Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144322
Title: Impact of sensors on collision risk prediction for non-cooperative traffic in terminal airspace
Authors: Wang, John Chung-Hung
Tan, Shi Kun
Koay, Lynette Jie Ting
Low, Kin Huat
Keywords: Engineering::Aeronautical engineering::Aviation
Issue Date: 2018
Source: Wang, J. C.-H., Tan, S. K., Koay, L. J. T., & Low, K. H. (2018). Impact of sensors on collision risk prediction for non-cooperative traffic in terminal airspace. Proceedings of the 2018 International Conference on Unmanned Aircraft Systems, 177-185. doi:10.1109/ICUAS.2018.8453424
Abstract: The availability of off the shelf, easy to control, unmanned aerial systems (UAS) on the market has led to an increase in report of UAS incursion into terminal airspace. Such incursions often lead to airport shutdowns due to safety concern and could cause a cascading disruption to airline operations throughout the region. A better assessment tool for the collision risk between the existing air traffic and the intruder could help reduce unnecessary disruption to air traffic operations. Work has been done on the assessment of such risk using probabilistic UAS positions prediction based on Monte-Carlo simulations, under the assumption of a non-cooperative intruder with worst-case intention aiming at the flight corridor. Alert areas around the runway and the aircraft flight path could be constructed using the collision prediction method, albeit only valid under specific conditions. The accuracy of the predictions could be further improved with the incorporation of ground-based tracking equipment. This paper looks into how the availability of UAS tracking information could be used to complement the collision prediction algorithm, and how its inclusion affects the collision risk assessment.
URI: https://hdl.handle.net/10356/144322
ISBN: 978-1-5386-1354-2
DOI: 10.1109/ICUAS.2018.8453424
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work is available at: https://doi.org/10.1109/ICUAS.2018.8453424
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers

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