MEMS加速度计噪声分析与降噪方法研究  

Research on noise analysis and noise reduction method of MEMS accelerometer

作　　者：杜少林 陈书钊 陈鹏光 田亚芳[1] 陈剑鸣[1] DU Shao-lin;CHEN Shu-zhao;CHEN Peng-guang;TIAN Ya-fang;CHEN Jian-ming(Faculty of Science,Kunming University of Science and Teelmology,Kuming 650504,Chin)

机构地区：[1]昆明理工大学理学院,云南昆明650504

出　　处：《传感器与微系统》2018年第7期45-48,55,共5页Transducer and Microsystem Technologies

基　　金：昆明理工大学计算物理与应用科技创新团队资助项目(KKGA201607014)

摘　　要：针对微机电系统(MEMS)加速度计的随机噪声对输出信号干扰的情况,提出了对加速度计噪声源及噪声类型进行辨识、估计与建模,并确定误差补偿的降噪方法,以提高加速度计精度。采用Allan方差分析法对MEMS加速度计的随机噪声进行分析,得到了影响MEMS加速度计性能的几种主要随机噪声,使用自回归滑动平均模型(ARMA)对加速度计输出数据进行数学建模,以最终预测误差(FPE)准则确定使用的模型与阶次。设计了Kalman滤波算法,对加速度计进行降噪,通过Allan方差方法对Kalman算法滤波效果进行分析。实验结果表明:Kalman滤波能有效降低加速度计的随机噪声。Aiming at problem of disturbance of random noise of micro-electro-mechanical system （MEMS） accelerometer on output signal, identification, estimation and modeling of aceelerometer noise source and noise type are carried out, and determination of denoising method for error compensation are needed to improve precision of aceelerometer. Allan variance analysis method is used to analyze on random noise of MEMS aceelerometer, several main random noise which affect on performance of MEMS accelerometer are obtained. Mathematical modeling of accelerometer output data is carried out using auto-regressive and moving average（ARMA） model, determine the used model and order by means of the final prediction error（FPE） criteria. Design Kalman filtering algorithm, and use Kalman filter for noise reduction of accelerometer. The Allan variance method is used to analyze on filtering effect of Kalman algorithm. Experimental results show that the Kalman filtering can effectively reduce random noise of accelerometer.

关 键 词：微机械加速度计 一随机噪声 ALLAN方差 自回归滑动平均模型 KALMAN滤波 

分 类 号：TH89[机械工程—仪器科学与技术]