通过带有混合定位编码的自动回归变压器进行噪声强大的飞机轨迹预测
Youyou Li1, Yuxiang Fang1, Teng Long2
1School of Air Traffic Management, Civil Aviation Flight University of China, Chengdu, 618307, China.
本研究介绍了一种用于飞机轨迹预测的新型噪声强大的自行回归变压器,提高了噪音环境中的可靠性. 该模型提高了长期准确性和实时响应性,以实现更安全的航空旅行.
科学领域:
- 航空航天工程 航空航天工程
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 预测飞机轨迹对于空中交通管理和安全至关重要.
- 现有的模型在杂和动态的环境中缺乏稳定性.
- 挑战包括捕捉复杂的时空动态和位置信息.
研究的目的:
- 引入一种用于飞机轨迹预测的新型,耐噪声的模型.
- 为了提高轨迹预测的可靠性和准确性.
- 为了解决当前模型在处理噪音数据方面的局限性.
主要方法:
- 开发了噪声强大的自回归变压器 (NAR-T).
- 集成的噪音调节嵌入式和多头注意力与混合定位编码.
- 制定了强大的轨迹预测作为一个序列到序列的学习问题,使用自动回归方法.
主要成果:
- 在杂的场景中,NAR-T模型证明了预测可靠性的提高.
- 实现了更好的时间空间关系的捕获和精确的位置信息.
- 与现有方法相比,展示了优越的长期预测准确性和实时响应能力.
结论:
- 拟议的NAR-T模型在可靠的飞机轨迹预测方面取得了重大进展.
- 对于复杂,动态和杂的空中交通环境是有效的.
- 通过改进的预测能力,为更安全,更有效的航空旅行铺平了道路.
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