Data-driven Conflict Detection Enhancement in 3D Airspace with Machine Learning

Zhengyi Wang, M. Liang, D. Delahaye
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引用次数: 4

Abstract

Trajectory prediction with Closest Point of Approach (CPA) concept is a fundamental element of aircraft Conflict Detection (CD) problem. Conventional motion-based CPA prediction model generally assumes that aircraft is flying in straight line with constant speed. But due to environment uncertainties and ground speed changes, this conventional method frequently lacks accuracy in the real world with a high rate of false alarms and missed detections. In this paper, we introduce a novel automated data-driven CD framework with Machine Learning (ML) for 3D CPA prediction in a lookahead time of less than 20 minutes. Firstly, a 3D CPA model with cylindrical norm is proposed as the baseline. Then, data preparation with Mode-S observation data in France is explained, including data collection and data processing, to convert raw Mode-S data to the close-to-reality dataset. Furthermore, feature engineering is applied to build up a feature set with 16 features. Finally, four prevailing ML models are used to predict the time, horizontal distance and vertical distance of CPA in 3D airspace. CD is conducted based on the predicted values. The prediction and CD results show that all proposed ML models outperform the baseline model. Especially, GBM and FFNNs could strongly enhance the performance of CD.
基于机器学习的三维空域数据驱动冲突检测增强
基于最接近点(CPA)概念的轨迹预测是飞机冲突检测(CD)问题的一个基本要素。传统的基于运动的CPA预测模型一般假设飞行器沿直线匀速飞行。但由于环境的不确定性和地面速度的变化,这种传统方法在现实世界中往往缺乏准确性,存在较高的误报率和漏检率。在本文中,我们引入了一种新颖的自动数据驱动CD框架,该框架具有机器学习(ML),用于在不到20分钟的时间内预测3D CPA。首先,提出了以柱面范数为基准的三维CPA模型;然后介绍了利用法国的Mode-S观测数据进行数据准备,包括数据采集和数据处理,将原始Mode-S数据转化为接近真实的数据集。在此基础上,利用特征工程构建了包含16个特征的特征集。最后,利用四种流行的ML模型预测三维空域中CPA的时间、水平距离和垂直距离。CD是根据预测值进行的。预测和CD结果表明,所有提出的ML模型都优于基线模型。特别是,GBM和ffnn能显著提高CD的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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