基于MEMS器件的捷联姿态测量系统技术研究
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摘要
本文以微机械(MEMS)惯性器件为测量元件，开展微小型捷联式惯性姿态测量系统的关键技术研究。
MEMS惯性器件在体积和成本上的优点较为突出，但在分辨率和精度上存在着很大的不足。与MEMS加速度计相比，陀螺仪的发展较为滞后，目前无法满足惯性姿态测量系统的精度要求。论文从实际需要出发，以MEMS惯性器件、磁强计和温度传感器为测量元件，配合必要的信号采集电路和微处理器完成低成本惯性姿态测量系统设计。
针对传感器分辨率低、输出信号误差大的特点，为保证系统的输出精度，研究了采用系统整体标定方案进行传感器标定的实现方法，讨论了标定误差的变化规律，提出了传感器确定性误差的有效补偿方法。
MEMS陀螺仪输出信号随机噪声强度大，频带宽，难以消除。论文在分析了小波分析方法原理的基础上，采用小波降噪算法对陀螺仪输出信号进行降噪处理，并通过试验结果对比确定了合适的小波函数和相应算法参数。
论文采用加速度计和磁强计的量测值完成系统的初始对准，通过Kalman滤波器完成陀螺仪、加速度计和磁强计信号的数据融合，实现载体姿态的最优估计。仿真试验表明，捷联姿态系统动态性能较好，能够满足设计指标要求。
This dissertation takes Micro Electro Mechanical Systems (MEMS) based inertial sensors as measure element to study key technologies of the micro strapdown inertial attitude measurement system.
MEMS based inertial sensors have superiorities in some aspects, such as small size and low cost. However, the precision of these sensors is much lower than other inertial sensors. Compared with MEMS accelerometers, the development of MEMS gyros is so slow that the precision couldn't meet the needs of inertial attitude measurement system so far. From the point of practical implementation view, a low-cost inertial attitude measurement system has been designed, which are composed of MEMS based inertial sensors, magnetometers, temperature sensors and aided by necessary signal sampling unit and micro processor.
For the low resolution of sensors and large errors existed in the output signal, a sensor calibration method based on systemic calibration has been studied to ensure a satisfactory system output precision. After a discussion on the calibration varying rule, an effective means to compensate the constant part of the sensor error has been carried out.
Due to the large power and wide frequency band, the stochastic noise in the output signal of MEMS gyros is difficult to eliminate. Therefore, on the basis of the principle study of Wavelet Analysis Methods, a wavelet de-noise technique is implied to the gyro output signal and examined by the practical experiments in order to specify appropriate wavelet function and according parameters.
The paper makes use of the measurements of the accelerators and magnetometers to complete the system initial alignment, and use Kalman filter for data integration of the gyros, accelerometers and magnetometers to get an optimal estimation of vehicle attitude. The simulation shows that the designed strapdown inertial attitude measurement system has a good dynamic performance and could meet the design requirement.
MEMS惯性器件在体积和成本上的优点较为突出，但在分辨率和精度上存在着很大的不足。与MEMS加速度计相比，陀螺仪的发展较为滞后，目前无法满足惯性姿态测量系统的精度要求。论文从实际需要出发，以MEMS惯性器件、磁强计和温度传感器为测量元件，配合必要的信号采集电路和微处理器完成低成本惯性姿态测量系统设计。
针对传感器分辨率低、输出信号误差大的特点，为保证系统的输出精度，研究了采用系统整体标定方案进行传感器标定的实现方法，讨论了标定误差的变化规律，提出了传感器确定性误差的有效补偿方法。
MEMS陀螺仪输出信号随机噪声强度大，频带宽，难以消除。论文在分析了小波分析方法原理的基础上，采用小波降噪算法对陀螺仪输出信号进行降噪处理，并通过试验结果对比确定了合适的小波函数和相应算法参数。
论文采用加速度计和磁强计的量测值完成系统的初始对准，通过Kalman滤波器完成陀螺仪、加速度计和磁强计信号的数据融合，实现载体姿态的最优估计。仿真试验表明，捷联姿态系统动态性能较好，能够满足设计指标要求。
This dissertation takes Micro Electro Mechanical Systems (MEMS) based inertial sensors as measure element to study key technologies of the micro strapdown inertial attitude measurement system.
