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 |
Full Paper: |
ION Members/Non-Members: 1 Download Credit
Sign In |