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Denoising GPS-Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet

机译:基于混合EMD和小波包的基于GPS的结构监测数据降噪

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摘要

High-frequency components are often discarded for data denoising when applying pure wavelet multiscale or empirical mode decomposition (EMD) based approaches. Instead, they may raise the problem of energy leakage in vibration signals. Hybrid EMD and wavelet packet (EMD-WP) is proposed to denoise Global Positioning System- (GPS-) based structure monitoring data. First, field observables are decomposed into a collection of intrinsic mode functions (IMFs) with different characteristics. Second, high-frequency IMFs are denoised using the wavelet packet; then the monitoring data are reconstructed using the denoised IMFs together with the remaining low-frequency IMFs. Our algorithm is demonstrated on a synthetic displacement response of a 3-story frame excited by El Centro earthquake along with a set of Gaussian random white noises on different levels added. We find that the hybrid method can effectively weaken the multipath effect with low frequency and can potentially extract vibration feature. However, false modals may still exist by the rest of the noise contained in the high-frequency IMFs and when the frequency of the noise is located in the same band as that of effective vibration. Finally, real GPS observables are implemented to evaluate the efficiency of EMD-WP method in mitigating low-frequency multipath.
机译:当应用基于纯小波多尺度或经验模式分解(EMD)的方法时,经常会丢弃高频分量以进行数据降噪。相反,它们可能会引起振动信号中能量泄漏的问题。提出了混合EMD和小波包(EMD-WP)对基于全球定位系统(GPS)的结构监测数据进行去噪。首先,将现场可观测量分解为具有不同特征的固有模式函数(IMF)的集合。其次,使用小波包对高频IMF进行去噪;然后使用降噪的IMF和其余的低频IMF重建监控数据。我们的算法在El Centro地震激发的三层框架的综合位移响应以及一组不同级别的高斯随机白噪声的基础上得到了证明。我们发现,混合方法可以有效地减弱低频的多径效应,并有可能提取振动特征。但是,高频IMF中包含的其余噪声以及当噪声的频率与有效振动位于同一频带时,错误的模态可能仍然存在。最后,通过实际的GPS观测值来评估EMD-WP方法在减轻低频多径方面的效率。

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  • 来源
    《Mathematical Problems in Engineering》 |2017年第12期|4920809.1-4920809.7|共7页
  • 作者

    Ke Lu;

  • 作者单位

    Zhejiang Univ Water Resources & Elect Power, Coll Informat Engn & Art Design, Hangzhou 310018, Zhejiang, Peoples R China;

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