MEMS based inertial sensors have superiorities in some aspects, such as small size and low cost. However, the precision of these sensors is much lower than other inertial sensors. Compared with MEMS accelerometers, the development of MEMS gyros is so slow that the precision couldn't meet the needs of inertial attitude measurement system so far. From the point of practical implementation view, a low-cost inertial attitude measurement system has been designed, which are composed of MEMS based inertial sensors, magnetometers, temperature sensors and aided by necessary signal sampling unit and micro processor.
For the low resolution of sensors and large errors existed in the output signal, a sensor calibration method based on systemic calibration has been studied to ensure a satisfactory system output precision. After a discussion on the calibration varying rule, an effective means to compensate the constant part of the sensor error has been carried out.
Due to the large power and wide frequency band, the stochastic noise in the output signal of MEMS gyros is difficult to eliminate. Therefore, on the basis of the principle study of Wavelet Analysis Methods, a wavelet de-noise technique is implied to the gyro output signal and examined by the practical experiments in order to specify appropriate wavelet function and according parameters.
The paper makes use of the measurements of the accelerators and magnetometers to complete the system initial alignment, and use Kalman filter for data integration of the gyros, accelerometers and magnetometers to get an optimal estimation of vehicle attitude. The simulation shows that the designed strapdown inertial attitude measurement system has a good dynamic performance and could meet the design requirement.
引文
[1] 陈哲著．捷联惯导系统原理．北京：宇航出版社，1986
[2] 黄德鸣，程禄编著．惯性导航系统．哈尔滨：哈尔滨工程大学讲义，2003
[3] Navid Yazdi, Farrokh Ayazi. Micromachined Inertial Sensors.Proceedings of the IEEE, August 1998, Volume86, Issue8,1640-1659P
[4] 李荣冰，刘建业等．基于MEMS技术的微型惯性导航系统的发展现状．中国惯性技术学报．2004，12(6)，88-94页
[5] 刘希珠著．陀螺力学基础．北京：清华大学出版社，1987
[6] Walid Abdel-Hamid. Accuracy enhancement of intergrated MEMS-IMU/GPS systems for land vehicular navigation applications. The University of Calgary's doctor degree paper.2005
[7] 黄德鸣，张树侠，孙枫编著．平台罗经．北京：国防工业出版社，1990
[8] 吴俊伟编．惯性技术基础．哈尔滨：哈尔滨工程大学出版社，2002
[9] D.H.Titterton, J.L.Weston. Strapdown Inertial Navigation Technology.Second Edition. Lexington:MIT Lincoln Laboratory, 2004
[10] 李滋刚，万德钧等编著．捷联式惯性导航技术．北京：中国船舶信息中心，2001
[11] Sungsu Park, Roberto Horowitz. New Adaptive Mode of Operation for MEMS Gyroscopes. Journal of Dynamic Systems, Measurement, and Control.December 2004, Volume 126, Issue4,800-810P
[12] Mei Wang, Yunchun Yang, Ronald R.Hatch. Adaptive Filter for a Miniature MEMS Based Attitude and Heading Reference System. Rec IEEE PLANS Position Locat Navig Symp, April 2004,193-200P
[13] Naser El-Sheimy, Sameh Nassar. Wavelet De-Noising for IMU Alignment. IEEE Aerospace and Electronic Systems Magazine, October 2004, Volume19, Issue10, 32-39P
[14] 牛小骥．微机械姿态测量单元及其用于卫星电视天线稳定的研究．清华 大学博士学位论文．2002
[15]黄苹．捷联惯导系统标定技术研究．哈尔滨工程大学硕士学位论文．2005
[16]傅建国，王孝通等．MEMS陀螺随机误差模型研究．传感器技术．2005，24(3)，75-77页
[17]Robin B.Miller. A New Strapdown Attitude Algorithm. Journal of Guidance, Control, and Dynamics, July 1983, Volume6, Issue4, 287-291P
[18]Wang Changhong,Huang Xu.Application of wavelet packet analysis in the de-noising of MEMS vibrationg gyro. Proceedings of the IEEE, April 2004
[19]Li Qiang, Teng Jianfu. Research of Gyro Signal De-Noising With Stationary Wavelets Transform. CCECE 2003-Canadian Conference on Electrical and Computer Engineering., May 2003, Volume3, 1989-1992 P.
