应用HP滤波的卫星遥测数据预测方法  

Prediction Method of Satellite Telemetry Data Using HP Filter

作　　者：李志强[1] 张香燕[1] 田华东[1] LI Zhiqiang;ZHANG Xiangyan;TIAN Huadong(Beijing Institute of Spacecraft System Engineering,Beijing 100094,China)

机构地区：[1]北京空间飞行器总体设计部,北京100094

出　　处：《航天器工程》2021年第4期23-30,共8页Spacecraft Engineering

摘　　要：卫星遥测数据预测分析在故障预警中发挥着重要作用。文章将用于时间序列分析的HP(Hodrick-Prescott)滤波引入到遥测数据分析领域中,提出了基于HP滤波分解的卫星遥测数据预测方法,并设计了在轨应用的系统方案。利用HP滤波将遥测数据的时间序列分解成趋势成分和波动成分,并根据各项特点分别使用多元自回归和季节型单整自回归移动平均(ARIMA)模型进行预测,然后叠加趋势成分和波动成分各自的预测值,得到最终组合预测结果。该预测方法可以有效降低由趋势性、波动性等因素相互影响产生的误差,提高预测精度,其系统方案可在卫星监视、故障诊断和预警中实际应用。利用预测方法对某卫星行波管阳压遥测数据进行分析,验证了预测方法的正确性和有效性,在半年时间内的预测结果的相对误差小于0.04%。Satellite telemetry data prediction plays an important role in fault early warning.In this paper,the HP(Hodrick-Prescott)filter for time series analysis is introduced into the field of telemetry data.A telemetry parameter prediction method and a system scheme based on HP filter are proposed.The time series of telemetry data is decomposed into trend term and fluctuation term by using HP filter.According to the characteristics of the series,multiple autoregressive model and seasonal ARIMA(autoregressive integrated moving average)model are used for prediction;then,the prediction values of trend term and fluctuation term are superimposed to get the final combination forecast result.This prediction method can effectively reduce the relative error caused by the interaction of trend,fluctuation,seasonality and other factors.This method can improve the prediction accuracy and the system scheme can be applied in satellite monitoring,fault diagnosis and early warning.The accuracy and effectiveness of the prediction method are verified by the analysis of the anode voltage data,and the relative error of the prediction result data over half a year is less than 0.04%.

关 键 词：卫星遥测数据 HP滤波 单整自回归移动平均模型 数据预测 

分 类 号：V474[航空宇航科学与技术—飞行器设计]