摘要
为解决MEMS陀螺输出信号中噪声大、随机漂移严重的问题,提出了一种基于小波去噪和AR建模的MEMS陀螺组合数据处理方法。采用小波去噪法对MEMS陀螺输出信号去噪,自适应确定小波分解层数,提高了其信噪比。采用AR(auto-regressive,自回归)模型对MEMS陀螺的随机漂移进行建模,利用平均均方预测误差确定模型的最佳阶数,并与传统的一阶马尔可夫模型进行了比较。实验结果表明,该组合数据处理方法可有效抑制MEMS陀螺输出噪声,且能更精确地对MEMS陀螺随机漂移进行建模及预测。
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 denoising and AR modeling is presented. Firstly, wavelet denoising is used to denoise and improve the signal-to-noise ratio of MEMS gyroscope output. The optimal number of decomposition level is selected using adaptive algorithm. Then AR model is established to model MEMS gyroscope random drift and compared with a first-order- Gauss-Ma- rkov process （GM）. The method based on average mean squared prediction error （APE） is used to choose the optimal order of AR model. Experiments indicated that the proposed method could effectively suppress noise and could more accurately model and predict the random drift of MEMS gyroscope when compared to a first-order-Gauss-Markov process.
出处
《计算机工程与设计》
CSCD
北大核心
2010年第19期4280-4283,共4页
Computer Engineering and Design
