Multitask Aircraft Capability Estimation Using Conjunctive Filters

Copyright 2017 by the authors. In this paper, a data-driven approach to producing rapid, online estimates of aircraft capability is presented. The process involves using physics-based models to produce an offline library of various damage states and associated capabilities. This association is performed in-flight by an online Bayesian classification process, using singlemaneuver sensor readings to predict capability. Previous literature focused on estimating capability for a singlemaneuver type, and this work extends that work to allow for simultaneous estimation of multiple maneuver types. Because of sensor noise, misclassifications can occur, and this is accounted for by incorporating uncertainty into the estimation. The ability to estimate capability for multiple maneuver types enables the performance of sequential information-gathering maneuvers, often resulting in more accurate, less uncertain estimates through information fusion. Information gained by performing sequential information-gathering maneuvers is fused using standard Bayesian fusion techniques, as well as a novel conjunctive fusion method developed in this work. The conjunctive filter is shown to perform with a lower mean squared error than the Bayesian fusion technique and the single-maneuver classification with no fusion step. Our methodology and demonstrations are developed in the context of a mediumaltitude, long-endurance unmanned aerial vehicle.

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