<br>基于改进LSTM神经网络的新型全球ZTD预测模型,IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

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基于改进LSTM神经网络的新型全球ZTD预测模型
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 4.7 ) Pub Date : 4-22-2024 , DOI: 10.1109/jstars.2024.3391821
Lin He ₁ , Yibin Yao ₁ , Chaoqian Xu ₂ , Huan Zhang ₃ , Feifei Tang ₄ , Changquan Ji ₄ , Zhuoya Liu ₅ , Wentan Wu ₆

Affiliation

天顶对流层延迟（ZTD）由天顶静水延迟（ZHD）和天顶湿延迟（ZWD）组成，是导致使用全球导航卫星系统（GNSS）精密单点定位（PPP）和实时定位的精确定位误差的重要因素。时间运动学技术。全球范围内准确、及时的 ZTD 预测对于提高 GNSS 定位精度和加快收敛至关重要。本研究提出了一种创新的全球对流层预测模型，利用长短期记忆（LSTM）神经网络，旨在实现 ZTD 的高精度和长期预测能力。使用的实验数据来自维也纳映射函数 3 优化的天顶总延迟 (ZTD) 数据集。本研究通过提取周期信号进一步深入分析 ZTD 残差。然后利用 ZTD 残差来训练修改后的 LSTM 神经网络模型，从而能够预测全局残差。最终的 ZTD 预测是通过将修改后的 LSTM ZTD 残差预测分量与 ZTD 周期分量相结合获得的。我们的结果表明，2020 年修改后的 LSTM-ZTD 模型的平均均方根误差 (RMSE) 为 1.44 cm。此外，预测ZTD在春季、夏季、秋季和冬季的平均RMSE分别为1.43厘米、1.47厘米、1.56厘米和1.36厘米。通过将 LSTM 神经网络与物理算法提取的 ZTD 周期信号相结合，这项工作成功地提高了全球范围内 ZTD 预测的准确性和时间跨度。

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A New Global ZTD Forecast Model Based on Improved LSTM Neural Network

Zenith tropospheric delay (ZTD), consisting of zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD), is a significant contributor to errors in precise positioning using the global navigation satellite system (GNSS) precise point positioning (PPP) and real-time kinematic techniques. Accurate and timely predictions of ZTD on a global scale are crucial for enhancing GNSS positioning accuracy and expediting convergence. This study proposes an innovative global tropospheric prediction model that leverages long short-term memory (LSTM) neural networks, aiming to achieve both high precision and long-term prediction capability for ZTD. The experimental data utilized were sourced from the Vienna Mapping Functions 3-Optimized zenith total delay (ZTD) dataset. This study delves further into the analysis of ZTD residuals by extracting periodic signals. The ZTD residuals were then utilized to train a modified LSTM neural network model, enabling the prediction of global residuals. The final ZTD predictions were obtained by combining the modified LSTM ZTD residual forecast component with the ZTD periodic component. Our results demonstrate that the average root-mean-square error (RMSE) of the modified LSTM-ZTD model in 2020 was 1.44 cm. In addition, the average RMSE of the forecasted ZTD during spring, summer, autumn, and winter was found to be 1.43 cm, 1.47 cm, 1.56 cm, and 1.36 cm, respectively. Through the integration of the LSTM neural network and the ZTD periodic signal extracted using a physical algorithm, this work has successfully enhanced the accuracy and time span of ZTD forecasts on a global scale.

更新日期：2024-08-19

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