An Efficient Approach to Sensor Fusion Development

Christopher J. Hogstrom and Robert N. Stoddard

Abstract:	Autonomous vehicles rely on a variety of sensors for accurate positioning, navigation, and timing (PNT). Data from these sensors are typically provided to a navigation fusion engine where an optimal estimate of the vehicle's state vector is formed to include position, velocity and time. Sensor fusion is required due to the ever-present organic threats to satellite based navigation systems that limit autonomous vehicle navigation such as urban canyons, multipath, and privacy jammers. The ability to fully simulate a sensor fusion environment, to include GNSS and non-GNSS based sensors, is essential to efficient development and accurate characterization of sensor fusion systems. Spirent Federal is working to develop a full simulation environment where all sensor data, including GNSS data, are coherent, and sensor outages and corruptions are modelled to measure the effects on fusion algorithms. Our cutting-edge simulation technology is the first of its kind to truly offer a comprehensive development environment that allows for full system stimulation with the added benefit of reducing cost and schedule associated with real-world testing. This paper takes a systems engineering approach to show how to design and develop a sensor fusion system to pace the organic threat environment, how to characterize and verify the system through our advanced simulation capabilities, and validate the end solution against customer needs.
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:	2521 - 2525
Cite this article:	Hogstrom, Christopher J., Stoddard, Robert N., "An Efficient Approach to Sensor Fusion Development," 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. 2521-2525. https://doi.org/10.33012/2021.17919
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