基于经验模分解的陀螺信号去噪

陀螺随机漂移是影响寻北精度的重要因素,小波消噪方法对小波基和分解尺度等因素依赖性较强。提出了一种新的基于功率谱密度准则的经验模态分解(EMD)去噪方法,可有效解决传统EMD去噪自适应滤波器截止阶数难以确定的难题,该方法将经验模态分解得到的固有模态函数(IMF)分为信号分量起主导作用模态与噪声分量起主导作用模态,并对噪声分量起主导作用的模态进行类似小波软阈值去噪的方法进行滤波,然后与信号分量起主导作用的模态共同对信号重建实现去噪。将该方法应用于测试信号与陀螺信号的去噪,结果表明:新方法能有效地判断噪声与信号起主导作用的模态分界点,具有良好的去噪效果,且不受主观参数的影响,具有自适应性。

A Denoising Method for Gyro Signal Based on Empirical Mode Decomposition

Gyro random drift is a remarkable factor that can affect the precision of north seeker,and the wavelet denoising method depends greatly on the selection of wavelet base and decomposition scale.A new denoising method of the empirical mode decomposition(EMD) is presented in this paper based on the power spectral density criteria,and it can effectively solve the problems which traditional EMD denoising adaptive filter is hard to define cutoff order number.This method can divide the intrinsic mode functions(IMF) derived from EMD into signal dominant modes and noise dominant modes,and filter the noise dominant modes using a similar wavelet soft threshold method,then reconstruct signal and realize denoising together with signal dominant modes.Simulations were conducted for simulated signals and a gyro signal using this method,the results showed that: the new denoising method can effectively judge the boundary between noise dominant modes and signal dominant modes,and this method does not be affected by subjective parameters and has good denoising effect and adaptability.