Statistical mid-air collision risk assessment

Lee, Sam Sangsueng (2021) Statistical mid-air collision risk assessment. Masters thesis, Memorial University of Newfoundland.

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This thesis describes current Detect and Avoid (DAA) equipment and communication methods among aircraft, Air Traffic Control (ATC) and Uninhabited Aerial Vehicle (UAV) Ground Control Stations (GCS). The limitations of current DAA systems are explored and analyzed to evaluate the effectiveness of each surveillance equipment combination. Notwithstanding the ongoing COVID-19 pandemic, future air traffic is predicted to increase and the airspace to become very congested due to the popularity of air travel and the increasing usage of UAVs. Background information is provided for the current recommendations for the definitions of Loss of Separation (LOS) and for determining Well-Clear (WC), Loss-of- Well-Clear (LOWC), unmitigated Near-Miss Air Collision (NMAC) and mitigated NMAC for detecting any intruder aircraft and maintaining separation to avoid any risk of a Mid-Air Collision (MAC). The primary contributions of this work are the determination of the impact of possible combinations of surveillance equipment that could be installed on current manned and unmanned aircraft, the estimated reduction of risk of violation of WC boundaries and thus the probability for maintenance of safe aircraft separation. The impact on mid-air collision risk is determined by considering the impact of DAA/Surveillance Equipment on the MAC (and NMAC) risk ratio. The concepts herein focus on existing technologies as used in manned aviation and their possible extension to use in UAS operations. The integration of such technologies into future DAA systems is the major recommendation for future study.

Item Type: Thesis (Masters)
Item ID: 15183
Additional Information: Includes bibliographical references (pages 158-168).
Keywords: Mid-Air Collision (MAC), risk assessment, Collision Avoidance System (CAS), aviation, Unmanned Aerial Vehicle (UAV), air navigation service, air traffic management
Department(s): Engineering and Applied Science, Faculty of
Date: May 2021
Date Type: Submission
Digital Object Identifier (DOI):
Library of Congress Subject Heading: Airplanes--Collision avoidance--Risk assessment; Aeronautics--Safety measures; Drone aircraft.

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