基于LSTM的卫星导航系统服务性能监测方法研究

在卫星导航系统中，对系统服务性能进行长期可靠的监测，是实现高精度、全覆盖、全天候卫星导航的关键。为进一步提高卫星导航系统服务性能监测分析的准确性与稳定性，利用分布全球的监测站监测数据，在PDOP和定位精度两方面进行解算分析，提出并实现了一种基于LSTM神经网络的卫星导航系统服务性能监测分析方法。实验结果表明，在PDOP方面，BDS和GPS基于LSTM预测的结果比根据星历数据预测出的结果均值准确度分别提高了5.15%和3.89%；在定位精度方面，BDS和GPS基于LSTM预测的结果比根据PDOP和用户等效测距误差预测出的结果均值准确度分别提高了79.64%和73.77%。由此可知，LSTM网络预测结果比依据星历数据的预测结果更好，其中定位精度的预测质量大幅度优于预测结果。该方法可依据PDOP与定位精度历史数据对数据未来的变化趋势进行有效预测，对系统服务性能进行态势跟踪，进而为系统服务性能预警提供参考依据。

Research on LSTM-based Performance Monitoring Techniques for Satellite Navigation Systems Services

To achieve high-precision, full-coverage, all-weather satellite navigation, the performance of the system''s services must be continuously and reliably monitored. A method of satellite navigation system service performance monitoring and analysis based on Long Short Term Memory(LSTM) neural network is proposed and implemented by using the monitoring data of monitoring stations around the world to solve and analyze position dilution of precision(PDOP) and positioning accuracy in order to further improve the accuracy and stability of satellite navigation system service monitoring and analysis. The experimental results demonstrate that, for PDOP, the mean accuracy of the results predicted by BDS and GPS based on LSTM is 5.15 and 3.89 percent higher than that predicted by ephemeris data, respectively; for positioning accuracy, the mean accuracy of the results predicted by BDS and GPS based on LSTM is 73.77% and 79.64% higher than that predicted by PDOP and user equivalent ranging error, respectively. It can be seen that the predictions made using the LSTM network outperform those made using ephemeris data in terms of prediction quality and localization accuracy. The method can effectively predict the future trend of data based on the historical data of PDOP and positioning accuracy, track the system service performance, and provide a reference basis for system service performance warning.