TransXLT:一种新的ZTD预测方法,使用基于SASR的数据重建
Shicheng Xie1,2, Xuexiang Yu1,2, Jiajia Yuan2,3
1School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China.
iScience
|April 25, 2025
概括
一个新的模型,变压器-xLSTM (TransXLT),通过整合全球导航卫星系统 (GNSS) 数据,ERA5和GPT3.3来提高Zenith热层延迟 (ZTD) 预测的准确性. 它有效地处理数据丢失,使用稀疏的基于注意力的时间序列重建 (SASR).
科学领域:
- 地质测量和卫星导航
- 大气科学与气象学
- 人工智能和机器学习
背景情况:
- 传统的Zenith热层延迟 (ZTD) 模型在复杂的天气和数据缺口期间难以准确.
- 全球导航卫星系统 (GNSS) 数据对于精确定位至关重要,但受到热层延迟的影响.
研究的目的:
- 为准确的ZTD估计开发一个先进的模型,解决现有方法的局限性.
- 通过整合多样化的数据源和强大的数据归算技术来提高 ZTD 预测的准确性.
主要方法:
- 开发了一个新的变压器-xLSTM (TransXLT) 模型,将GNSS,ERA5和GPT3.3的时空信息结合起来.
- 使用稀疏的基于注意力的时间序列重建 (SASR) 方法来处理缺失的GNSS数据.
主要成果:
- 在显著数据丢失的情况下,SASR将平均绝对误差 (MAE) 降低了24.5%,训练根平均平方误差 (RMSE) 降低了15.1%.
- 在6个地点,TransXLT模型实现了8.13毫米的平均RMSE,比基准性能高出76.54%.
- 该模型在不同度,海拔和季节显示出强度.
结论:
- TransXLT模型显著提高了GNSS应用中ZTD估计的准确性,特别是在具有挑战性的条件下.
- 集成先进的人工智能技术和多源数据为精确的大气参数估计提供了一个有希望的方向.
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