Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data Article Swipe
YOU?
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· 2018
· Open Access
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· DOI: https://doi.org/10.1109/tits.2018.2877572
· OA: W2895874880
Models for predicting aircraft motion are an important component of modern\naeronautical systems. These models help aircraft plan collision avoidance\nmaneuvers and help conduct offline performance and safety analyses. In this\narticle, we develop a method for learning a probabilistic generative model of\naircraft motion in terminal airspace, the controlled airspace surrounding a\ngiven airport. The method fits the model based on a historical dataset of\nradar-based position measurements of aircraft landings and takeoffs at that\nairport. We find that the model generates realistic trajectories, provides\naccurate predictions, and captures the statistical properties of aircraft\ntrajectories. Furthermore, the model trains quickly, is compact, and allows for\nefficient real-time inference.\n
