A Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

Evan Dill, Julian Gutierrez, Steven Young, Andrew Moore, Arthur Scholz, Emily Bates, Ken Schmitt, Jonathan Doughty

Abstract: The emergence and development of advanced technologies and vehicle types has created a growing demand for the introduction of new forms of flight operations. These new and increasingly complex operational paradigms such as Advanced and Urban Air Mobility (AAM/UAM) present regulatory authorities and the aviation community with several design and implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is finding methods to integrate these emerging operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. This paper focus on the development and testing of a prognostic service aimed at estimating the quality of Global Navigation Satellite System (GNSS) performance for an autonomous aircraft in complex environments. The intent of this function is to proactively reduce a flight operations risk of exposure to states that may induce poor or unacceptable navigation system performance by factoring in estimates of GNSS quality into pre-flight and/or in-flight route planning. Methodologies for producing quality estimates are specified and results are provided for selected simulation and flight test cases.
Published in: Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021)
September 20 - 24, 2021
Union Station Hotel
St. Louis, Missouri
Pages: 125 - 137
Cite this article: Dill, Evan, Gutierrez, Julian, Young, Steven, Moore, Andrew, Scholz, Arthur, Bates, Emily, Schmitt, Ken, Doughty, Jonathan, "A Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 125-137. https://doi.org/10.33012/2021.18138
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