MEMS陀螺误差建模与滤波方法

从实际工程应用角度出发,探讨了微机电系统(micro-electro-mechanical systems,MEMS)陀螺误差的有效滤波降噪方法.基于随机序列时序分析法的基本原理,采用实时平均算法对陀螺原始量测数据进行常值补偿预处理,得到随机漂移信号.对去除渐进项后的差分漂移信号进行AR模型建模,并依据该模型进行改进卡尔曼滤波,在输出差分信号滤渡值的同时解算当前陀螺输出滤波值.通过对某MEMS陀螺实测数据的误差补偿结果表明,提出的滤波方法能够有效地抑制其漂移误差,提高实际应用中的测量精度.

Error modeling and filtering method for MEMS gyroscope

The compensating method for micro-electro-mechanical systems(MEMS) gyroscopic random drift error is discussed from the point of view on practical applications.Based on the principle of the time-sequence analysis from the random series,the original measurement from the MEMS gyroscope is pretreated by an real time average estimation algorithm to get random drift signals.The residual signal is modeled by an AR model and is filtered by Kalman filtering.The compensating results for the practical testing data of an MEMS gyroscope show that the drift error can be effectively reduced by using the filtering method presented,and the measurement accuracy in practice can be further improved.