Aircraft trajectory forecasting using local functional regression in Sobolev space

International audienceThis paper considers the problem of short to mid-term aircraft trajectory prediction, that is, the estimation of where an aircraft will be located over a 10-30 min time horizon. Such a problem is central in decision support tools, especially in conflict detection and resolution algorithms. It also appears when an air traffic controller observes traffic on the radar screen and tries to identify convergent aircraft, which may be in conflict in the near future. An innovative approach for aircraft trajectory prediction is presented in this paper. This approach is based on local linear functional regression that considers data preprocessing, localizing and solving linear regression using wavelet decomposition. This algorithm takes into account only past radar tracks, and does not use any physical or aeronautical parameters. This approach has been successfully applied to aircraft trajectories between several airports on the data set that is one year air traffic over France. The method is intrinsic and independent from airspace structure

Imprecise regression and regression on fuzzy data A preliminary discussion

SerrP005International audienceThe paper provides a discussion of the possibilistic regression method originally proposed by H. Tanaka. This method has the advantage of allowing the learning of an imprecise model, in the form of an interval-valued function. It may lead to an imprecise model even in presence of precise data, which is satisfactory from a learning point of view. Indeed, finding a precise model that perfectly represents the concept to be learned is illusory, due to the existence of the bias caused by the choice of a modeling representation space, the limited amount of data, and the possibility of missing relevant data. However, what is obtained with possibilistic regression is more an imprecise model than a genuine fuzzy one. The paper illustrates and emphasizes this point on environmental data and suggest two different approaches for learning genuine fuzzy regression models from precise dat