摘要
光纤陀螺(FOG)随机噪声中包含了白噪声和具有长程相关性、自相似性及1/f^r类型谱密度特点的一种非平稳随机噪声——1/f^r类分形噪声。采用传统的方法很难去除该类噪声。由于小波分析的多分辨分析特性,使之成为研究分形噪声的有力工具。提出一种新的基于提升小波的自适应阈值选取滤波方法对光纤陀螺的输出信号进行阈值滤波,进而提高光纤陀螺的精度,算法包括提升小波分解、滤波参数估计及自适应软阈值滤波。对多组实测数据进行仿真实验,将传统小波固定阈值滤波方法与新方法进行比较,实验结果验证了新方法的有效性。
The output of fiber optic gyroscopes (FOG) involves Gaussian white noise and fractional noise which is difficult to eliminate by traditional methods because of the non-stationary, long-term correlation, self-similarity characteristics. On account of the characteristics of the wavelet multi-resolution analysis, wavelet analysis has become a powerful tool to study fractal noises. This paper introduces an effective technique for the de-noising of FOG corrupted by non-stationary noises. The proposed method is based on a second generation wavelet transform and level-dependent threshold estimator. The whole algorithm consists of the de-composition based on lifting wavelet, parameter estimation, and level-dependent soft threshold de-noising. The de-noising method based on traditional universal thresholding wavelet is also investigated to provide a comparison with the new method. Experimental results prove that the proposed method based on level-dependent lifting wavelet outperforms the traditional wavelet de-noising method.
出处
《中国激光》
EI
CAS
CSCD
北大核心
2009年第3期625-629,共5页
Chinese Journal of Lasers
基金
航天科技创新项目基金资助课题
关键词
光纤光学
提升小波
分形噪声
光纤陀螺
最大似然估计
软阈值滤波
Block codes
Fiber optics
Fibers
Gyroscopes
Optical materials
Optoelectronic devices
Parameter estimation
Security of data
Turbo codes
Wavelet transforms
White noise
