基于Allan方差和SVR的MEMS陀螺仪随机误差分析与预测
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摘要
为了使低成本MEMS陀螺仪数据的精度更高,本文提出了一种混合核函数支持向量回归(SVR)的MEMS陀螺仪随机误差预测模型,并通过粒子群优化(PSO)算法对模型参数和核函数参数进行优化;同时通过Allan方差法对SVR预测前后的MEMS陀螺仪随机误差数据进行分析。试验结果表明:混合核函数SVR对MEMS陀螺仪随机误差的预测准确度可达99.99%;当MEMS陀螺仪所处状态不同,但噪声特性相同时,可采用统一的SVR预测模型预测随机误差,研究结果为进一步用于MEMS陀螺仪的实时误差补偿中提供依据。
In order to make the precision of low-cost MEMS gyroscope data higher,this paper proposes a hybrid kernel function support vector regression( SVR) MEMS gyroscope random error prediction model,and through the particle swarm optimization( PSO)algorithm for model parameters and kernel. The function parameters are optimized. At the same time,the MEMS gyro random error data before and after prediction is analyzed by the Allan variance method. The experimental results show that the hybrid kernel function SVR can predict the random error of MEMS gyroscope up to 99. 99%. When the MEMS gyroscope is in different state but the noise characteristics are the same,a unified SVR prediction model can be used to predict the random error. The results provide a basis for further real-time error compensation for MEMS gyroscopes.
In order to make the precision of low-cost MEMS gyroscope data higher,this paper proposes a hybrid kernel function support vector regression( SVR) MEMS gyroscope random error prediction model,and through the particle swarm optimization( PSO)algorithm for model parameters and kernel. The function parameters are optimized. At the same time,the MEMS gyro random error data before and after prediction is analyzed by the Allan variance method. The experimental results show that the hybrid kernel function SVR can predict the random error of MEMS gyroscope up to 99. 99%. When the MEMS gyroscope is in different state but the noise characteristics are the same,a unified SVR prediction model can be used to predict the random error. The results provide a basis for further real-time error compensation for MEMS gyroscopes.
引文
[1]杜少林,陈书钊,陈鹏光,等.基于Allan方差的MEMS陀螺仪噪声分析[J].仪表技术与传感器,2018(5):20-22,27.
[2]陈永冰,查峰,刘勇.光纤陀螺随机误差的重叠分段Allan分析方法[J].中国惯性技术学报,2016,24(2):235-241.
[3] SONG Rui,CHEN Xiyuan,HUANG Haoqiian. Nonstationary dynamic stochastic error analysis of fiber optic gyroscope based on optimized Allan variance[J].Sensors and Actuators A(Physical),2018,276:26-33.
[4]刘乾坤,隋立芬,陈泉余,等.基于Allan方差的BDS随机误差分析[J].测绘通报,2016(5):18-21,65.
[5]张谦,王玮,王蕾,等.基于动态Allan方差的光纤陀螺随机误差分析及算法改进[J].光学学报,2015,35(4):65-71.
[6]陈帅,赵开斌,赵琳.基于动态Allan方差的MEMS惯性测量单元的性能评价[J].应用科技,2013,40(5):71-74,85.
[7] TAN X,WANG J,MENG S J X. GA-SVR and pseudoposition-aided GPS/INS integration during GPS outage[J]. Journal of Navigation,2015,68(4):678-696.
[8]成雨,叶东,孙兆伟.MEMS陀螺随机误差趋势项的支持向量回归机预测补偿算法[J].中国惯性技术学报,2016,24(5):600-606.
[9] ZHU Bui,WEI Y. Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology[J]. Omega-international Journal of Management Science,2017,41(3):517-524.
[10] WEI Guo,YU Xudong,LONG Xingwu. Novel approach for identifying Z-axis drift of RLG based on GA-SVR model[J]. Journal of Systems Engineering and Electronics,2014,25(1):115-121.
[11]李泽民,段凤阳,马佳智.基于支持向量机的MEMS陀螺仪随机漂移补偿[J].传感技术学报,2012,25(8):1084-1087.
[12]厉宽宽,陈允芳,程敏,王云鹏.MEMS-IMU随机误差的Allan方差分析[J].全球定位系统,2016,41(6):102-106.
[13] LENG X,WANG J,JI H,et al. Prediction of sizefractionated airborne particle-bound metals using MLR,BP-ANN and SVM analyses[J]. Chemosphere,2017,180:513-522.
[14]单黎黎,张宏军,王杰,等.一种改进粒子群算法的混合核ε-SVM参数优化及应用[J].计算机应用研究,2013,30(6):1636-1639.
[15]吴军伟,缪玲娟,李福胜,等.改进支持向量机的光纤陀螺温度漂移补偿方法[J].红外与激光工程,2018,47(5):142-147.
[2]陈永冰,查峰,刘勇.光纤陀螺随机误差的重叠分段Allan分析方法[J].中国惯性技术学报,2016,24(2):235-241.
[3] SONG Rui,CHEN Xiyuan,HUANG Haoqiian. Nonstationary dynamic stochastic error analysis of fiber optic gyroscope based on optimized Allan variance[J].Sensors and Actuators A(Physical),2018,276:26-33.
[4]刘乾坤,隋立芬,陈泉余,等.基于Allan方差的BDS随机误差分析[J].测绘通报,2016(5):18-21,65.
[5]张谦,王玮,王蕾,等.基于动态Allan方差的光纤陀螺随机误差分析及算法改进[J].光学学报,2015,35(4):65-71.
[6]陈帅,赵开斌,赵琳.基于动态Allan方差的MEMS惯性测量单元的性能评价[J].应用科技,2013,40(5):71-74,85.
[7] TAN X,WANG J,MENG S J X. GA-SVR and pseudoposition-aided GPS/INS integration during GPS outage[J]. Journal of Navigation,2015,68(4):678-696.
[8]成雨,叶东,孙兆伟.MEMS陀螺随机误差趋势项的支持向量回归机预测补偿算法[J].中国惯性技术学报,2016,24(5):600-606.
[9] ZHU Bui,WEI Y. Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology[J]. Omega-international Journal of Management Science,2017,41(3):517-524.
[10] WEI Guo,YU Xudong,LONG Xingwu. Novel approach for identifying Z-axis drift of RLG based on GA-SVR model[J]. Journal of Systems Engineering and Electronics,2014,25(1):115-121.
[11]李泽民,段凤阳,马佳智.基于支持向量机的MEMS陀螺仪随机漂移补偿[J].传感技术学报,2012,25(8):1084-1087.
[12]厉宽宽,陈允芳,程敏,王云鹏.MEMS-IMU随机误差的Allan方差分析[J].全球定位系统,2016,41(6):102-106.
[13] LENG X,WANG J,JI H,et al. Prediction of sizefractionated airborne particle-bound metals using MLR,BP-ANN and SVM analyses[J]. Chemosphere,2017,180:513-522.
[14]单黎黎,张宏军,王杰,等.一种改进粒子群算法的混合核ε-SVM参数优化及应用[J].计算机应用研究,2013,30(6):1636-1639.
[15]吴军伟,缪玲娟,李福胜,等.改进支持向量机的光纤陀螺温度漂移补偿方法[J].红外与激光工程,2018,47(5):142-147.