[20]孙延奎著．小波分析及其应用．北京：机械工业出版社，2005
[21]胡昌华，张军波等著．基于MATLAB的系统分析与设计，小波分析．西安：西安电子科技大学出版社，1999
[22]郭庆增．车载动态通讯天线姿态算法的探索．导航与控制．2006，5(2)，10-14页
[23]朱荣，周兆英．基于MEMS的姿态测量系统．测控技术．Vol．21，No．10，2002，21(10)，6-13页
[24]Seong Yun Cho, Ki Won Lee. A personal Navigation System Using Low-Cost MEMS/GPS/Fluxgate. ION 59th Annual Meeting/CIGTF 22nd Guidance Test Symposium, 23-25 June 2003
[25]李锦明著．电容式微机械陀螺仪设计．北京：国防工业出版社，2006
[26]敬喜著．卡尔曼滤波器及其应用基础．北京：国防工业出版社，2003
[27]许文白，魏丽萍等．GPS辅助MEMS惯性系统与常规炮弹的制导化．导航与控制．2005，14(2)，19-74页
[28]丁明理，王祁，宋凯．无陀螺惯性测量组合姿态解算新方法．哈尔滨工业大学学报．2006，38(7)，1025-1027页
[29]牛小骥，高仲毓等．基于微机械惯性传感器的卫星电视天线稳定系统．中国惯性技术学报．2002，10(5)，11-15页
[30]刘忠，两晓庚等．基于四元数的导弹反步控制及全方位算法应用．系统仿真学报．2006，18(10)，2734-2737页
[31]聂水茹．高动态环境下捷联惯导的优化算法研究．西北工业大学硕士学位论文．2003
[32]Eun-Hwan Shin. Estimation techniques for low-cost inertial navigation.The University of Calgary's doctor degree paper.2005
[33]杨艳娟．捷联惯性导航系统关键技术研究．哈尔滨工程大学博士学位论文．2001
[34]吉训生，王寿荣．MEMS陀螺仪随机漂移误差研究．宇航学报．2006，27(4)，640-642页
[35]李文强，马建军，郑志强．低精度MEMS IMU数据预处理方法研究．战术导弹控制技术．No．4，2006
[36]李迪．基于DSP的光纤陀螺捷联惯导数字信号处理系统．哈尔滨工程大学硕士学位论文．2002
[37]张锐，张长虹等．INS＼GPS组合导航中联邦卡尔曼滤波算法的应用．导航与控制．2006，5(2)，18-21页
[38]蒋芹，罗均等著．微型传感器及其应用．北京：化学王业出版社，2005
[39]高仲毓，董景新等．微机电传感器发展及应用的现状与趋势．机械工程学报．2003，39(11)，7-16页
[40]刘杰，朱启兵等．基于新阈值函数的二进小波变换信号去噪研究．东北大学学报(自然科学版)．2006，27(5)，536-539页
[41]李艳，徐长发．一种双正交小波代数构造方法．华中科技大学学报(自然科学版)．2006，34(5)，37-39页
[42]刘建娟，徐晓苏等．遗传优化小波神经网络在组合导航系统中的应用．中国惯性技术学报．2006，14(2)，33-37页
[43]Hailong Zhu, James T.Kwok. Improving De-Noising by Coefficient De-Noising and Dyadic Wavelet Transform. Proceedings of the IEEE, 16th International Conference on Pattern Recognition, February 2002, Volume2 273-276 P
[44]Dwight F．Mix等著．杨志华，杨力华译．小波基础及应用教程．北京：机 械工业出版社，2006
[45]Alben Boggess，Francis J．Narcowich著．芮国胜，康健等译．小波与傅里叶分析基础．北京：电子工业出版社，2004
[46]刘明才著．小波分析及其应用．北京：清华大学出版社，2005
[47]飞思科技产品研发中心编著．小波分析理论与MATLAB 7实现．北京：电子工业出版社，2005
[48]秦永元，张洪钺等编著．卡尔曼滤波与组合导航原理．西安：西北工业大学出版社，1998
[49]Christopher Hide, Terry Moore. Adaptive Kalman Filtering Algorithms for Integrating GPS and Low Cost INS. IEEE Position Location and Navigation Symposium, April 2004, 227-233P
