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Semantic Scholar · Article

Empirical study of airport geofencing for unmanned aircraft operation based on flight track distribution

Published 2020-12-01 5 authors

Attribution

This is the abstract and citation. Full text lives at Semantic Scholar — we link out rather than host. All credit to the authors and Semantic Scholar.

Abstract

Verbatim from Semantic Scholar. Not paraphrased, not summarized.

Abstract With the increasing activities of unmanned aircraft (UA) near airports, severe challenges have emerged regarding airport safety management. This paper presents a brand-new data-driven based approach to accurately design the geofencing for UAs around an airport, focusing on midair collision risk between commercial flights and small UAs which is considered as the most important hazard. In premise, prevalent trajectory extraction from real flight tracks is realized based on an advanced and general model, taking the advantage of describing turning legs which is more popular in terminal area. As a key solution, a Gauss-Laplace-composite distribution is provided for rigorous estimation of flight track distribution in line with the extracted prevalent trajectory. Based on those, airport geofencing is proposed consisting of an inner critical area and an outer buffer area. This approach can be adaptive for different traffic patterns of an airport. The buffer area is an extension range to help the execution of some defense measures. Results of an empirical study for setting up geofencing for small UAs around Chongqing Jiangbei International Airport (ZUCK) show the superiority of the proposed approach, which indicates that it can provide practical application, especially varying from different target level of safety.

Authors

  • Zhang Jianping
  • Xiang Zou
  • Wu Qinggang
  • Xie Fangquan
  • Liu Weidong

Keywords

  • Computer Science
  • Engineering
  • Environmental Science

Citation: Zhang Jianping, Xiang Zou, Wu Qinggang , et al. (2020). Empirical study of airport geofencing for unmanned aircraft operation based on flight track distribution. Semantic Scholar ID dfb7dea2d1c1693227fa4bd16f20a6ca6125913e. https://doi.org/10.1016/j.trc.2020.102881 ↗