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Predicting the future location of a General Aviation aircraft

ICRAT 2014 - Istanbul Claude le Tallec Joram Verstraeten Damiano Taurino Carlo Lancia Giuseppe Frau

P r o G A P r o j e c t

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

S T O R Y B O A R D

1 2

USER NEEDS BENEFITS APPROACHES CHALLENGES

• CAPT. MASSIMO

BE-FREE SAFE PLANNING G VFR

P L A N N I N G

TRAFFIC INFO

AVOID KNOWN CRITICAL SPOTS

HOTSPOTS A B

P L A N N I N G

Intent data

Historical data ACCEPTABILITY TBD

hotspots

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)

ACCEPTABILITY ADSB-LIKE KALMAN FILTER

New observation zt+1 available xt: aircraft state (e.g. position and speed) zt: “noisy” observation of xt

Update Phase:

xt+1 | zt+1

Prediction Phase:

xt+1 | zt Observation model: zt ~ xt Dynamical model: xt+1 ~ xt

Two-phase iterative scheme

I N – F L I G H T

IN-FLIGHT (LONG TERM)

VISUALIZATION

UNCERTAINTY VIS. METHODS

IN-FLIGHT REPLANNING

IN-FLIGHT (LONG TERM)

DATA AVAILAB BETTER SA

MATCHING

COMPUTATION

RECONSTRUCTION KDE

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

SEND FLIGHT PATH

ENGAGEMENT Flown Path

UPLOAD

REAL TIME TRACK

FLIGHT CORRIDORS VS VOLUMES OF OPERATION

FLIGHT CORRIDORS + HOTSPOTS

VOLUME OF OPERATION powered glider

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

• peration 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

PLANNING TRAFFIC INFO

AVOID KNOWN CRITICAL SPOTS

HOTSPOTS BETTER SA VISUALIZATION

AVOID POTENTIAL CONFLICTS

ACCEPTABILITY ADSB-LIKE KALMAN FILTER

UNCERTAINTY VIS. METHODS

IN-FLIGHT REPLANNING DATA AVAILAB

MATCHING

COMPUTATION

RECONSTRUCTION KDE FLIGHT PATH SHARING

ENGAGEMENT LIBERTY SAFE

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

Trajectory ¡ management ¡ Intent ¡based ¡ surveillance ¡ State ¡based ¡ surveillance ¡

Midair ¡ ¡ collision ¡

T H A N K Y O U

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

/credits