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In this paper, we build a multidimensional wavelet decomposition based on polyharmonic B-splines. The prewavelets are polyharmonic splines and so not tensor products of univariate wavelets. Explicit construction of the filters specified by the classical dyadic scaling relations is given and the decay of the functions and the filters is shown. We then design the decomposition/recomposition algorithm by means of downsampling/upsampling and convolution products.

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This 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 to 30 minutes 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.