+ Predicting the future location of a General Aviation aircraft - PowerPoint PPT Presentation

+ Predicting the future location of a General Aviation aircraft Claude le Tallec ICRAT 2014 - Istanbul Joram Verstraeten Giuseppe Frau Damiano Taurino Carlo Lancia

P r o G A P r o j e c t Probabilistic 4d Trajectories of light General Aviation operations M a i n O b j e c t i v e study the feasibility of a system that can continually and automatically predict the future GA aircraft’s flight corridor or its volume of operation

S T O R Y B O A R D 1 2 USER NEEDS BENEFITS APPROACHES CHALLENGES

CAPT. MASSIMO BE-FREE G SAFE PLANNING VFR

P L A N N I N G TRAFFIC INFO A B HOTSPOTS AVOID KNOWN CRITICAL SPOTS

P L A N N I N G ACCEPTABILITY Historical data hotspots TBD Intent data

I N - F L I G H T

I N - F L I G H T BETTER SA VISUALIZATION AVOID POTENTIAL CONFLICTS

IN–FLIGHT (short-term prediction) Two-phase iterative scheme ACCEPTABILITY x t : aircraft state (e.g. position and speed) z t : “noisy” observation of x t New observation z t+1 available ADSB-LIKE Dynamical Observation KALMAN FILTER model: model: x t+1 ~ x t z t ~ x t Prediction Phase: Update Phase: x t+1 | z t x t+1 | z t+1

I N – F L I G H T

IN-FLIGHT (LONG TERM) VISUALIZATION UNCERTAINTY VIS. METHODS IN-FLIGHT REPLANNING

IN-FLIGHT (LONG TERM) BETTER SA DATA AVAILAB COMPUTATION KDE RECONSTRUCTION MATCHING

KDE FOR TIPICALLY FLOWN PATHS KDE from the flown paths between Bernay (LFPD) Saint-Cyr-l'École (LFPZ)

KDE FOR TIPICALLY FLOWN PATHS KDE from the flown paths between Bernay (LFPD) Saint-Cyr-l'École (LFPZ)

KDE FOR TIPICALLY FLOWN PATHS KDE from the flown paths between Bernay (LFPD) Saint-Cyr-l'École (LFPZ)

A F T E R - F L I G H T ACCEPTABILITY ENGAGEMENT Flown Path SEND FLIGHT PATH REAL TIME TRACK UPLOAD

FLIGHT CORRIDORS VS VOLUMES OF OPERATION powered glider FLIGHT CORRIDORS + HOTSPOTS VOLUME OF OPERATION

FLIGHT CORRIDORS VS VOLUMES OF OPERATION Termal lift flights

W A Y F O R W A R D n Define and consolidate the user and functional requirements of a ProGA system n Develop the algorithms for flight corridor and volume of operation predictions n Develop the algorithms to define hotspots n Develop a prototype HMI n Assess the foreseen benefits of a ProGA system in the key performance area of safety n Determine the social acceptance within the GA community of sharing location and intent during flight

C O N C L U S I O N S AVOID KNOWN IN-FLIGHT CRITICAL SPOTS AVOID POTENTIAL REPLANNING CONFLICTS LIBERTY BETTER SA TRAFFIC INFO PLANNING SAFE FLIGHT PATH ADSB-LIKE KDE MATCHING SHARING KALMAN FILTER UNCERTAINTY VIS. HOTSPOTS METHODS RECONSTRUCTION DATA AVAILAB VISUALIZATION ENGAGEMENT ACCEPTABILITY COMPUTATION

C O N C L U S I O N S n ProGA can help improve three layers of conflict management n This will help reducing GA midair collision risk Midair ¡ ¡ collision ¡ Trajectory ¡ State ¡based ¡ Intent ¡based ¡ management ¡ surveillance ¡ surveillance ¡

T H A N K Y O U /credits n Database by Dmitry Baranovskiy from The Noun Project n Process by Takao Umehara from The Noun Project n Airplane by Juan Pablo Bravo from The Noun Project n X and check by useiconic.com from The Noun Project n Captain Massimo