最小二乘拟合的MEMS加速度计简易标定方法
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摘要
MEMS惯性测量单元中加速度计组件的确定性误差是惯性导航系统的主要误差源之一。针对分立式标定方法依赖专业外界设备、Kalman滤波方法需要严密的公式推导及精确调整参数的问题,提出一种基于惯性测量单元原地有效旋转的加速度计简易标定方法。通过分析加速度计器件误差模型,利用三轴加速度计输出适应椭球约束,提出以最小二乘拟合方法进行误差标定和补偿。与传统分立式标定方法不同,基于最小二乘拟合方法在有效旋转下,可在1min内快速确定加速度计确定性误差补偿系数。实验结果表明,该标定方法具有可行性和有效性,可满足实际外场标定场景。
The deterministic error of accelerometer components in MEMS IMU is one of the main causes of errors in inertial navigation system.To cope with the situation that discrete calibration method relies on professional external equipment and Kalman filtering method requires strict formula derivation and precise adjustment of parameters, a simple calibration method based on in-situ effective rotation of IMU is proposed. Through analysis of the accelerometer device error model and adaption of three-axis accelerometer to ellipsoid constraint, the least squares fitting method is proposed to perform error calibration and compensation. Compared with traditional discrete calibration method, the accelerometer deterministic error can be determined within 1 min of effective rotation based on least squares fitting method. Experiment results show that the proposed calibration method is feasible and effective, and it satisfies the external field calibration scenario.
The deterministic error of accelerometer components in MEMS IMU is one of the main causes of errors in inertial navigation system.To cope with the situation that discrete calibration method relies on professional external equipment and Kalman filtering method requires strict formula derivation and precise adjustment of parameters, a simple calibration method based on in-situ effective rotation of IMU is proposed. Through analysis of the accelerometer device error model and adaption of three-axis accelerometer to ellipsoid constraint, the least squares fitting method is proposed to perform error calibration and compensation. Compared with traditional discrete calibration method, the accelerometer deterministic error can be determined within 1 min of effective rotation based on least squares fitting method. Experiment results show that the proposed calibration method is feasible and effective, and it satisfies the external field calibration scenario.
引文
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[2]孙伟,吴家骥.基于间接EKF的MEMS惯性系统最优姿态估计[J].传感技术学报,2019,32(1):77-81.
[3]陆欣,刘忠,谢宇,等.附加运动约束的MEMS惯性传感器融合定姿算法[J].海军工程大学学报,2019,31(1):100-106.
[4]刘维亭,白杨.基于四元素的扩展卡尔曼滤波航姿参考系统算法设计[J].船舶工程,2015,37(4):53-56.
[5]刘兴川,张盛,李丽哲,等.基于四元数的MARG传感器姿态测量算法[J].清华大学学报(自然科学版),2012,52(5):627-631.
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[7]SIPOS M,PACESP,ROHACJ,et al.Analyses of Triaxial Accelerometer Calibration Algorithms[J].IEEE Sensors Journal,2012,12(5):1157-1165.
[8]NIEMINEN T,KANGAS J,SUURINIEMI S,et al.An Enhanced Multi-position Calibration Method for Consumer-grade Inertial Measurement Units Applied and Tested[J]. Measurement Science and Technology,2010,21(10):105204.
[9]LI Y,NIU X,ZHANG Q,et al.An in Situ Hand Calibration Method Using a Pseudo-observation Scheme for Low-end Inertial Measurement Units[J]. Measurement Science and Technology,2012,23(10):105104.
[10]LI Y,GEORGY J,NIU X,et al.Autonomous Calibration of MEMS Gyros in Consumer Portable Devices[J].IEEE Sensors Journal,2015,15(7):4062-4072.
[11]杨庆江,李国庆.低精度MEMSIMU的标定方法及误差分析[J].自动化与仪器仪表,2018,38(10):153-157,161.
[12]LOTTERSJC,SCHIPPERJ,VELTINKPH,et al.Procedure for In-use Calibration of Triaxial Accelerometers in Medical Applications[J].Sensors and Actuators A,1998,68(1-3):221-228.
[13]BONNETS,BASSOMPIERRE C,GODINC,et al.Calibration Methods for Inertial and Magnetic Sensors[J].Sensors and Actuators:A Physical,2009,156(2):302-311.
[14]宋宇,翁新武,郭昕刚.基于四元数EKF算法的小型无人机姿态估计[J].吉林大学学报(理学版),2015,53(3):511-518.