中文摘要：

现阶段的经典模态分解MEMS陀螺消噪是一种易造成信号失真缺陷的强制消噪算法。针对该问题,在由本征模态函数样本熵和相似度参数线性组合确定复合指标的基础上,提出基于复合指标筛选本征模态函数的互补集合经验模态分解的阈值滤波降噪算法,根据信号的随机噪声特征参数确定阈值,并对筛选后的本征模态函数进行处理。仿真和实例分析结果表明,该算法的消噪效果较强制消噪滤波有较大提高,使MEMS陀螺信号的零偏不稳定性下降了76.4%,速率斜坡下降了62.3%,均方根误差下降了67.5%,同时能够提高MEMS-IMU在行人导航中的定位精度。

英文摘要：

In this paper a complementary ensemble empirical mode decomposition （UEENID） de-noising algorithm for MEMS-gyro, is proposed to alleviative the drawbacks of the present empirical mode decomposition （EMD） forced de-noising method. The proposed method readily conditions signal distortion based on intrinsic mode function （IMF）, as selected by composite evaluation; the results can be transferred into the linear combination of sample entropy （SE） and similarity. The filtering thresholdings are calculated by the signal noise parameters, and the relevant IMFs are filtered by those thresholdings. Simulation and test results show that the effect improves greatly as compared with the forced algorithm. In particular, the MEMS-gyro＇ s bias instability decreases by 76.4 %, the MEMS-gyro＇ s rate ramp decreases by 62.3% and the MEMS-gyro＇ s RMSE decreases by 67.5%, after filtering by the proposed method. Furthermore, the filter improves the positioning accuracy of MEMS-IMU pedestrian navigation.