光纤陀螺随机误差的集成建模及滤波处理
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摘要
为了对光纤陀螺仪随机误差进行分析处理,提高其使用精度,提出了一种经验模态分解与时间序列模型相结合的误差分析建模方法。以经验模态分解得到的本征模态函数为基础,分层进行ARMA建模;在模型基础上逐层进行Kalman滤波,实现对于随机漂移信号的滤除;最后通过信号重构,完成了从全频率角度对光纤陀螺仪随机误差进行分析建模的构想。与其他建模方法相比,该方法对于原始数据的模拟匹配程度更高,试验结果进一步表明,本文方法有效去除了光纤陀螺仪的随机漂移,提高了光纤陀螺仪的使用精度。
In order to analyze and process the random error of the fiber optic gyroscope(FOG) and improve its use precision, an error modeling method that combined empirical mode decomposition(EMD) and time series model was proposed. On the basis of the intrinsic mode functions(Imf) which was obtained by empirical mode decomposition, auto-regressive and moving average model(ARMA) modeling is performed hierarchically for each Imf. Then, Kalman filtering is performed layer by layer on the basis of the model to remove the random drift signals from the real angular velocity information. At the end of the algorithm, the signal which had been filtered need to be reorganized, and through the above steps, the conception of analyzing and modeling in connection with the random error of FOG from full frequency's point of view was realized. Compared with other modeling methods, this method has a higher degree of simulation matching to the original data, at the same time, the experimental results have further shown that this method can effectively remove the signal of random drift from the fiber optic gyroscope's output signal and improve its use precision significantly.
In order to analyze and process the random error of the fiber optic gyroscope(FOG) and improve its use precision, an error modeling method that combined empirical mode decomposition(EMD) and time series model was proposed. On the basis of the intrinsic mode functions(Imf) which was obtained by empirical mode decomposition, auto-regressive and moving average model(ARMA) modeling is performed hierarchically for each Imf. Then, Kalman filtering is performed layer by layer on the basis of the model to remove the random drift signals from the real angular velocity information. At the end of the algorithm, the signal which had been filtered need to be reorganized, and through the above steps, the conception of analyzing and modeling in connection with the random error of FOG from full frequency's point of view was realized. Compared with other modeling methods, this method has a higher degree of simulation matching to the original data, at the same time, the experimental results have further shown that this method can effectively remove the signal of random drift from the fiber optic gyroscope's output signal and improve its use precision significantly.
引文
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[14]Bai J Q,Zhang K,Wei Y X.Modeling and analysis of fiber optic gyroscope random drifts[J].Journal of Chinese Inertial Technology,2012,20(5):621-624.白俊卿,张科,卫育新.光纤陀螺随机漂移建模与分析[J].中国惯性技术学报,2012,20(5):621-624.
[15]IEEE.IEEE standard specification format guide and test procedure for single-axis laser gyros:STD 647-2006[S].New York:IEEE,2006.
[16]Morrow R B,Heckman D W.High precision IFOG insertion into the strategic submarine navigation system[C]//Proceedings of1998 Position Location and Navigation Symposium.Palm Springs,CA,USA,1998:332-338.
[2]Chen X,Yang J H,Zhou Y L,et al.The application of low-noise DC-DC power source in fiber-optic gyroscope system[J].Opto-Electronic Engineering,2018,45(1):170517.陈贤,杨建华,周一览,等.一种低噪声开关电源在光纤陀螺系统中的应用[J].光电工程,2018,45(1):170517.
[3]Narasimhappa M,Nayak J,Terra M H,et al.ARMA model based adaptive unscented fading kalman filter for reducing drift of fiber optic gyroscope[J].Sensors and Actuators A:Physical,2016,251:42-51.
[4]Teng F,Jin J,Huang Y L,et al.Noise analysis and measurement of high sensitivity photonic crystal fiber-optic gyroscope[J].Optical Fiber Technology,2015,25:1-6.
[5]Yang J H,Li H Z.Analysis and research of fiber optic gyro drift error based on data mining[J].Laser Journal,2016,37(6):123-126.杨景花,李华中.数据挖掘的光纤陀螺漂移值误差分析与研究[J].激光杂志,2016,37(6):123-126.
[6]Zhao Y X,Li X Y,Liu C X,et al.Comparative research in signal processing of FOG[J].Journal of Chinese Inertial Technology,2003,11(2):52-56.赵玉新,李绪友,刘承香,等.光纤陀螺信号处理方法的比较研究[J].中国惯性技术学报,2003,11(2):52-56.
[7]Ma J M,Zhu J H,Xie N,et al.Adaptive filtering for fiber optic gyroscope based on wavelet-ARMA model[J].Optical Technique,2016,42(5):469-473.马晋美,朱家海,谢聂,等.基于小波-ARMA模型的光纤陀螺自适应滤波[J].光学技术,2016,42(5):469-473.
[8]Cui B B,Chen X Y,Song R.Application of EMD threshold filtering for fiber optical gyro drift signal de-noising[J].Acta Optica Sinica,2015,35(2):0207001.崔冰波,陈熙源,宋锐.EMD阈值滤波在光纤陀螺漂移信号去噪中的应用[J].光学学报,2015,35(2):0207001.
[9]Tian Y P,Yang X J,Guo Y Z,et al.Filtering analysis on the random noise of fiber optic guroscope[J].Acta Optica Sinica,2015,35(9):906006.田云鹏,杨小军,郭云曾,等.光纤陀螺随机噪声滤波分析[J].光学学报,2015,35(9):906006.
[10]Chai J X,Wang X L,Wang D,et al.A high precision modeling method for random vibration error of fiber optic gyroscope[J].Aero Weaponry,2017(4):49-54.柴嘉薪,王新龙,王盾,等.一种光纤陀螺随机振动误差高精度建模方法[J].航空兵器,2017(4):49-54.
[11]Wu Z H,Huang N E.A study of the characteristics of white noise using the empirical mode decomposition method[J].Proceedings:Mathematical,Physical and Engineering Sciences,2004,460(2046):1597-1611.
[12]Huang N E,Wu M L,Qu W D,et al.Applications of Hilbert-Huang transform to non-stationary financial Time series analysis[J].Applied Stochastic Models in Business And Industry,2003,19(3):245-268.
[13]Yang Y H,Meng C X.Research on the characteristic of bias error and noise analysis method of fiber optic gyroscope[J].Acta Optica Sinica,2014,34(12):1206006.杨远洪,孟晨雪.光纤陀螺零偏误差特性及噪声分析方法研究[J].光学学报,2014,34(12):1206006.
[14]Bai J Q,Zhang K,Wei Y X.Modeling and analysis of fiber optic gyroscope random drifts[J].Journal of Chinese Inertial Technology,2012,20(5):621-624.白俊卿,张科,卫育新.光纤陀螺随机漂移建模与分析[J].中国惯性技术学报,2012,20(5):621-624.
[15]IEEE.IEEE standard specification format guide and test procedure for single-axis laser gyros:STD 647-2006[S].New York:IEEE,2006.
[16]Morrow R B,Heckman D W.High precision IFOG insertion into the strategic submarine navigation system[C]//Proceedings of1998 Position Location and Navigation Symposium.Palm Springs,CA,USA,1998:332-338.