基于小波变换和MC-ANNs并行模型的经验加速度预测
|
摘要
为对低轨卫星精密定轨过程产生的经验加速度进行拟合与预报,提出基于小波变换和MC-ANNs (Markov chainartificial neural networks)的并行模型。引入小波变换技术,结合神经网络的深度抽象以及马尔科夫链的动态随机的双重优势,缓解串联式马尔科夫链-神经网络模型随着时间推移导致误差变大的缺点。对某低轨航天器的实测星载的经验加速度预测分析结果表示,相较于BP (back propagation)神经网络模型、串联式马尔科夫链-神经网络模型,该模型的预测精度平均提高29.83%、16.04%,应用于轨道确定与轨道预报中,可提高卫星定位的精度。
To fit and forecast the empirical acceleration generated by the low earth orbit satellite determination,the parallel model based on wavelet transform and MC-ANNs was proposed.With the introduction of the wavelet transform,the depth abstractness of neural network and the dynamic randomness of Markov chain were combined to minimize inaccuracies accumulated over time from the series Markov chain-neural network model.When it came to the use of predicting and analyzing the empirical acceleration of a low orbit spacecraft,the prediction accuracy of this model compared with that of the BP neural network model and the series Markov chain-neural network model can be increased by an average of 29.83% and 16.04% respectively.The accuracy of satellite positioning can be improved by applying this model in orbit determination and orbit prediction.
To fit and forecast the empirical acceleration generated by the low earth orbit satellite determination,the parallel model based on wavelet transform and MC-ANNs was proposed.With the introduction of the wavelet transform,the depth abstractness of neural network and the dynamic randomness of Markov chain were combined to minimize inaccuracies accumulated over time from the series Markov chain-neural network model.When it came to the use of predicting and analyzing the empirical acceleration of a low orbit spacecraft,the prediction accuracy of this model compared with that of the BP neural network model and the series Markov chain-neural network model can be increased by an average of 29.83% and 16.04% respectively.The accuracy of satellite positioning can be improved by applying this model in orbit determination and orbit prediction.
引文
[1]Wang Qianxin,Hu Chao,Xu Tianhe,et al.Impacts of earth rotation parameters on GNSS ultra-rapid orbit prediction:Derivation and real-time correction[J].Advances in Space Research,2017,60(12):2855-2870.
[2]HE Lina.Perturbation forces analysis for spacecraft of different orbit altitudes[J].Journal of Geodesy and Geodynamics,2017,37(11):1156-1160(in Chinese).[何丽娜.不同摄动力对低中高轨航天器轨道的影响分析[J].大地测量与地球动力学,2017,37(11):1156-1160.]
[3]Jin SG,Jin R,Li D.Assessment of BeiDou differential code bias variations from multi-GNSS network observations[J].Annales Geophysicae,2016,34(2):259-269.
[4]CAO Lei.Satellite orbit prediction method based on compensation model by using deep neural network[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2014(in Chinese).[曹磊.基于深度神经网络补偿模型的轨道预报技术[D].南京:南京航空航天大学,2014.]
[5]Wang WB,Gao Y.Effective empirical acceleration modeling and its application to enhanced-accuracy orbit prediction[J].Transactions of the Japan Society for Aeronautical&Space Sciences Aerospace Technology Japan,2016,14(ists30):Pd_39-Pd_45.
[6]YANG Feng,XUE Bin,LIU Jian.Rain attenuation prediction at W band based on non-stationary time-series ARIMA model[J].Journal of Electronics&Information Technology,2015,37(10):2475-2482(in Chinese).[杨峰,薛斌,刘剑.基于非平稳时序ARIMA模型的W频段雨衰预测[J].电子与信息学报,2015,37(10):2475-2482.]
[7]HAN Min,XU Meiling,MU Dayun.Relevance vector machine with reservoir for time series prediction[J].Chinese Journal of Computers,2014,37(12):2427-2432(in Chinese).[韩敏,许美玲,穆大芸.无核相关向量机在时间序列预测中的应用[J].计算机学报,2014,37(12):2427-2432.]
[8]GOU Chengcheng,QIN Yujun,TIAN Tian,et al.Social message outbreak prediction model based on recurrent neural network[J].Journal of Software,2017,28(11):3030-3042(in Chinese).[笱程成,秦宇君,田甜,等.一种基于RNN的社交消息爆发预测模型[J].软件学报,2017,28(11):3030-3042.]
[9]Hou N,Dong H,Wang Z,et al.Non-fragile state estimation for discrete Markovian jumping neural networks[J].Neurocomputing,2016,179(C):238-245.
[10]Chen GY,Gan M,Chen GL.Generalized exponential autoregressive models for nonlinear time series:Stationarity,estimation and applications[J].Information Sciences,2018,438:46-57.
[11]ZHOU Guangfu,WEN Chenglin,GAO Jingli.Light field reconstruction based on wavelet transform and sparse fourier transform[J].Acta Electronica Sinica,2017,45(4):782-790(in Chinese).[周广福,文成林,高敬礼.基于小波变换与稀疏傅里叶变换相结合的光场重构方法[J].电子学报,2017,45(4):782-790.]
