Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144480
Title: Data analysis on track deviation of UAS operating under Visual Line of Sight (VLoS) condition
Authors: Wang, John Chung-Hung
Low, Kin Huat
Ng, Ee Meng
Chan, E. Yang Jie
Keywords: Engineering::Aeronautical engineering::Aviation
Issue Date: 2020
Source: Wang, J. C.-H., Low, K. H., Ng, E. M., & Chan, E. Y. J. (2020). Data analysis on track deviation of UAS operating under Visual Line of Sight (VLoS) condition. Proceedings of the AIAA Aviation 2020 Forum. doi:10.2514/6.2020-2899
Abstract: The management of collision risk posed by recreational unmanned aerial systems (UAS) intruding into controlled airspace is becoming more critical with the surge in accessibility and popularity of these UAS. Risk mitigation actions that could be taken by the airport operators currently are limited by the lack of reliable UAS detection equipment, which limits their ability to track UAS positions over time and predict the collision risks posed by the UAS. While recent developments in airborne collision prevention of manned aircraft could utilize Markov Decision Process with state probabilities based on historical flight track records and processed using Bayesian Network, this method is not suitable for the off-nominal case of UAS intrusion into controlled airspace. Instead, the prediction of collision risk posed by non-cooperative recreational UAS have to rely on the assumption of worst-case intention, where the UAS aims for the aircraft operating within the aerodrome, and the Reich collision risk model to generate the probable distribution of future UAS positions. This paper documents a series of flight test to simulate such scenario with a UAS operating under visual line of sight condition while aiming for an imaginary three dimensional target in the air. The data was analyzed for the deviation in UAS positions at fixed time interval in the (horizontal) longitudinal and lateral direction, as well as the deviation in altitude. A comparison between the observed deviation and a Monte-Carlo based UAS path prediction following the UAS flight dynamic model were also conducted.
URI: https://hdl.handle.net/10356/144480
ISBN: 978-1-62410-598-2
DOI: 10.2514/6.2020-2899
Rights: © 2020 Nanyang Technological University Singapore. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers

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