The current satellite management system mainly relies on manual work. If small faults cannot be found in time, it may cause systematic fault problems and then affect the accuracy of satellite data and the service quality of meteorological satellite. If the operation trend of satellite will be predicted, the fault can be avoided. However, the satellite system is complex, and the telemetry signal is unstable, nonlinear, and time-related. It is difficult to predict through a certain model. Based on these, this paper proposes a bidirectional long short-term memory (BiLSTM) deep leaning model to predict the operation trend of meteorological satellite. In the method, the layer number of the model is designed to be two, and the prediction results, which are forecasted by LSTM network as the future trend data and historical data, are both taken as the input of BiLSTM model. The dataset for the research is generated and transmitted from Advanced Geostationary Radiation Imager (AGRI), which is the load of FY4A meteorological satellite. In order to demonstrate the superiority of the BiLSTM prediction model, it is compared with LSTM based on the same dataset in the experiment. The result shows that the BiLSTM method reports a state-of-the-art performance on satellite telemetry data.

中文翻译：

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使用 BiLSTM 深度学习模型的气象卫星运行预测

目前的卫星管理系统主要依靠人工。如果不能及时发现小故障，可能会造成系统故障问题，进而影响卫星数据的准确性和气象卫星的服务质量。如果能预测卫星的运行趋势，就可以避免故障。但卫星系统复杂，遥测信号不稳定、非线性、时间相关。很难通过某种模型进行预测。基于这些，本文提出了一种双向长短期记忆（BiLSTM）深度学习模型来预测气象卫星的运行趋势。该方法将模型层数设计为2层，预测结果由LSTM网络预测为未来趋势数据和历史数据，都作为 BiLSTM 模型的输入。用于研究的数据集由高级地球静止辐射成像仪（AGRI）生成并传输，这是 FY4A 气象卫星的负载。为了证明 BiLSTM 预测模型的优越性，在实验中将其与基于相同数据集的 LSTM 进行了比较。结果表明，BiLSTM 方法报告了卫星遥测数据的最新性能。