https://hal-enac.archives-ouvertes.fr/hal-00938037Delahaye, DanielDanielDelahayeDGAC - Direction Générale de l'Aviation Civile Puechmorel, StéphaneStéphanePuechmorelENAC - Ecole Nationale de l'Aviation CivileVacher, PierrePierreVacherCERT - ONERAWindfield estimation by radar track Kalman filtering and vector spline extrapolationHAL CCSD2003Kalman filtersair traffic controlaircraftanemometersextrapolationradar trackingwind[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Porte, Laurence2014-05-05 15:00:342021-10-19 11:02:492014-05-05 15:57:20enConference papershttps://hal-enac.archives-ouvertes.fr/hal-00938037/document10.1109/DASC.2003.1245869application/pdf1Accurate wind magnitude and direction estimation is essential for aircraft trajectory prediction. For instance, based on these data, one may compute entry and exit time from a sector or detect potential conflict between aircraft. Since the flight path has to be computed and updated on real time for such applications, wind information has to be available in real time too. The wind data which are currently available through meteorological service broadcast suffer from small measurement rate with respect to location and time. In this paper, a new wind estimation method based on radar tracks is developed. An Extended Kalman filer extracts the wind information by observation of the radar tracks in turns. After performing many evaluations in realistic framework, our approach is able to estimate the wind vectors accurately. By this mean, each aircraft can be seen as a wind sensor when it is turning. Based on those measurements, a global space-time wind field estimation using vector splines is extrapolated in order to produce wind maps in the area of interest. The underline model for wind field computation is Shallow-Water, which assumes geostrophic wind. The accuracy of this wind map is dependent of the number of aircraft turns in a given zone; then the estimation is better in the terminal area (TMA) than in en-route area because aircraft are tuning more often. Further improvements to the estimation can be made by correlating with meteorological measurements.