一种结合改进经验模态分解和前向线性预测算法的光纤陀螺仪混合去噪方法。

Hybrid de-noising approach for fiber optic gyroscopes combining improved empirical mode decomposition and forward linear prediction algorithms.

作者信息

Shen Chong, Cao Huiliang, Li Jie, Tang Jun, Zhang Xiaoming, Shi Yunbo, Yang Wei, Liu Jun

机构信息

Key Laboratory of Instrumentation Science and Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, People's Republic of China and National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, People's Republic of China.

出版信息

Rev Sci Instrum. 2016 Mar;87(3):033305. doi: 10.1063/1.4941437.

DOI:10.1063/1.4941437

PMID:27036770

Abstract

A noise reduction algorithm based on an improved empirical mode decomposition (EMD) and forward linear prediction (FLP) is proposed for the fiber optic gyroscope (FOG). Referred to as the EMD-FLP algorithm, it was developed to decompose the FOG outputs into a number of intrinsic mode functions (IMFs) after which mode manipulations are performed to select noise-only IMFs, mixed IMFs, and residual IMFs. The FLP algorithm is then employed to process the mixed IMFs, from which the refined IMFs components are reconstructed to produce the final de-noising results. This hybrid approach is applied to, and verified using, both simulated signals and experimental FOG outputs. The results from the applications show that the method eliminates noise more effectively than the conventional EMD or FLP methods and decreases the standard deviations of the FOG outputs after de-noising from 0.17 to 0.026 under sweep frequency vibration and from 0.22 to 0.024 under fixed frequency vibration.

摘要

提出了一种基于改进经验模态分解（EMD）和前向线性预测（FLP）的光纤陀螺仪（FOG）降噪算法。该算法被称为EMD-FLP算法，其开发目的是将光纤陀螺仪的输出分解为多个固有模态函数（IMF），然后进行模态处理，以选择仅含噪声的IMF、混合IMF和残余IMF。接着采用FLP算法处理混合IMF，从中重构出精细的IMF分量，以产生最终的降噪结果。这种混合方法应用于模拟信号和光纤陀螺仪的实验输出，并进行了验证。应用结果表明，该方法比传统的EMD或FLP方法更有效地消除了噪声，在扫频振动下，降噪后光纤陀螺仪输出的标准差从0.17降至0.026，在固定频率振动下，从0.22降至0.024。