基于经验模分解的陀螺信号消噪  

De-noising Method for Gyro Signal Based on EMD

作　　者：甘雨[1] 隋立芬[1] 

机构地区：[1]信息工程大学测绘学院,河南郑州450052

出　　处：《测绘学报》2011年第6期745-750,共6页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(40974010);信息工程大学测绘学院硕士学位论文创新与创优基金(S201101)

摘　　要：陀螺随机漂移是影响惯性导航精度的重要因素。小波消噪方法对异常噪声效果不明显,且对小波基和分解尺度等因素依赖性较强。提出陀螺信号经验模分解(EMD)消噪方法,将信号进行经验模分解得到一个本征模态函数(IMF)组,先基于2σ准则处理异常噪声IMF分量,再利用相关系数确定高频噪声IMF分量个数,将噪声分量去除以实现陀螺信号消噪。详细对比小波方法与EMD方法,利用交叠式Allan方差分析两者的消噪效果,通过惯导算例进一步验证EMD方法的实效性。结果表明,相比小波方法,EMD消噪法能剔除异常噪声,可以更有效地抑制陀螺漂移。Gyro random drift is a remarkable factor that can affect the precision of inertial navigation system（INS）, Wavelet de-noising method is poor in coping with exceptional noise, and it depends greatly on the selection of wavelet base and decomposition scale, Empirical mode decomposition （EMD） de-noising method for gyrosignal is presented, The signal is decomposed into an intrinsic mode function （IMF） group, Based on this group, IMFs of exceptional noise are first disposed by 2σ criterion and then the number of IMFs of high frequency noise is determined by correlation coefficient. The de-noising process is finally done by removing the noisy IMFs, Detailed comparison between EMD method and wavelet method is given, Overlapping Allan variance is used to analyze the effect of the two methods, and the applicable ability of EMD method is tested through an INS calculation. It is shown that EMD method outperforms wavelet method in removing exceptional noise and is more efficient in weakening random drift.

关 键 词：陀螺随机漂移 小波 经验模分解 消噪 

分 类 号：P228[天文地球—大地测量学与测量工程]