Efficient Estimation of Probability of Conflict Between Air Traffic Using Subset Simulation

Mishra, Chinmaya, Maskell, Simon ORCID: 0000-0003-1917-2913, Au, Siu-Kui ORCID: 0000-0002-0228-1796 and Ralph, Jason F ORCID: 0000-0002-4946-9948
(2019) Efficient Estimation of Probability of Conflict Between Air Traffic Using Subset Simulation. IEEE Transactions on Aerospace and Electronic Systems, 55 (6). pp. 2719-2742.

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This paper presents an efficient method for estimating the probability of conflict between air traffic within a block of airspace. Autonomous Sense-and-Avoid is an essential safety feature to enable Unmanned Air Systems to operate alongside other (manned or unmanned) air traffic. The ability to estimate probability of conflict between traffic is an essential part of Sense-and-Avoid. Such probabilities are typically very low. Evaluating low probabilities using naive Direct Monte Carlo generates a significant computational load. This paper applies a technique called Subset Simulation. The small failure probabilities are computed as a product of larger conditional failure probabilities, reducing the computational load whilst improving the accuracy of the probability estimates. The reduction in the number of samples required can be one or more orders of magnitude. The utility of the approach is demonstrated by modeling a series of conflicting and potentially conflicting scenarios based on the standard Rules of the Air.

Item Type: Article
Additional Information: Submitted to IEEE Transactions on Aerospace and Electronic Systems
Uncontrolled Keywords: Air traffic, benchmarking, direct Monte Carlo (DMC), Metropolis–Hastings (MH), probability of conflict, sense-and-avoid (SAA), subset simulation (SS)
Depositing User: Symplectic Admin
Date Deposited: 11 Feb 2019 08:28
Last Modified: 19 Jan 2023 01:04
DOI: 10.1109/TAES.2019.2899714
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3032606

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