系统仿真学报 ›› 2018, Vol. 30 ›› Issue (12): 4703-4711.doi: 10.16182/j.issn1004731x.joss.201812026

• 仿真应用工程 • 上一篇    下一篇

基于状态预测的空中防撞系统多机避碰性能改进方法

汤俊, 朱峰, 万宇, 老松杨   

  1. 国防科学技术大学,系统工程学院,湖南 长沙 410073
  • 收稿日期:2018-06-28 修回日期:2018-07-29 出版日期:2018-12-10 发布日期:2019-01-03
  • 作者简介:汤俊(1988-),男,安徽安庆,博士,讲师,研究方向为航空安全管理; 朱峰(1985-),男,湖北咸宁,博士,讲师,研究方向为复杂系统建模; 万宇(1994-),男,四川眉山,硕士,研究方向为飞机防撞。
  • 基金资助:
    国家自然科学基金(71601181)

Collision Avoidance Performance Improvement for TCAS in Multi-Aircraft Situations Based on State Prediction

Tang Jun, Zhu Fen, Wan Yu, Lao Songyang   

  1. College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China
  • Received:2018-06-28 Revised:2018-07-29 Online:2018-12-10 Published:2019-01-03

摘要: 目前随着空中交通流量的大幅提升,空域密度不断增加,发生双机、多机冲突的可能性增大,但空中防撞系统(TCAS)不能解决所有多机态势下的防撞问题。因此迫切需要提高其在多机态势下的防撞性能。本文通过数学描述传统的TCAS防撞机制,并实现其在水平方向上的拓展,从而提出基于状态预测的垂直与水平方向综合优化的TCAS避碰策略选择算法。算法以高度和垂直速度调整为核心,辅以水平变向,多机协同,采取最优化的策略避免碰撞。仿真实验验证了方法的有效性。

关键词: 空中交通管理, 空中防撞系统, 多机态势, 状态预测, 碰撞风险

Abstract: Currently the density of the airspace increases with the air traffic flow increasing significantly, and therefore the conflict occurrence possibility of two or multiple aircraft raises. However, the traffic collision avoidance system (TCAS) may not be able to resolve all the collision problems of multi-threat situation. There is an active demand to improve its performance in multi-threat situation. This paper mathematically describes the traditional TCAS anti-collision mechanism, and achieves its expansion in the horizontal direction, thus proposes the improvement algorithm of collision avoidance performance for TCAS in multi-aircraft situations based on the state prediction. Adjusting the height and vertical speed are used as the core in this algorithm, course change and multiple aircraft cooperation are combined to choose the optimization strategy to avoid collision. Simulation results demonstrate the effectiveness of our approaches.

Key words: air traffic management, TCAS, multi-aircraft situations, state prediction, collision risk

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