Importance sampling for probabilistic prognosis of sector-wide flight separation safety

Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

“…A well-defined but unknown probability distribution is applied with the assumption that there exists some underlying random process which generates the values of these variables. Naive Bayes is a widely used Probabilistic model whereas in ATC system Probabilistic models like Bayes theorem is used in [4,7,8,11,12,20].…”

“…The research in [11] develops a probabilistic simulation methodology for detecting lateral separation violations in en-route safety assessment of multiple aircraft flying within an airspace sector over a particular duration while considering the multiple factors of uncertainty. This study uses FlightAware's public ADS-B database which consists of historical trajectory about position of an aircraft flying over Houston air traffic sector containing information such as altitude, latitude, longitude, course angle and true air speed, at a sampling rate of 16 s. The methodology makes use of two prediction models known as kinematics-based Generalized National Airspace Trajectory-Prediction System (GNATS), and the dynamics-based Base of Aircraft Data (BADA) in order to find trajectory and devise aircraft dynamic model which also include model error as an input.…”

“…It is required to provide constant monitoring in order to establish highest levels of aircraft safety. Moreover, the manual safety systems implemented at many airports of the world are time consuming and lack the ability to make efficient intime decisions about flight [11]. Many problems and challenges are faced by them such as air traffic congestion, flight safety failures, increased delays and higher costs [12].…”

Combined importance sampling and separable Monte Carlo: analytical variance estimator and applications to structural reliability

“…Importance Sampling (IS), another popular variance reduction technique Tokdar & Kass (2010), is based on the concentration of the sampling in specific areas defined by a support distribution. While IS has been initially developed in the context of reliability several decades ago, it continues to be a very active topic with many recent developments seeking to further improve it [Papaioannou et al (2019), Chaudhuri et al (2020), Nadjafi & Najafi ARK (2021), Tabandeh et al (2022)], together with industrial applications, like [Gao et al (2020), Misraji et al (2020), Saaed & Daghigh (2021), Liu et al (2022), Subramanian & Mahadevan (2022)] among others. One open problem is given by the most efficient construction of the support importance distribution of IS.…”