轨迹-BERT:基于BERT轨迹预训练模型和颗粒过器算法的轨迹估计
You Wu1, Hongyi Yu1, Jianping Du1
1Information System Engineering College, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究引入了一种新的方法,使用双向编码器从变压器 (BERT) 模型的表示来提高航空轨迹数据的精度. 该方法有效地重建噪音飞行路径,改善安全性和飞行意图分析.
科学领域:
- 航空 航空 航空 航空 航空
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 轨迹数据对于航空安全和飞行意图分析至关重要.
- 噪音和信号中断会降低收集的航空数据的精度.
- 现有的方法在不完整或杂的轨迹信息中扎.
研究的目的:
- 开发一种可靠的方法来估计缺失或噪音的航空轨迹数据.
- 使用先进的人工智能模型提高飞行路径重建的准确性.
- 通过更可靠的轨迹数据提高航空安全.
主要方法:
- 使用双向编码器从变压器表示 (BERT) 模型进行轨迹数据预训练.
- 在BERT培训期间,员工根据周围的点估计了面具轨迹数据.
- 集成了一种精细的颗粒过算法,用于生成噪音数据的替代轨迹集.
- 通过BERT模型计算最大后方概率来确定最佳轨迹.
主要成果:
- 该BERT轨迹预训练模型成功地学习了复杂的运动模式.
- 结合BERT和颗粒过器的方法在轨迹重建方面表现出卓越的性能.
- 实验结果表明,拟议的模型在处理杂数据方面优于传统算法.
结论:
- 基于BERT的轨迹估计方法显著提高了数据的准确性和可靠性.
- 这种方法为提高航空安全和运营效率提供了一个有希望的解决方案.
- 该模型处理噪声的能力使其在现实世界航空应用中具有价值.
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