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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,在输出差分信号滤波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。
From the perspective of practical engineering applications, an effective filtering and noise reduction method for micro-electro-mechanical systems (MEMS) gyroscope errors is discussed. Based on the basic principle of stochastic sequence analysis, a real-time average algorithm is used to preprocess the gyroscope’s original measurement data to obtain the stochastic drift signal. The differential drift signal after the removal of the asymptotic term is modeled as AR model, and the improved Kalman filter is built according to the model. The output of the differential signal is filtered and the current gyro output filter value is calculated. The error compensation results of the measured data of a MEMS gyroscope show that the proposed filtering method can effectively suppress the drift error and improve the measurement accuracy in practical applications.