北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (3): 576-582.doi: 10.13700/j.bh.1001-5965.2017.0159

• 论文 • 上一篇    下一篇

一种基于SVM的低空飞行冲突探测算法

韩冬1, 张学军1, 聂尊礼1, 管祥民2   

  1. 1. 北京航空航天大学电子信息工程学院, 北京 100083;
    2. 中国民航管理干部学院, 北京 100102
  • 收稿日期:2017-03-17 出版日期:2018-03-20 发布日期:2017-05-10
  • 通讯作者: 张学军 E-mail:zhxj@buaa.edu.cn
  • 作者简介:韩冬,男,博士研究生。主要研究方向:航空监视、航空电子;张学军,男,博士,教授,博士生导师。主要研究方向:航空数据通信系统、航空电子技术、现代空中交通管理技术。
  • 基金资助:
    国家自然科学基金(U1533119)

A conflict detection algorithm for low-altitude flights based on SVM

HAN Dong1, ZHANG Xuejun1, NIE Zunli1, GUAN Xiangmin2   

  1. 1. School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
    2. Civil Aviation Management Institute of China, Beijing 100102, China
  • Received:2017-03-17 Online:2018-03-20 Published:2017-05-10
  • Supported by:
    National Natural Science Foundation of China (U1533119)

摘要: 随着低空飞行密度不断增加,低空航行安全已引起广泛关注,由于低空环境复杂,低空飞行受地面障碍物和天气影响比商用航空显著,传统的空中交通警戒与防撞系统(TCAS)和其他冲突探测方法并不适用于低空密集飞行环境。针对传统探测方法计算量大、适用性差的不足,引入支持向量机(SVM)的二元分类方法,通过对本机和周边飞机航迹归一化处理,采用智能优化算法对关键参数进行优化,利用模拟数据对分类器进行预先训练,实现了适用于低空飞行的高效冲突探测。以大量的仿造数据对算法有效性进行了测试验证,结果表明漏警率和误警率分别控制在约0.1%和6%,克服了传统确定型方法与概率型方法难以兼顾效率与适用性的缺陷。

关键词: 通用航空, 冲突探测, 支持向量机(SVM), GA-PSO, 智能优化算法

Abstract: With the continuous increasing of flight density, the aviation safety in low altitude has caused extensive concern. Low-altitude environment is complex, and ground obstacles and weather have more significant impact on low-altitude flight than commercial aviation. Traditional traffic alert and collision avoidance system (TCAS) and other methods may not be applicable to low-altitude intensive flight environment. In view of the computational complexity and lack of applicability of traditional detection methods, a binary classification method of support vector machine (SVM) was introduced. By normalizing the trajectories of own and surrounding aircraft, optimizing the key parameters by intelligent optimization algorithm, and pre-training the classifier through simulation data, efficient conflict detection for low-altitude flight was carried out. Various sets of artificial data were utilized to verify the effectiveness of the algorithm. The results show that the missed alarm rate and false alarm rate are controlled at about 0.1% and 6% respectively, which proves that the proposed algorithm can overcome the shortcomings of traditional deterministic and probabilistic methods which are difficult to take both the efficiency and applicability into account.

Key words: general aviation, conflict detection, support vector machine(SVM), GA-PSO, intelligent optimization algorithm

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