局域均值分解在MEMS陀螺随机误差消噪上的应用

Random Error Filtering Based on Local Mean Decomposition for MEMS Gyro

针对标准Kalman滤波需要建立准确的系统模型、小波阈值降噪对小波基和阈值的选取依赖于经验的不足,将局域均值分解(LMD)方法引入MEMS陀螺的随机误差滤波。该方法可自适应地将随机误差信号分解为若干PF分量之和,且对各分量进行小波降噪处理,将处理后的各分量相加得到降噪信号。实验分析表明该滤波方法效果明显。

The standard Kalman filter needs exact system model, and the choosing of wavelet base and threshold in wavelet threshold de-noising is dependent on the experience. Taking the problems into consideration,we proposed a new filtering method based on Local Mean Decomposition （LMD）. Random error signal was decomposed to several Production Function （PF） adaptively, and wavelet de-nosing was made to some given frequency PF that contained noises. Each PF after processing can be reconstructed to obtain the de-noised signal. The experiment shows that wavelet threshold de-noising based on LMD has