基于BP神经网络的ELoran传播延迟预测模型用于复杂的气象环境
Shiyao Liu1,2, Wei Guo1,2, Yu Hua1,2
1National Time Service Center, Chinese Academy of Sciences, Xi'an 710600, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种反向传播神经网络 (BPNN) 模型,用于预测eLoran计时系统中天气变化引起的地面波传播延迟波动. BPNN模型准确地预测了这些延迟,提高了系统计时的准确性.
科学领域:
- 导航系统 导航系统
- 地质物理学 地质物理学
- 人工智能的人工智能
背景情况:
- 埃洛兰的地面定时导航系统依赖于精确的地面波传播延迟测量.
- 气象变化显著影响传播延迟,特别是在复杂的陆地环境中,影响系统计时精度.
研究的目的:
- 为复杂的气象条件开发一个强大的传播延迟预测模型.
- 为了减轻在eLoran系统中因天气引起的计时不准确性的影响.
主要方法:
- 对影响传播延迟组件的气象因素的理论分析.
- 对测量数据的相关性分析,以确定气象因素和传播延迟之间的关系.
- 开发和验证一个包含区域气象变化的逆向传播神经网络 (BPNN) 模型.
主要成果:
- 证明了七个主要气象因素和传播延迟之间的复杂关系,包括区域差异.
- 拟议的BPNN模型有效地预测了几天的传播延迟波动.
- 与现有的线性和简单神经网络模型相比,显著提高了性能.
结论:
- BPNN模型提供了一种可行的解决方案,用于预测和补偿eLoran计时系统中的气象干扰.
- 精确预测传播延迟波动可以提高地面定时导航的整体可靠性和精度.
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