将半参数和机器学习方法结合起来,用于对卫星时钟偏差的短期预测
Lihong Jin1, Wanzhuo Zhao2, Xiong Pan3
1School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, 430200, China.
Scientific reports
|April 8, 2025
概括
一个新的半LFA-Informer (SLFAI) 模型提高了卫星时钟偏差 (SCB) 预测的准确性. 这种综合方法提高了高精度定位系统的计算效率和概括性.
科学领域:
- 地质测量和地质数学
- 卫星导航系统 卫星导航系统
- 时间序列分析时间序列分析
背景情况:
- 精确的卫星时钟偏差 (SCB) 建模对于高精度定位至关重要.
- 半参数模型和神经网络等现有方法在内核选择和参数初始化方面存在局限性.
- 在SCB时间序列中的非线性,非静态性和组件重叠带来了建模挑战.
研究的目的:
- 引入一个新的集成模型,半LFA-Informer (SLFAI),用于增强SCB预测.
- 为了解决当前SCB建模技术的实际局限性.
- 提高北斗卫星导航系统 (BDS-3) SCB预测的准确性和效率.
主要方法:
- 开发了SLFAI模型,将半参数技术与优化的自我注意神经网络集成在一起.
- 应用了SLFAI模型来预测BDS-3的SCB.
- 将SLFAI性能与二次多项式 (QP),光谱分析 (SA) 和长短期记忆 (LSTM) 网络进行比较.
主要成果:
- 与QP,SA和LSTM相比,SLFAI模型显示出更高的预测稳定性和准确性.
- 实现了平均预测准确度超过0.15 ns,0.25 ns和0.35 ns,分别为3,6和12小时的预测.
- 在3,6小时和12小时的预测中,平均预测准确度分别提高了53.6%,59.4%和43.5%.
结论:
- SLFAI模型有效地克服了SCB预测中的概括能力问题.
- 拟议的方法显著提高了计算效率和预测准确度.
- SLFAI为卫星时钟偏差建模和预测提供了一种新的,高质量的方法.
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