Classification non supervisée de courbes basée sur l'information au second ordre : détection de la dégradation de l'état de pistes d'atterrissage

Abstract : In air transportation, especially in airport safety, radar tracks are continuously recorded and may be used for detecting incidents on airport surface. However, all known statistical algorithms, even those based on functional data, are unable to distinguish between a safety critical flight and another one departing from standard behavior, but otherwise safe. In this work, we propose a change of paradigm by representing curves as points in a shape manifold. In this framework, it is possible to use Finsler distances between shapes that explicitly take into account the second derivative and can be able to 1 correctly detect skid situations from deviant trajectories that cannot be considered as a slipped trajectory. This metric is next used in curve clustering for detecting bad runway conditions. Some results on datasets of synthetic and real trajectories are presented, as well as a comparison of existing metrics.
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Stéphane Puechmorel, Florence Nicol, Cindie Andrieu, Baptiste Gregorutti. Classification non supervisée de courbes basée sur l'information au second ordre : détection de la dégradation de l'état de pistes d'atterrissage. 50ème Journées de Statistique, May 2018, Paris Saclay, France. ⟨hal-01799089⟩

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