卫星激光测距系统的异常检测及其稳定性监测
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摘要
针对卫星激光测距系统(SLR)的稳定性问题,该文分析了北京房山kHz地靶观测数据,根据系统延迟的统计信息构建异常辨识统计量,剔除异常数据。地靶有效数据在一个较小的范围内集中,变化幅度0.25ns左右;分析了单次系统延迟变化在0.2ns左右;通过ARMA时间序列分析方法,确定2018年北京房山SLR系统延迟的变化规律,建立SLR系统延迟变化的预报模型;最后,根据预报模型,对系统延迟进行短期预报,以检测SLR系统异常,实现对SLR系统运行稳定性监测的目的。
Aiming at the stability problem of SLR system,the kHz calibration data of Beijing Fangshan was analyzed,according to the statistical information of system delay,the abnormal identification statistics were constructed,and the abnormal data was eliminated.The valid data of the ground target was concentrated in a small range with a variation range of 0.25 ns.The single-system delay variation was about 0.2 ns.Auto-regressive moving average(ARMA)time series analysis method was used to determine the SLR system delay of Beijing Fangshan,and the prediction model of delay variation of SLR system was established.Finally,according to the forecast model,the system delay was short-term forecasted to detect the abnormality of the SLR system to achieve the purpose of monitoring the stability of the SLR system.
Aiming at the stability problem of SLR system,the kHz calibration data of Beijing Fangshan was analyzed,according to the statistical information of system delay,the abnormal identification statistics were constructed,and the abnormal data was eliminated.The valid data of the ground target was concentrated in a small range with a variation range of 0.25 ns.The single-system delay variation was about 0.2 ns.Auto-regressive moving average(ARMA)time series analysis method was used to determine the SLR system delay of Beijing Fangshan,and the prediction model of delay variation of SLR system was established.Finally,according to the forecast model,the system delay was short-term forecasted to detect the abnormality of the SLR system to achieve the purpose of monitoring the stability of the SLR system.
引文
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[12]OMOLE D O,LONGE E O,MUSA A G.An approach to reaeration coefficient modeling in local surface water quality monitoring[J].Environmental Modeling&Assessment,2013,18(1):85-94.
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[2]张忠萍,张海峰,吴志波,等.高精度千赫兹重复频率卫星激光测距系统及实测结果[J].科学通报,2011,56(15):1177-1183.(ZHANG Zhongping,ZHANGHaifeng,WU Zhibo,et al.kHz repetition satellite laser ranging system with high precision and measuring results[J].Chinese Science Bulletin,2011,56(15):1177-1183.)
[3]邓华荣,张海峰,龙明亮,等.4kHz重复频率卫星激光测距试验及应用研究[J].光学学报,2019,39(3):0314002.(DENG Huarong,ZHANG Haifeng,LONGMingliang,et al.Research on 4kHz repetition rate satellite laser ranging system and its application[J].Acta Optica Sinica,2019,39(3):0314002.)
[4]门涛,谌钊,徐蓉,等.空间目标激光测距技术发展现状及趋势[J].激光与红外,2018,48(12):1451-1457.(MEN Tao,SHEN Zhao,XU Rong,et al.Development status and tendency of space target laser ranging technique[J].Laser&Infrared,2018,48(12):1451-1457.)
[5]PROCHAZKA I,KODET J,BLAZEJ J,et al.Identification and calibration of one-way delays in satellite laser ranging systems[J].Advances in Space Research,2017,59(10):2466-2472.
[6]OTSUBO T,MLLER H,PAVLIS E C,et al.Rapid response quality control service for the laser ranging tracking network[J].Journal of Geodesy,2018(5):1-10.
[7]APPLEBY G,RODRGUEZ J,ALTAMIMI Z.Assessment of the accuracy of global geodetic satellite laser ranging observations and estimated impact on ITRFscale:estimation of systematic errors in LAGEOS observations 1993-2014[J].Journal of Geodesy,2016,90(12):1371-1388.
[8]王燕.应用时间序列分析[M].2版.北京:中国人民大学出版社,2008.(WANG Yan.Application of time series analysis[M].2nd ed.Beijing:Peoples University of China Press,2008.)
[9]何书元.应用时间序列分析[M].北京:北京大学出版社,2007.(HE Shuyuan.Application of time series analysis[M].Beijing:Peking University Press,2007.)
[10]徐君毅,曾安敏.ARIMA(0,2,q)模型在卫星钟差预报中的应用[J].大地测量与地球动力学,2009,29(5):116-120.(XU Junyi,ZENG Anmin.Application of ARIMA(0,2,q)model to prediction of satellite clock error[J].Journal of Geodesy and Geodynamics,2009,29(5):116-120.)
[11]席超,蔡成林,李思敏,等.基于ARMA模型的导航卫星钟差长期预报[J].天文学报,2014,55:(1):78-89.(XI Chao,CAI Chenglin,LI Simin,et al.Long-term satellite clock bias prediction based on ARMA model[J].Acta Astronomica Sinica,2014,55(1):78-89.)
[12]OMOLE D O,LONGE E O,MUSA A G.An approach to reaeration coefficient modeling in local surface water quality monitoring[J].Environmental Modeling&Assessment,2013,18(1):85-94.
[13]KRISHNAIAH P R.Applications of statistics[J].Technometrics,1976,20(3):322-323.