ARIMA-SVR组合模型在卫星遥测参数预测中的应用

为辅助卫星在轨运行提供决策分析支持，结合卫星遥测参数的时间序列特性，利用一种ARIMA-SVR组合预测方法，通过对卫星遥测参数进行预测，判定实际遥测数据是否处于正常范围。该组合模型利用ARIMA模型对预处理后的数据进行线性拟合，并利用SVR模型对数据的非线性部分进行补偿。以KX09卫星星敏A的温度遥测数据为基础，分别利用组合模型对短期及中期星敏A温度进行预测，得出短期和中期均方根误差（RMSE）分别为0.768和0.968，相比单一ARIMA模型，短中期RMSE分别提高46.2%和16.4%。此外，对该卫星陀螺B的x轴角速度进行了短中期预测：短期预测中，组合模型比单一ARIMA模型的RMSE提高71.2%；中期预测中，组合模型比单一ARIMA模型的RMSE提高64.2%。实验结果表明，ARIMA-SVR组合模型为保证卫星在轨正常运行提供了有效的决策分析支持。

Research on the Application of ARIMA-SVR Combination Model in Satellite Telemetry Parameter Prediction

In order to provide decision analysis support for assisting the in-orbit operation of satellite, combining with the time series characteristics of satellite telemetry parameters, an ARIMA-SVR combination prediction method is used to judge whether the actual telemetry data is in the normal range through the prediction of satellite telemetry parameters. In this model, ARIMA model is used to linearly fit the preprocessed data, and SVR model is used to compensate the nonlinear part of the data. Based on the telemetry data of temperature of star sensor A in KX09 satellite, the combination model was used to predict the short-term and medium-term temperature of star sensor A, and the Root Mean Square Error (RMSE) of the short-term and medium-term temperature of star sensor A was 0.768 and 0.968, respectively. Compared with the single ARIMA model, the RMSE of the short and medium-term temperature of star sensor A was 46.2% and 16.4% higher than that of the single ARIMA model respectively. In addition, the x-axis angular velocity of gyro B on the satellite is predicted in short and medium term. In the short term prediction, the RMSE of the combined model is increased by 71.2% compared with that of the single ARIMA model. In the medium prediction, the RMSE of the combination model is 64.2% higher than that of the single ARIMA model. Experimental results show that the ARIMA-SVR combination model provides effective decision analysis support for ensuring the healthy in-orbit operation of satellites.