基于EMD分解的MEMS加速度计随机误差补偿  

Random Noise Compensation Method for MEMS Accelerometer Based on EMD Decomposition

作　　者：田易[1,2] 李继秀 钟燕清[1] 阎跃鹏 孟真[1] 张兴成 TIAN Yi;LI Ji-xiu;ZHONG Yan-qing;YAN Yue-peng;MENG Zhen;ZHANG Xing-cheng(Institute of Microelectronics of the Chinese Academy of Science,Beijing 100029;University of Chinese Academy of Sciences,Beijing 100049)

机构地区：[1]中国科学院微电子研究所,北京100029 [2]中国科学院大学,北京100049

出　　处：《数字技术与应用》2021年第1期105-107,共3页Digital Technology & Application

摘　　要：为提高MEMS加速度计测量精度,采用了一种基于经验模态分解法(EMD)的随机误差补偿方法。文中通过EMD算法将加速度计信号分解为本征模态函数(IMFs)和一个残余分量,将IMF分为噪声主导分量、信号/噪声混合分量及信号主导分量三类:通过阈值处理实现信号/噪声混合分量降噪;将经过降噪的信号/噪声混合分量与信号主导分量进行重构,得到降噪后的加速度计信号。通过仿真动态数据验证和静态采集数据验证,证明算法有效提高了加速计的测量精度。In order to improve the measurement accuracy of MEMS accelerometers,a stochastic error compensation method based on empirical mode decomposition(EMD)is adopted.In this paper the accelerometer signal is decomposed into intrinsic mode function(IMFs)and a residual component by EMD algorithm,and IMF is divided into three categories:noise dominant component,signal/noise mixed component and signal dominant component;noise reduction of signal/noise mixed component is realized by threshold processing,the signal/noise mixed component after noise reduction and the dominant component of the signal are reconstructed to obtain the accelerometer signal after noise reduction.It is proved that the algorithm can effectively improve the measuring accuracy of the accelerometer through the verification of dynamic data and static data acquisition.

关 键 词：微机电系统加速度计 经验模态分解 随机误差补偿 本征模态函数 

分 类 号：TH824.4[机械工程—仪器科学与技术]