基于MEMS阵列的虚拟陀螺的实现
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摘要
MEMS陀螺随半导体技术的发展迅速兴起,但其普遍精度不高。为提高MEMS陀螺的精度,满足实际应用需求。利用4个陀螺组成阵列,标定并补偿了其对准误差和标度因数误差,并采用基于自回归模型的卡尔曼滤波融合算法进行数据融合,从而得到一个精度更高的虚拟陀螺。在静态实验中,陀螺的1σ标准差降低了4.36倍,零偏不稳定性降低了2.22倍;在动态摇摆实验中,其速率误差的1σ标准差降低了3.69倍。实验也表明,经过标定补偿陀螺阵列的输出数据更接近真实值。根据实验结果,可知对四陀螺阵列的标定补偿和数据融合的方法有效抑制了陀螺的噪声。
With the development of semiconductor technology,MEMS gyroscope flourishing quickly,but most of the MEMS gyroscopes don't have a good accuracy. In order to improve the accuracy and meet actual needs,a gyro array which is consisted of 4 MEMS gyroscopes is built. The alignment error and calibration factor of every sensitive axis of the gyroscope array have been calibrated and compensated,and the algorithm of Kalman filter based on AR model is used to fuse the outputs of the array. In the static experiment,the standard deviation of 1σ of virtual gyroscope decreased 4.36 times than individual gyro's; and the bias instability decreased 2.22 times. In the rolling motion experiment,the standard deviation of 1σ of the error of virtual gyroscope decreased 3.69 times than individual gyro's. The experiment also shows that after compensation more accurate data is available,that can prove its effectiveness. According to the results of experiment,the error compensation and the data fusion method used in the four-gyro array is effective to decrease the noise.
With the development of semiconductor technology,MEMS gyroscope flourishing quickly,but most of the MEMS gyroscopes don't have a good accuracy. In order to improve the accuracy and meet actual needs,a gyro array which is consisted of 4 MEMS gyroscopes is built. The alignment error and calibration factor of every sensitive axis of the gyroscope array have been calibrated and compensated,and the algorithm of Kalman filter based on AR model is used to fuse the outputs of the array. In the static experiment,the standard deviation of 1σ of virtual gyroscope decreased 4.36 times than individual gyro's; and the bias instability decreased 2.22 times. In the rolling motion experiment,the standard deviation of 1σ of the error of virtual gyroscope decreased 3.69 times than individual gyro's. The experiment also shows that after compensation more accurate data is available,that can prove its effectiveness. According to the results of experiment,the error compensation and the data fusion method used in the four-gyro array is effective to decrease the noise.
引文
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[13] 张鹏,常洪龙,苑伟政,等. 虚拟陀螺技术研究[J]. 传感技术学报,2016,19(5):2226-2229.
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[15] 刘洁瑜,沈强,李灿,等. 基于优化KF的MEMS陀螺阵列信号融合方法[J]. 系统工程与电子技术,2016,38(12):2705-2710.
[16] 沈强,刘洁瑜,汪立新,等. MEMS陀螺阵列技术研究进展[J]. 导航与控制,2017,16(5):97-103.
[17] 孙田川,刘洁瑜. 基于支持度和自适应加权的MEMS陀螺信息融合算法[J]. 传感技术学报,2016,29(10):1548-1552.
[18] 庞博. 批量MEMS陀螺信息融合技术研究[D]. 哈尔滨:哈尔滨工业大学,2013.
[19] 何昆鹏,程万娟,高延滨,等. 虚拟陀螺技术在MEMS惯性导航系统中的应用[J]. 哈尔滨工程大学学报,2009,30(10):1123-1128.
[20] Ji X S. Research on Signal Processing of MEMS Gyro Array[J]. Mathematical Problem in Engineering,2015(3):1-6.
[21] 王辛望,沈小林,刘新生. 基于自适应Kalman滤波的MEMS陀螺的随机误差分析[J]. 传感技术学报,2017,30(11):1666-1670.
[22] 严恭敏,李四海,秦永元. 惯性仪器测试与数据分析[M]. 北京:国防工业出版社,2012.臧雪岩(1992-),男,河北工业大学电子信息工程学院硕士,主要从事智能控制与智能系统研究,zang_xueyan@163.com;伍萍辉(1970-),女,河北工业大学电子信息工程学院教授,主要从事微机测控技术、智能控制等研究,wuphui@126.com; 曾成(1971-),男,河北工业大学电子信息工程学院副教授,主要从事信息融合、惯性导航定位技术、计算机测控技术及应用等研究。
[2] Bayard D S,Scott R Ploen. High Accuracy Inertial Sensors from Inexpensive Components[P]. United States patent,US20030187623A1,2003.
