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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

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.

Original languageEnglish
Title of host publication2018 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages486-493
Number of pages8
ISBN (Electronic)9781538665961
DOIs
StatePublished - 2 Jul 2018
Externally publishedYes
Event56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018 - Monticello, United States
Duration: 2 Oct 20185 Oct 2018

Publication series

Name2018 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018

Conference

Conference56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018
Country/TerritoryUnited States
CityMonticello
Period2/10/185/10/18

Keywords

  • Gaussian sequence
  • Trajectory modeling and prediction
  • air traffic control (ATC)
  • conditionally Markov (CM) sequence
  • dynamic model

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