[50]W.abdel-Hamid, T.Abdelazim. Improvement of MEMS-IMU/GPS Performance using fuzzy modeling. GPS Solut. February 2006, Volume10, Issue1, 1-11P
[51]Haiying Hou. Modeling inertial sensors errors using allan variance. The University of Calgary's master degree paper.2004
[52]高为广，杨元喜．IMU/GPS组合导航系统自适应Kalman滤波算法．武汉大学学报(信息科学版)．2004，31(5)，466-469页
[2] 黄德鸣，程禄编著．惯性导航系统．哈尔滨：哈尔滨工程大学讲义，2003
[3] Navid Yazdi, Farrokh Ayazi. Micromachined Inertial Sensors.Proceedings of the IEEE, August 1998, Volume86, Issue8,1640-1659P
[4] 李荣冰，刘建业等．基于MEMS技术的微型惯性导航系统的发展现状．中国惯性技术学报．2004，12(6)，88-94页
[5] 刘希珠著．陀螺力学基础．北京：清华大学出版社，1987
[6] Walid Abdel-Hamid. Accuracy enhancement of intergrated MEMS-IMU/GPS systems for land vehicular navigation applications. The University of Calgary's doctor degree paper.2005
[7] 黄德鸣，张树侠，孙枫编著．平台罗经．北京：国防工业出版社，1990
[8] 吴俊伟编．惯性技术基础．哈尔滨：哈尔滨工程大学出版社，2002
[9] D.H.Titterton, J.L.Weston. Strapdown Inertial Navigation Technology.Second Edition. Lexington:MIT Lincoln Laboratory, 2004
[10] 李滋刚，万德钧等编著．捷联式惯性导航技术．北京：中国船舶信息中心，2001
[11] Sungsu Park, Roberto Horowitz. New Adaptive Mode of Operation for MEMS Gyroscopes. Journal of Dynamic Systems, Measurement, and Control.December 2004, Volume 126, Issue4,800-810P
[12] Mei Wang, Yunchun Yang, Ronald R.Hatch. Adaptive Filter for a Miniature MEMS Based Attitude and Heading Reference System. Rec IEEE PLANS Position Locat Navig Symp, April 2004,193-200P
[13] Naser El-Sheimy, Sameh Nassar. Wavelet De-Noising for IMU Alignment. IEEE Aerospace and Electronic Systems Magazine, October 2004, Volume19, Issue10, 32-39P
[14] 牛小骥．微机械姿态测量单元及其用于卫星电视天线稳定的研究．清华 大学博士学位论文．2002
[15]黄苹．捷联惯导系统标定技术研究．哈尔滨工程大学硕士学位论文．2005
[16]傅建国，王孝通等．MEMS陀螺随机误差模型研究．传感器技术．2005，24(3)，75-77页
[17]Robin B.Miller. A New Strapdown Attitude Algorithm. Journal of Guidance, Control, and Dynamics, July 1983, Volume6, Issue4, 287-291P
[18]Wang Changhong,Huang Xu.Application of wavelet packet analysis in the de-noising of MEMS vibrationg gyro. Proceedings of the IEEE, April 2004
[19]Li Qiang, Teng Jianfu. Research of Gyro Signal De-Noising With Stationary Wavelets Transform. CCECE 2003-Canadian Conference on Electrical and Computer Engineering., May 2003, Volume3, 1989-1992 P.
