Abstract
The detection of slope change points in wind curves depends on linear curve-fitting. Hall and Titterington’s algorithm based on smoothing is adapted and compared to a Bayesian method of curve-fitting. After prior spline smoothing of the data, the algorithms are tested and the errors between the split-linear fitted wind and the real one are estimated. In our case, the adaptation of the edge-preserving smoothing algorithm gives the same good performance as automatic Bayesian curve-fitting based on a Monte Carlo Markov chain algorithm yet saves computation time.
Highlights
This study is aimed at the improvement of the aircraft autopilot conception process
We focus on the effect of the linear wind components in the last 30 seconds to show that they are a decisive factor in touchdown precision. This is achieved by comparing simulated landings with either a real wind or its piecewise linear approximation. This has led us to develop a method of split-linear fitting based on slope change detection adapted to our data
Note that the quality of fit is measured by the MSE since, as already explained, it is impossible to replace the criterion by the one we focus on, that is difference between touchdown with the fitted wind curves and the real ones
Summary
This study is aimed at the improvement of the aircraft autopilot conception process. The autopilot allows landings in bad weather conditions and must guarantee passengers safety, touchdown comfort, and precision. This is achieved by comparing simulated landings with either a real wind or its piecewise linear approximation This has led us to develop a method of split-linear fitting based on slope change detection adapted to our data. The criteria to be met are estimation of a parsimonious model by detecting only the significant slope changes in the curve and achievement of the same landing properties with the real wind and with the split-linear approximation. This is an important feature of this paper, which especially involves adaptations of the algorithms which consist in prior smoothing of the data and algorithm refinements.
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