小波阈值去噪和FAR建模结合的MEMS陀螺数据处理方法

为解决MEMS陀螺输出信号中噪声大、随机漂移严重的问题,提出了一种小波阈值去噪和函数系数自回归FAR建模结合的MEMS陀螺数据处理方法。采用小波阈值去噪法对MEMS陀螺输出信号去噪,提高其信噪比;为克服常用的自回归AR模型无法解决MEMS陀螺随机漂移存在的非线性问题,引入FAR模型对MEMS陀螺的随机漂移进行建模。实验结果表明,此数据处理方法可有效抑制MEMS陀螺输出噪声,且与AR模型相比,FAR模型能更精确地对MEMS陀螺随机漂移进行建模及预测。

A combined method for MEMS gyroscope data processing based on wavelet thresholding denoising and FAR modeling

To solve the problem that the outputs of MEMS gyroscope contain high noise and serious random drift,a combined method for MEMS gyroscope data progressing based on wavelet thresholding denoising and FAR modeling is presented.Firstly, wavelet thresholding denoising is used to denoise and improve the signal-to-noise ratio of MEMS gyroscope output.As AR model couldn't solve the nonlinear problem of MEMS gyroscope random drift,FAR model is introduced to model MEMS gyroscope random drift.Experiments show that the proposed method could effectively suppress noise,and FAR could more accurately model and predict the random drift of MEMS gyroscope when compared to AR model.