Learning Air-Data Parameters for Flush Air Data Sensing Systems
Ankur Srivastava, Andrew J. Meade, Kurtis R. Long · Journal of Aerospace Computing Information and Communication · 2012
An adaptive scattered data approximation scheme was developed to calibrate the Flush Air Data System (FADS) of a surface vessel. An array of pressure sensors were mounted flush with the deckhouse periphery and the airdata parameters were extracted from the pressure measurements. The developed Galerkin derived self-adaptive greedy function approximation scheme gave reliable and robust surrogates for predicting wind speed and direction. The resulting surrogates were also used to evaluate the sensitivity to each of the flush mounted pressure sensor. Fault tolerance of the proposed surrogates were also studied with respect to pressure sensor failure. Nomenclature bn = nth bias in the approximation cn = nth linear coefficient in the approximation Cp = Coefficient of pressure d = input dimension f = arbitrary function n = number of bases nr = n th stage of the function residual \\ = real coordinate space d \\ = d- dimensional vector space s = number of training samples ()u ξ = target function ()anu ξ = nth stage of the target function approximation v = arbitrary function D f v = (s i ii)f v ∑ = discrete inner product of f and v f = absolute value of f 2 f = ( = L)1 / 22f dξΩ ∫ 2 norm of f