基于神经网络的ADS-B故障条件下的飞机轨迹预测和航空安全
Zhanji Yang1, Xiaolei Kang2, Yuanhao Gong3
1Naval Petty Officer Academy, Bengbu, 233010, China. zhanji.yang@outlook.com.
Scientific reports
|November 11, 2023
概括
本研究引入了一种使用历史自动依赖监控广播 (ADS-B) 数据来预测飞机轨迹的新方法. 双向长短期记忆 (Bi-LSTM) 网络通过预测飞行路径,特别是信号中断期间,提高了航空安全.
科学领域:
- 航空航天工程 航空航天工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 全球飞行需求正在增加,挑战当前的空中交通管理 (ATM) 系统.
- 传统的雷达系统不足以满足现代飞行安全要求.
- 自动依赖监控广播 (ADS-B) 是下一代技术,但容易因设备故障或干扰而导致信号损失.
研究的目的:
- 通过解决ADS-B信号中断问题,在繁忙的空域提高航空安全.
- 开发一种可靠的方法来短期预测飞机轨迹.
- 提高空中交通监控的准确性和可靠性.
主要方法:
- 利用历史的ADS-B数据进行动态飞机建模.
- 实施了对ADS-B原始数据的数据清理方法.
- 开发了一种双向长期短期内存 (Bi-LSTM) 网络框架,用于轨迹预测.
主要成果:
- 拟议的Bi-LSTM方法证明了飞机轨迹的高预测准确性.
- 有效地解决了各种飞机类型的动态建模方面的挑战.
- 在准确性方面表现优于其他7种预测方法.
结论:
- Bi-LSTM网络预测方法通过准确预测飞机轨迹,显著提高了航空安全.
- 该方法为ADS-B信号中断提供了可靠的解决方案.
- 历史数据的利用和先进的深度学习增强了空中交通监控能力.
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