[12]DONG Rongsheng,MA Zhengxian,GUO Yunchuan,et al.A Markov game theory-based energy balance routing algorithm[J].Chinese Journal of Computers,2013,36(7):1500-1508(in Chinese).[董荣胜,马争先,郭云川,等.一种基于马尔可夫博弈的能量均衡路由算法[J].计算机学报,2013,36(7):1500-1508.]
[13]JIAO Licheng,YANG Shuyuan,LIU Fang,et al.Seventy years beyond neural networks:Retrospect and prospect[J].Chinese Journal of Computers,2016,39(8):1697-1716(in Chinese).[焦李成,杨淑媛,刘芳,等.神经网络七十年:回顾与展望[J].计算机学报,2016,39(8):1697-1716.]
[14]WANG Wenbin,LIU Rongfang.A new method of orbit prediction for LEO satellites using empirical accelerations[J].Chinese Journal of Space Science,2015,35(6):715-720(in Chinese).[王文彬,刘荣芳.基于经验加速度的低轨卫星轨道预报新方法[J].空间科学学报,2015,35(6):715-720.]
[15]Yang X,Li J,Zhang S.Ionospheric correction for spaceborne single-frequency GPS based on single layer model[J].Journal of Earth System Science,2014,123(4):767-778.
[2]HE Lina.Perturbation forces analysis for spacecraft of different orbit altitudes[J].Journal of Geodesy and Geodynamics,2017,37(11):1156-1160(in Chinese).[何丽娜.不同摄动力对低中高轨航天器轨道的影响分析[J].大地测量与地球动力学,2017,37(11):1156-1160.]
[3]Jin SG,Jin R,Li D.Assessment of BeiDou differential code bias variations from multi-GNSS network observations[J].Annales Geophysicae,2016,34(2):259-269.
[4]CAO Lei.Satellite orbit prediction method based on compensation model by using deep neural network[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2014(in Chinese).[曹磊.基于深度神经网络补偿模型的轨道预报技术[D].南京:南京航空航天大学,2014.]
[5]Wang WB,Gao Y.Effective empirical acceleration modeling and its application to enhanced-accuracy orbit prediction[J].Transactions of the Japan Society for Aeronautical&Space Sciences Aerospace Technology Japan,2016,14(ists30):Pd_39-Pd_45.
[6]YANG Feng,XUE Bin,LIU Jian.Rain attenuation prediction at W band based on non-stationary time-series ARIMA model[J].Journal of Electronics&Information Technology,2015,37(10):2475-2482(in Chinese).[杨峰,薛斌,刘剑.基于非平稳时序ARIMA模型的W频段雨衰预测[J].电子与信息学报,2015,37(10):2475-2482.]
[7]HAN Min,XU Meiling,MU Dayun.Relevance vector machine with reservoir for time series prediction[J].Chinese Journal of Computers,2014,37(12):2427-2432(in Chinese).[韩敏,许美玲,穆大芸.无核相关向量机在时间序列预测中的应用[J].计算机学报,2014,37(12):2427-2432.]
[8]GOU Chengcheng,QIN Yujun,TIAN Tian,et al.Social message outbreak prediction model based on recurrent neural network[J].Journal of Software,2017,28(11):3030-3042(in Chinese).[笱程成,秦宇君,田甜,等.一种基于RNN的社交消息爆发预测模型[J].软件学报,2017,28(11):3030-3042.]
[9]Hou N,Dong H,Wang Z,et al.Non-fragile state estimation for discrete Markovian jumping neural networks[J].Neurocomputing,2016,179(C):238-245.
[10]Chen GY,Gan M,Chen GL.Generalized exponential autoregressive models for nonlinear time series:Stationarity,estimation and applications[J].Information Sciences,2018,438:46-57.
[11]ZHOU Guangfu,WEN Chenglin,GAO Jingli.Light field reconstruction based on wavelet transform and sparse fourier transform[J].Acta Electronica Sinica,2017,45(4):782-790(in Chinese).[周广福,文成林,高敬礼.基于小波变换与稀疏傅里叶变换相结合的光场重构方法[J].电子学报,2017,45(4):782-790.]
[12]DONG Rongsheng,MA Zhengxian,GUO Yunchuan,et al.A Markov game theory-based energy balance routing algorithm[J].Chinese Journal of Computers,2013,36(7):1500-1508(in Chinese).[董荣胜,马争先,郭云川,等.一种基于马尔可夫博弈的能量均衡路由算法[J].计算机学报,2013,36(7):1500-1508.]
[13]JIAO Licheng,YANG Shuyuan,LIU Fang,et al.Seventy years beyond neural networks:Retrospect and prospect[J].Chinese Journal of Computers,2016,39(8):1697-1716(in Chinese).[焦李成,杨淑媛,刘芳,等.神经网络七十年:回顾与展望[J].计算机学报,2016,39(8):1697-1716.]
[14]WANG Wenbin,LIU Rongfang.A new method of orbit prediction for LEO satellites using empirical accelerations[J].Chinese Journal of Space Science,2015,35(6):715-720(in Chinese).[王文彬,刘荣芳.基于经验加速度的低轨卫星轨道预报新方法[J].空间科学学报,2015,35(6):715-720.]
[15]Yang X,Li J,Zhang S.Ionospheric correction for spaceborne single-frequency GPS based on single layer model[J].Journal of Earth System Science,2014,123(4):767-778.