A dynamic data-driven approach to multiple task capability estimation for self-aware aerospace vehicles
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© 2016, American Institute of Aeronautics and Astronautics. All right reserved. In this paper, a data-driven approach to producing rapid, online estimates of aircraft ca-pability is presented. The process involves using physics-based models to produce an ofline library of various damage states and associated capabilities. This association is performed using an online Bayesian classification process, using single maneuver sensor readings to predict capability across multiple flight paths. Information from multiple maneuver is fused using standard Bayesian fusion techniques, as well as a novel conjunctive fusion method developed in this work. Our methodology and demonstrations are developed in the context of a medium altitude, long-endurance unmanned aerial vehicle.
author list (cited authors)
Burrows, B. J., Isaac, B., & Allaire, D.