基于MEEMD与FLP的光纤陀螺去噪

戴邵武,陈强强,刘志豪,等. 基于MEEMD与FLP的光纤陀螺去噪[J]. 光电工程,2020,47(6):190137. doi: 10.12086/oee.2020.190137
引用本文: 戴邵武,陈强强,刘志豪,等. 基于MEEMD与FLP的光纤陀螺去噪[J]. 光电工程,2020,47(6):190137. doi: 10.12086/oee.2020.190137
Dai S W, Chen Q Q, Liu Z H, et al. De-noising algorithm for FOG based on MEEMD and FLP algorithm[J]. Opto-Electron Eng, 2020, 47(6): 190137. doi: 10.12086/oee.2020.190137
Citation: Dai S W, Chen Q Q, Liu Z H, et al. De-noising algorithm for FOG based on MEEMD and FLP algorithm[J]. Opto-Electron Eng, 2020, 47(6): 190137. doi: 10.12086/oee.2020.190137

基于MEEMD与FLP的光纤陀螺去噪

  • 基金项目:
    山东省自然科学基金面上项目资助(ZR2017MF036)
详细信息
    作者简介:
    通讯作者: 陈强强(1993-),男,博士研究生,主要从事飞行器综合导航的研究。E-mail: 1195275597@qq.com
  • 中图分类号: TP212

De-noising algorithm for FOG based on MEEMD and FLP algorithm

  • Fund Project: Supported by the Natural Science Foundation of Shandong (ZR2017MF036)
More Information
  • 为了降低噪声对光纤陀螺输出的影响,提出了一种基于改进经验模态分解(MEEMD)和前向线性预测(FLP)结合的光纤陀螺去噪算法。首先,引入排列熵概念,利用改进经验模态分解对光纤陀螺信号进行分解与重构;然后针对分解后混合噪声的低阶IMF项,通过FLP算法进行滤波去噪;最后将经过MEEMD-FLP处理后的信号进行重构以得到结果。对某干涉型FOG进行静态测试,通过实测数据计算结果表明:与原始FOG信号相比,降噪后的RMSE降低了76.77%,标准差降低了76.76%。该算法可有效降低噪声对FOG输出信号的影响,具有更高的去噪精度。

  • Overview: Fiber optic gyroscope (FOG) is a new inertial sensor based on the Sagnac effect and it is widely used in servo control, flight control and inertial navigation. It has the advantages of high reliability, high measurement accuracy, and ease of integration. It has become an ideal device for inertial navigation systems. The collected fiber optic gyroscope drift data is affected by many factors such as the light source, fiber bending, and ambient temperature, making it often submerged in the noise and leading to difficulties in direct modeling compensation. In order to establish an accurate error compensation model, data preprocessing is demanded to output data on the gyroscope.

    In order to reduce the influence of noise on the output signal of fiber optic gyroscope, a de-noising algorithm of fiber optic gyroscope signal based on modified ensemble empirical mode decomposition (MEEMD) and forward linear prediction (FLP) is proposed. At the beginning, we studied the output signal of fiber optic gyroscope in depth and discovered that it is complicated that we cannot reduce the noise directly. As a result, the concept of permutation entropy (PE) is introduced. PE is a new algorithm proposed for detecting the randomicity and dynamic changes of time series, which can be used in the field of time series analysis. According to the PE theory, MEEMD algorithm is proposed and the fiber optic gyroscope signal is decomposed and reconstructed. Then, the low-order IMF terms of the mixed noise after decomposition is filtered and de-noised by the FLP algorithm. Finally, the signal processed by the MEEMD-FLP is reconstructed to get the result. The static test of a fiber optic gyroscope is carried out. The experimental results show that compared with the original fiber optic gyroscope signal, the RMSE after de-noising is reduced by 76.77%, and the standard deviation is reduced by 76.76%. It can effectively reduce the influence of noise on the fiber optic gyroscope output signal and has higher de-noising accuracy.

    In a word, compared with the existing methods, the proposed method is applied to the original fiber optic gyroscope signal's adaptive analysis and de-noising modeling, which is completely adaptive instead of other man-made settings, such as the choice of wavelet basis function in the wavelet transform. It improved the de-noising accuracy of the system while reducing the influence of noise. We can predict that the method provides a new perspective for the analysis and de-noising of the fiber optic gyroscope's signal.

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  • 图 1  MEEMD-FLP算法流程图

    Figure 1.  Flowchart of MEEMD-FLP

    图 2  光纤陀螺的原始含噪信号

    Figure 2.  The orignanoiy signal of FOG

    图 3  EMD分解结果

    Figure 3.  The results of EMD

    图 4  MEEMD分解结果

    Figure 4.  The results of MEEMD

    图 5  序列的排列熵

    Figure 5.  Permutation entropy of IMFs

    图 6  MEEMD-FLP处理结果

    Figure 6.  Detailed processing by the MEEMD-FLP

    图 7  三种去噪方法的性能对比

    Figure 7.  Performance comparison of three methods

    图 8  去噪后的均值及标准差

    Figure 8.  Standard deviations and means of the FOG outputs after de-noising

    表 1  三种去噪方法的性能对比

    Table 1.  Performance comparison of the three methods

    指标 原数据 FLP EMD MEEMD-FLP
    RMSE 0.001421 0.00099 0.000738 0.00033
    SSE 0.008078 0.003927 0.00218 0.000436
    R 0.013235 0.0082 0.0058 0.0026
    下载: 导出CSV
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出版历程
收稿日期:  2019-03-25
修回日期:  2019-11-16
刊出日期:  2020-06-01

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