[3] Lam Q M,Wilson Jr T,Contillo R,et al. Enhancing MEMS Sensors Accuracy via Random Noise Characterization and Calibration[C]//Proceedings of SPIE,Bellingham,WA,2004,540:427-438.
[4] Tanenhaus M,Gcis T,Carhoun D,et al. Accurate Real Time Inertial Navigation Device by Application and Processing of Arrays of MEMS Inertial Sensors[C]//Proceedings of IEEE/ION Position,Location and Navigation Symposium,2010:20-26.
[5] Tancnhaus M,Carhoun D,Gcis T,et al. Miniature IMU/INS with Optimally Fused Low Drift MEMS Gym and Accelerometers for Applications in GPS-Denied Environments[C]//Proceedings of IEEE/ION Position,Location and Navigation Symposium,2012:259-264.
[6] Al-Majed M I,Alsuwaidan B N. A New Testing Platform for Attitude Determination and Control Subsystems:Design and Applications[C]//IEEE/ASME International Conference on Advanced Intelligent,Mechatronics,Singapore,2009:1318-1323.
[7] Heera M M,Divya J K,Vamna M S,et al. Minimum Variance Optimal Filter Design for a 3×3 MEMS Gyroscope Cluster Configuration[J]. IFAC Papers on Line,2016,49(1):639-645.
[8] Vaccaro R J,Zaki A S. Reduced-Drift Virtual Gyro from an Array of Low-Cost Gyros[J]. Sensors,2017,17(2):352.
[9] 胡敏. 基于阵列技术的MEMS虚拟陀螺技术研究[D]. 西安:西北工业大学,2006.
[10] 张鹏. 微机械陀螺的高精度“虚拟”实现方法研究[D]. 西安:西北工业大学,2007.
[11] Chang H L,Xue L,Qin W,et al. An Integrated MEMS Gyroscope Array with Higher Accuracy Output[J]. Sensors,2008,8(4):2886-2899.
[12] Xue L,Jiang C Y,Chang H L,et al. A Novel Kalman Filter for Combining Outputs of MEMS Gyroscope Array[J]. Measurement,2012,45(4):745-754.
[13] 张鹏,常洪龙,苑伟政,等. 虚拟陀螺技术研究[J]. 传感技术学报,2016,19(5):2226-2229.
[14] Liu J Y,Shen Q,Qin W W. Signal Processing Technique for Combining Numerous MEMS Gyroscopes Based on Dynamic Conditional Correlation[J]. Micromachines,2015,6(6):684-698.
[15] 刘洁瑜,沈强,李灿,等. 基于优化KF的MEMS陀螺阵列信号融合方法[J]. 系统工程与电子技术,2016,38(12):2705-2710.
[16] 沈强,刘洁瑜,汪立新,等. MEMS陀螺阵列技术研究进展[J]. 导航与控制,2017,16(5):97-103.
[17] 孙田川,刘洁瑜. 基于支持度和自适应加权的MEMS陀螺信息融合算法[J]. 传感技术学报,2016,29(10):1548-1552.
[18] 庞博. 批量MEMS陀螺信息融合技术研究[D]. 哈尔滨:哈尔滨工业大学,2013.
[19] 何昆鹏,程万娟,高延滨,等. 虚拟陀螺技术在MEMS惯性导航系统中的应用[J]. 哈尔滨工程大学学报,2009,30(10):1123-1128.
[20] Ji X S. Research on Signal Processing of MEMS Gyro Array[J]. Mathematical Problem in Engineering,2015(3):1-6.
[21] 王辛望,沈小林,刘新生. 基于自适应Kalman滤波的MEMS陀螺的随机误差分析[J]. 传感技术学报,2017,30(11):1666-1670.
[22] 严恭敏,李四海,秦永元. 惯性仪器测试与数据分析[M]. 北京:国防工业出版社,2012.臧雪岩(1992-),男,河北工业大学电子信息工程学院硕士,主要从事智能控制与智能系统研究,zang_xueyan@163.com;伍萍辉(1970-),女,河北工业大学电子信息工程学院教授,主要从事微机测控技术、智能控制等研究,wuphui@126.com; 曾成(1971-),男,河北工业大学电子信息工程学院副教授,主要从事信息融合、惯性导航定位技术、计算机测控技术及应用等研究。
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