|Abstract:||Multi-sensor navigation is quickly becoming operational as more alternative sensors continue to mature. Unfortunately, increasing the number of sensors also increases the possibility of corrupting the navigation solution with incorrect measurement models and undetected sensor failures. The multi-sensor resiliency chal- lenge remains largely unsolved, with only some sensor combinations (e.g., visual-inertial and GPS-inertial) having dedicated resiliency research. This research aims to provide multi-sensor resiliency through an au- tonomous sensor management framework. The proposed framework places each sensor into one of four modes: monitoring, validation, calibration, and remodeling. Each mode contains particular tasks that affect how the filter processes sensor measurements. The framework is developed by generalizing and interfacing functions found in existing research (fault detection, parameter estimation, and model selection) to achieve similar goals, but in a robust and sensor-agnostic manner. The proposed framework is compared against con- ventional filtering using two simulated scenarios including multiple sequential sensor failures and incorrectly modeled sensors.|
Proceedings of the ION 2019 Pacific PNT Meeting
April 8 - 11, 2019
Hilton Waikiki Beach
|Pages:||142 - 159|
|Cite this article:||
Jurado, Juan D., Raquet, John F., "Autonomous and Resilient Management of All-Source Sensors," Proceedings of the ION 2019 Pacific PNT Meeting, Honolulu, Hawaii, April 2019, pp. 142-159.
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