[20]孙延奎著．小波分析及其应用．北京：机械工业出版社，2005
[21]胡昌华，张军波等著．基于MATLAB的系统分析与设计，小波分析．西安：西安电子科技大学出版社，1999
[22]郭庆增．车载动态通讯天线姿态算法的探索．导航与控制．2006，5(2)，10-14页
[23]朱荣，周兆英．基于MEMS的姿态测量系统．测控技术．Vol．21，No．10，2002，21(10)，6-13页
[24]Seong Yun Cho, Ki Won Lee. A personal Navigation System Using Low-Cost MEMS/GPS/Fluxgate. ION 59th Annual Meeting/CIGTF 22nd Guidance Test Symposium, 23-25 June 2003
[25]李锦明著．电容式微机械陀螺仪设计．北京：国防工业出版社，2006
[26]敬喜著．卡尔曼滤波器及其应用基础．北京：国防工业出版社，2003
[27]许文白，魏丽萍等．GPS辅助MEMS惯性系统与常规炮弹的制导化．导航与控制．2005，14(2)，19-74页
[28]丁明理，王祁，宋凯．无陀螺惯性测量组合姿态解算新方法．哈尔滨工业大学学报．2006，38(7)，1025-1027页
[29]牛小骥，高仲毓等．基于微机械惯性传感器的卫星电视天线稳定系统．中国惯性技术学报．2002，10(5)，11-15页
[30]刘忠，两晓庚等．基于四元数的导弹反步控制及全方位算法应用．系统仿真学报．2006，18(10)，2734-2737页
[31]聂水茹．高动态环境下捷联惯导的优化算法研究．西北工业大学硕士学位论文．2003
[32]Eun-Hwan Shin. Estimation techniques for low-cost inertial navigation.The University of Calgary's doctor degree paper.2005
[33]杨艳娟．捷联惯性导航系统关键技术研究．哈尔滨工程大学博士学位论文．2001
[34]吉训生，王寿荣．MEMS陀螺仪随机漂移误差研究．宇航学报．2006，27(4)，640-642页
[35]李文强，马建军，郑志强．低精度MEMS IMU数据预处理方法研究．战术导弹控制技术．No．4，2006
[36]李迪．基于DSP的光纤陀螺捷联惯导数字信号处理系统．哈尔滨工程大学硕士学位论文．2002
[37]张锐，张长虹等．INS＼GPS组合导航中联邦卡尔曼滤波算法的应用．导航与控制．2006，5(2)，18-21页
[38]蒋芹，罗均等著．微型传感器及其应用．北京：化学王业出版社，2005
[39]高仲毓，董景新等．微机电传感器发展及应用的现状与趋势．机械工程学报．2003，39(11)，7-16页
[40]刘杰，朱启兵等．基于新阈值函数的二进小波变换信号去噪研究．东北大学学报(自然科学版)．2006，27(5)，536-539页
[41]李艳，徐长发．一种双正交小波代数构造方法．华中科技大学学报(自然科学版)．2006，34(5)，37-39页
[42]刘建娟，徐晓苏等．遗传优化小波神经网络在组合导航系统中的应用．中国惯性技术学报．2006，14(2)，33-37页
[43]Hailong Zhu, James T.Kwok. Improving De-Noising by Coefficient De-Noising and Dyadic Wavelet Transform. Proceedings of the IEEE, 16th International Conference on Pattern Recognition, February 2002, Volume2 273-276 P
[44]Dwight F．Mix等著．杨志华，杨力华译．小波基础及应用教程．北京：机 械工业出版社，2006
[45]Alben Boggess，Francis J．Narcowich著．芮国胜，康健等译．小波与傅里叶分析基础．北京：电子工业出版社，2004
[46]刘明才著．小波分析及其应用．北京：清华大学出版社，2005
[47]飞思科技产品研发中心编著．小波分析理论与MATLAB 7实现．北京：电子工业出版社，2005
[48]秦永元，张洪钺等编著．卡尔曼滤波与组合导航原理．西安：西北工业大学出版社，1998
[49]Christopher Hide, Terry Moore. Adaptive Kalman Filtering Algorithms for Integrating GPS and Low Cost INS. IEEE Position Location and Navigation Symposium, April 2004, 227-233P
[50]W.abdel-Hamid, T.Abdelazim. Improvement of MEMS-IMU/GPS Performance using fuzzy modeling. GPS Solut. February 2006, Volume10, Issue1, 1-11P
[51]Haiying Hou. Modeling inertial sensors errors using allan variance. The University of Calgary's master degree paper.2004
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