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基于形态学的自动驾驶仪振动信号基线漂移去噪

张景元 何玉珠

张景元, 何玉珠. 基于形态学的自动驾驶仪振动信号基线漂移去噪[J]. 北京航空航天大学学报, 2018, 44(5): 907-913. doi: 10.13700/j.bh.1001-5965.2017.0371
引用本文: 张景元, 何玉珠. 基于形态学的自动驾驶仪振动信号基线漂移去噪[J]. 北京航空航天大学学报, 2018, 44(5): 907-913. doi: 10.13700/j.bh.1001-5965.2017.0371
ZHANG Jingyuan, HE Yuzhu. Removing baseline drift in vibration signal of autopilot based on morphology[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 907-913. doi: 10.13700/j.bh.1001-5965.2017.0371(in Chinese)
Citation: ZHANG Jingyuan, HE Yuzhu. Removing baseline drift in vibration signal of autopilot based on morphology[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 907-913. doi: 10.13700/j.bh.1001-5965.2017.0371(in Chinese)

基于形态学的自动驾驶仪振动信号基线漂移去噪

doi: 10.13700/j.bh.1001-5965.2017.0371
详细信息
    作者简介:

    张景元  男, 硕士研究生。主要研究方向:自动测试技术与故障诊断

    何玉珠  男, 博士, 教授。主要研究方向:测试系统通用性技术、故障诊断、定位技术

    通讯作者:

    何玉珠, E-mail: heyuzhuhe@buaa.edu.cn

  • 中图分类号: TP391

Removing baseline drift in vibration signal of autopilot based on morphology

More Information
  • 摘要:

    导弹自动驾驶仪在振动测试过程中存在信号基线漂移且污染严重的问题,而传统的时频处理方法难以达到去噪要求,因此基于形态学基本原理提出了一种用于解决振动信号基线漂移的滤波方法。该滤波方法由3级结构组成,前2级结构均是基于形态学基本原理,第3级进行相消与平滑处理,通过相互级联,可以有效抑制基线漂移。此外,通过引入粒子群优化(PSO)算法使得该滤波方法更具适应性。对比实验利用该滤波方法和对比方法对自动驾驶仪实测振动信号与标准ECG信号进行了处理,结果表明:该滤波方法在抑制基线漂移方面要优于小波阈值去噪和传统的形态学去噪。

     

  • 图 1  级联滤波器结构模型

    Figure 1.  Structure model of cascading filter

    图 2  直线形结构元素基本模型

    Figure 2.  Basic model of linear structural elements

    图 3  静止和振动状态实测信号

    Figure 3.  Measured signals in static and vibration states

    图 4  传统形态学方法去噪结果

    Figure 4.  Denoising results of traditional morphological method

    图 5  小波阈值方法去噪结果

    Figure 5.  Denoising results of wavelet threshold method

    图 6  Lθ对去噪效果的影响

    Figure 6.  Influence of L and θ on denoising

    图 7  本文方法对自动驾驶仪实测振动信号的去噪结果

    Figure 7.  Denoising results of measured vibration signal of autopilot using proposed method

    图 8  本文方法和对比方法对B型号导弹自动驾驶仪实测振动信号的去噪结果

    Figure 8.  Denoising results of measured vibration signal of Type B missile autopilot by proposed method and reference method

    图 9  含有基线漂移噪声的ECG信号去噪结果

    Figure 9.  Denoising results of ECG signals containing baseline drift noise

    表  1  不同分解层数下小波阈值去噪结果

    Table  1.   Denoising results of wavelet transformation with different wavelet-bases

    小波基 分解层数 均方差 信噪比 波形相似比
    sym8 2 0.151 20.80 0.94
    sym8 3 0.202 13.82 0.89
    sym8 4 0.428 -21.84 0.32
    db3 2 0.165 18.71 0.93
    db3 3 0.254 7.76 0.83
    db3 4 0.428 -21.42 0.32
    下载: 导出CSV

    表  2  PSO算法优化结果

    Table  2.   Optimization results using PSO algorithm

    实验次数 L1 θ1 L2 θ2 信噪比
    1 2 2 29 40 28.7
    2 3 3 30 80 28.7
    3 3 2 78 72 28.7
    4 3 5 68 65 27.6
    5 3 5 76 40 27.6
    6 2 3 36 72 28.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-06-05
  • 录用日期:  2017-08-01
  • 刊出日期:  2018-05-20

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