A Hybrid Data-Model Fusion Approach to Calibrate a Flush Air Data Sensing System

摘要

A hybrid data-model fusion approach has been devised to calibrate a Flush Air Data Sensing system. Numerical simulation and artificial neural network based approaches have been previously applied to predict airdata state from flush pressure measurements. However both approaches have their shortcomings. Numerical simulations rely on approximations to model the truth while learning algorithms do not incorporate the physics of the problem and often need a large set of data for training. A principled approach has been devised to fuse experimental FADS data and numerical solutions in an optimal manner. The purpose of this approach is to improve the prediction accuracies of the airdata state obtained by a pure neural network based approach. Other objectives of this approach include better noise tolerance and a need for fewer experimental data.