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Cited by 162Air Traffic Management and OptimizationTarget Tracking and Data Fusion in Sensor NetworksAerospace Engineering and Control Systems

The DOI matches its Crossref record.

New Algorithms for Aircraft Intent Inference and Trajectory Prediction

Yepes, Javier Lovera, Hwang, Inseok, Rotea, Mario·2007·Journal of Guidance, Control, and Dynamics
DOI 10.2514/1.26750
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Abstract

Aircraft tracking, intent inference, and trajectory predictions are important tools for enhanced capacity in air traffic control operations. In this paper, we propose an algorithm that performs these three tasks accurately. The algorithm uses a hybrid estimation algorithm to estimate the aircraft's state and flight mode. These estimates are combined with knowledge about air traffic control regulations, the aircraft's flight plan, and the environment to infer the pilot's intent. Trajectory predictions are computed as a function of the aircraft's motion (state and mode estimates) and the inferred intent The result is an algorithm that provides, in real-time, accurate intent and trajectory predictions for aircraft. We analyze and test the performance of the proposed algorithm with various scenarios representative of current and future aircraft operations within the National Airspace System.

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Cited by

Sorted by citations

  • Status quo and challenges in air transport management research

    Wandelt, S., Antoniou, C., Birolini, S., Delahaye, D., Dresner, M., Fu, X., Gössling, S., Hong, S.-J., Odoni, A. R., Zanin, M., et al. (2024) · Journal of the Air Transport Research Society · doi:10.1016/j.jatrs.2024.100014 · Cited by 67

    “Many efforts have been done in the past for improving the performances of the operation prediction even by Machine Learning ( Yepes et al., 2007 ), but trajectory prediction remains one main challenge for improving the performances of the air traffic management system.”
    Background

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Metadata

Authors
Yepes, J. L., Hwang, I., & Rotea, M.
Affiliations
Purdue University West Lafayette (US)
ORCID
Venue
Journal of Guidance, Control, and Dynamics
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Published
2007-03
Volume
30
Issue
2
Pages
370-382
ISSN
  • 0731-5090 (ISSN-L)
  • 1533-3884
DOI
10.2514/1.26750
OpenAlex
W2169030242
Cited by
162
References
0
Citations by year
2026: 7 · 2025: 11 · 2024: 10 · 2023: 9 · 2022: 6 · 2021: 19 · 2020: 15 · 2019: 8 · 2018: 20 · 2017: 9 · 2016: 9 · 2015: 10 · 2014: 1 · 2013: 5 · 2012: 9
FWCI
6.41
Type
journal article
Language
en
Open access
closed
Locations
Journal of Guidance Control and Dynamics (publishedVersion)
Funders
Purdue University
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
  • Physical Sciences › Engineering › Aerospace Engineering › Aerospace Engineering and Control Systems
Keywords
trajectory prediction, aircraft tracking, hybrid estimation, air traffic control, flight plan, real-time prediction, National Airspace System

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CoChat. (2026). Citation check: New Algorithms for Aircraft Intent Inference and Trajectory Prediction. https://app.cochat.ai/papers/10.2514/1.26750
The paper
Yepes, J. L., Hwang, I., & Rotea, M. (2007). New Algorithms for Aircraft Intent Inference and Trajectory Prediction. Journal of Guidance, Control, and Dynamics. https://doi.org/10.2514/1.26750