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Aerospace 2019, 6(2), 19; https://doi.org/10.3390/aerospace6020019

Probabilistic Safety Assessment for UAS Separation Assurance and Collision Avoidance Systems

1
Department of Mechanical Engineering, University of North Dakota, Grand Forks, ND 58202, USA
2
School of Engineering, Aerospace Engineering and Aviation, RMIT University, Bundoora, VIC 3083, Australia
*
Author to whom correspondence should be addressed.
Received: 6 September 2018 / Revised: 17 January 2019 / Accepted: 17 January 2019 / Published: 14 February 2019
(This article belongs to the Special Issue Civil and Military Airworthiness: Recent Developments and Challenges)
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Abstract

The airworthiness certification of aerospace cyber-physical systems traditionally relies on the probabilistic safety assessment as a standard engineering methodology to quantify the potential risks associated with faults in system components. This paper presents and discusses the probabilistic safety assessment of detect and avoid (DAA) systems relying on multiple cooperative and non-cooperative tracking technologies to identify the risk of collision of unmanned aircraft systems (UAS) with other flight vehicles. In particular, fault tree analysis (FTA) is utilized to measure the overall system unavailability for each basic component failure. Considering the inter-dependencies of navigation and surveillance systems, the common cause failure (CCF)-beta model is applied to calculate the system risk associated with common failures. Additionally, an importance analysis is conducted to quantify the safety measures and identify the most significant component failures. Results indicate that the failure in traffic detection by cooperative surveillance systems contribute more to the overall DAA system functionality and that the probability of failure for ownship locatability in cooperative surveillance is greater than its traffic detection function. Although all the sensors individually yield 99.9% operational availability, the implementation of adequate multi-sensor DAA system relying on both cooperative and non-cooperative technologies is shown to be necessary to achieve the desired levels of safety in all possible encounters. These results strongly support the adoption of a unified analytical framework for cooperative/non-cooperative UAS DAA and elicits an evolution of the current certification framework to properly account for artificial intelligence and machine-learning based systems. View Full-Text
Keywords: unmanned aircraft systems; sense and avoid; unified analytical framework; ADS-B; surveillance sensor; fault tree analysis; importance measure unmanned aircraft systems; sense and avoid; unified analytical framework; ADS-B; surveillance sensor; fault tree analysis; importance measure
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Tabassum, A.; Sabatini, R.; Gardi, A. Probabilistic Safety Assessment for UAS Separation Assurance and Collision Avoidance Systems. Aerospace 2019, 6, 19.

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