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一种基于机器学习的热层预测方法,用于高精度实时GNSS定位
1Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
一种新的机器学习方法可以实时预测全球导航卫星系统 (GNSS) 应用的热层错误. 这种方法克服了现有数据的延迟问题,实现了高精度的精确定位.
科学领域:
- 地质测量和地质数学
- 大气科学 大气科学
- 机器学习应用 机器学习应用
背景情况:
- 热带层的错误对全球导航卫星系统 (GNSS) 的高精度定位产生了重大影响,达到米.
- 目前的实时热带层校正服务的区域可用性有限.
- 国际GNSS服务 (IGS) 的现有任务后数据的延迟时间为1-2周,不适合实时使用.
研究的目的:
- 为GNSS应用开发一个实时热层预测方法.
- 为了克服当前热层校正数据的延迟限制.
- 通过减轻实时热层误差,实现高精度定位.
主要方法:
- 开发了一个实时热层预测模型,利用机器学习技术.
- 杆国际GNSS服务 (IGS) 后处理产品作为输入数据.
- 使用一年长的数据集验证了预测方法.
主要成果:
- 取得了2厘米的根平均平方误差 (RMSE) 对于热层预测.
- 证明了开发的方法对实时应用的适用性.
- 成功地消除了与传统的任务后数据相关的长延迟.
结论:
- 拟议的基于机器学习的方法为GNSS提供了准确的实时热层预测.
- 这一进步通过解决一个关键的错误源来支持高精度定位应用程序.
- 该方法为现有服务提供了可行的替代方案,其覆盖范围有限或延迟高.
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