基于经验模态概率分布的光纤陀螺信号处理

Fiber optic gyro signal processing based on empirical mode probability distribution

为了抑制光纤陀螺随机漂移，基于改进的经验模态分解(EMD)和新型模态筛选标准提出了一种自适应的区间阈值滤波方法。首先分析加入高斯噪声对 EMD 分解结果的影响，提出有界噪声辅助以改善 EMD 分解质量，然后针对本征模态函数的概率分布特征提出了基于样本熵的模态筛选标准，最后采用数据驱动的阈值选择方法实现自适应的区间阈值滤波。为了验证算法的有效性，采集一款干涉型光纤陀螺静态漂移信号进行实验分析，结果表明本文方法较基于平稳小波变换和 EMD 的阈值滤波有更好的去噪效果。仿真分析表明该去噪算法减小了捷联惯性导航系统的航向角误差，均方根误差较平稳小波变换去噪算法改善了约 78.6%。

Based on an improved empirical mode decomposition（EMD） and a newly proposed mode selection criterion,an adaptive interval threshold filtering method is developed to mitigate the random drift of fiber optic gyroscope. First,the effect of adding Gaussian noise to assist EMD decomposition is analyzed,and a bounded assist is developed to improve decomposition quality. Then,aiming at the probability distribution feature of intrinsic mode function,a novel mode selection criterion is proposed. Finally,an adaptive interval threshold filter is developed based on data-driven threshold selection. The experiment analysis,which employs static data detected from an interferometric FOG,is performed to verify the proposed algorithm,and the results show that,compared with threshold filtering methods based on stationary wavelet transform（SWT） and EMD,the proposed method significantly improves the denoising result. In addition,the simulation results show that,compared with SWT-based method,the proposed method improves the heading accuracy of strapdown inertial navigation system by 78.6% in term of mean square error.