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Trajectory Modeling and Prediction with Waypoint Information Using a Conditionally Markov Sequence | IEEE Conference Publication | IEEE Xplore

Trajectory Modeling and Prediction with Waypoint Information Using a Conditionally Markov Sequence


Abstract:

Information about the waypoints of a moving object, e.g., an airliner in an air traffic control (ATC) problem, should be considered in trajectory modeling and prediction....Show More

Abstract:

Information about the waypoints of a moving object, e.g., an airliner in an air traffic control (ATC) problem, should be considered in trajectory modeling and prediction. Due to the ATC regulations, trajectory design criteria, and restricted motion capability of airliners there are long range dependencies in trajectories of airliners. Waypoint information can be used for modeling such dependencies in trajectories. This paper proposes a conditionally Markov (CM) sequence for modeling trajectories passing by waypoints. A dynamic model governing the proposed sequence is obtained. Filtering and trajectory prediction formulations are presented. The use of the proposed sequence for modeling trajectories with waypoints is justified.
Date of Conference: 02-05 October 2018
Date Added to IEEE Xplore: 07 February 2019
ISBN Information:
Conference Location: Monticello, IL, USA

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