Open Access

ARTICLE

The Lateral Conflict Risk Assessment for Low-altitude Training Airspace Using Weakly Supervised Learning Method

Kaijun Xu¹, Xueting Chen², Yusheng Yao¹, Shanshan Li¹

1 Institute of Flight Technology, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China
2 Institute of Foreign Languages, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China

* Corresponding Author: Kaijun Xu,

Intelligent Automation & Soft Computing 2018, 24(3), 603-611. https://doi.org/10.31209/2018.100000027

Abstract

The lateral conflict risk assessment of low-altitude training airspace strategic planning, which is based on the TSE errors has always been a difficult task for training flight research. In order to effectively evaluate the safety interval and lateral collision risk in training airspace, in this paper, TSE error performance using a weakly supervised learning method was modelled. First, the lateral probability density function of TSE is given by using a multidimensional random variable covariance matrix, and the risk model of a training flight lateral collision based on TSE error is established. The lateral conflict risk in specific training airspace is analyzed, and then the lateral collision model is built. Through the quantification of the risk probability of lateral collisions, the security level of a specific airspace is evaluated. The analysis of the examples shows that for normal training flight in a variety of 4D flight track data, the lateral collision risk in specific training airspace is 0.543744 × 10^-13, the conflict risk meets the requirement of safety target level of international civil aviation organization.

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