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

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  • Subramanian, Abhinav
  • Mahadevan, Sankaran

Abstract

This study develops a probabilistic simulation methodology for the en-route safety assessment of multiple aircraft flying within an airspace sector. This assessment is affected by multiple sources of uncertainty, such as external inputs such as wind gust, internal factors such as aircraft component failure, and model errors (e.g., in the aircraft dynamics model) and variability in observed trajectories. The concern is to evaluate whether any pair of aircraft fail to maintain a minimum required lateral separation, and the safety assessment problem involves the prognosis of separation violations between all possible aircraft pairs within the sector, over a duration of interest. Some of the uncertainty sources are modeled as random variables and others as random processes. We combine two deterministic trajectory prediction models – the kinematics-based Generalized National Airspace Trajectory-Prediction System (GNATS), and the dynamics-based Base of Aircraft Data (BADA) – to formulate a probabilistic aircraft dynamic model which also includes model errors as additional external inputs. A computationally efficient importance sampling-based scheme is developed for this safety assessment problem involving a large number of aircraft. This approach is demonstrated using historical flight data, for aircraft operating in the Houston air traffic sector.

Suggested Citation

  • Subramanian, Abhinav & Mahadevan, Sankaran, 2022. "Importance sampling for probabilistic prognosis of sector-wide flight separation safety," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000825
    DOI: 10.1016/j.ress.2022.108410
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    References listed on IDEAS

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    1. Zhang, Xiaoqiang & Gao, Huiying & Huang, Hong-Zhong & Li, Yan-Feng & Mi, Jinhua, 2018. "Dynamic reliability modeling for system analysis under complex load," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 345-351.
    2. Wang, Zhonglai & Liu, Jing & Yu, Shui, 2020. "Time-variant reliability prediction for dynamic systems using partial information," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    3. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2020. "Time-variant reliability analysis for industrial robot RV reducer under multiple failure modes using Kriging model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    4. Du, Weiqi & Luo, Yuanxin & Wang, Yongqin, 2019. "Time-variant reliability analysis using the parallel subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 250-257.
    5. Fang, Guanqi & Pan, Rong & Hong, Yili, 2020. "Copula-based reliability analysis of degrading systems with dependent failures," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
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    Cited by:

    1. Wu, Pengcheng & Chen, Jun, 2024. "Data-driven zonotopic approximation for n-dimensional probabilistic geofencing," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    2. Mathpati, Yogesh Chandrakant & More, Kalpesh Sanjay & Tripura, Tapas & Nayek, Rajdip & Chakraborty, Souvik, 2023. "MAntRA: A framework for model agnostic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Zhong, Gang & Du, Sen & Zhang, Honghai & Zhou, Jiangying & Liu, Hao, 2024. "Demarcation method of safety separations for sUAV based on collision risk estimation," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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