Learning Air-Data Parameters for Flush Air Data Sensing Systems
作者： Ankur Srivastava , Andrew J. Meade , Kurtis R. Long
DOI: 10.2514/1.54947
关键词:
摘要: An adaptive scattered data approximation scheme was developed to calibrate the Flush Air Data System (FADS) of a surface vessel. array pressure sensors were mounted flush with deckhouse periphery and airdata parameters extracted from measurements. The Galerkin derived selfadaptive greedy function gave reliable robust surrogates for predicting wind speed direction. resulting also used evaluate sensitivity each sensor. Fault tolerance proposed studied respect sensor failure.
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