficient implementation. A modular algorithm is designed, only requiring a minor augmentation of the snapshot airborne ARAIM multiple hypothesis solution separation (MHSS) algorithm. Other modifications to enable time-sequential ARAIM include additional ground segment performance commitments, and the inclusion of extra parameters in the broadcast integrity support message (ISM). Availability is analyzed worldwide for aircraft precision approach navigation applications. Results show substantial performance improvements for sequential ARAIM over snapshot ARAIM, not only to achieve ‘localizer precision vertical’ (LPV) requirements using depleted GPS and Galileo constellations, but also to fulfill much more stringent requirements including a ten-meter vertical alert limit. INTRODUCTION This paper describes the design, analysis, and evaluation of a new time-sequential positioning and fault detection method for Advanced Receiver Autonomous Integrity Monitoring (ARAIM) using dual-frequency, multi- constellation Global Navigation Satellite Systems (GNSS). The new approach differs from prior work on ‘snapshot’ (or instantaneous) ARAIM algorithms [1-3] in that it also exploits satellite motion, which provides observability of constant measurement biases [4]. This