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Junwei Ma¹, Jun Tang¹, Hanyang Teng¹

¹Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.

Sensors (Basel, Switzerland)

|January 28, 2026

概括

一种新的CNN-LSTM-Attention模型提高了精确点定位 (PPP) 的卫星时钟偏差 (SCB) 预测准确度. 这种先进的模型通过克服网络中断并确保稳定,准确的结果来增强实时定位.

科学领域:

地质测量和卫星导航
人工智能在地理空间科学中的应用

背景情况:

卫星时钟偏差 (SCB) 显著影响精确定位点 (PPP) 的准确性.
国际GNSS服务 (IGS) 实时产品面临中断,影响定位可靠性.

研究的目的:

为准确和稳定的SCB预测开发一个先进的模型.
为了减轻网络中断对实时PPP的影响.

主要方法:

卷积神经网络 (CNN) 和注意力机制与长期短期记忆 (LSTM) 网络的整合.
开发CNN-LSTM-Attention模型用于特征提取和加权预测.
通过各种预测地平线和动态PPP实验进行验证.

主要成果:

与基准模型 (LP,QP,ARIMA,BP,LSTM) 相比,在多个预测期内显著提高了预测准确性.
在动态PPP实验中实现了与后加工产品相比的定位精度.
在SCB预测中表现出卓越的准确性和稳定性.

结论:

CNN-LSTM-Attention模型有效地解决了实时PPP中的SCB预测挑战.
该模型增强了定位准确性和稳定性,满足了应用需求.
这种方法为可靠的卫星导航提供了强大的解决方案.

关键词:

这是一个BDS卫星.在美国,CNN是CNN.这是LSTM的LSTM.在 SCB SCB 中,SCB 在 SCB 中.注意力机制注意力机制动态的PPP是PPP.

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