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

Abstract

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, highfrequency 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.

A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring

“…In addition, wavelet transformation has been widely applied to denoising in GNSS positioning [144,[149][150][151][152][153][154]. In Wang et al [150], an EMD and wavelet combined model is applied to extract the systematic errors from GPS double differential residual series.…”

“…To sum up, the role of wavelets in GNSS-based SHM applications is divided into the following four categories: outlier (change) detection [25,46,143,147], bias separation [142], multipath mitigation [148], and denoising [144,[149][150][151][152][153][154].…”

PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts

“…The study of Nitsche et al [38] adopted a Kalman filter to carry out preprocessing operation over the track data. Rahemi et al [41] have used the least squared approach for evaluating the signals obtained GPS. The recent work carried out by Ke [42] have used the hybrid decomposition of empirical values of GPS and applied denoising principle over white noise.…”

Hybrid Wavelet and Principal Component Analyses Approach for Extracting Dynamic Motion Characteristics from Displacement Series Derived from Multipath-Affected High-Rate GNSS Observations

“…It can be used to extract the movement components, whereas the wavelet can divide the frequencies of measurements into low and high frequencies [30]. In addition, it can be integrated with other smoothing methods to improve the denoising outputs of sensors' measurements [31,32]. The energy of wavelet decompositions is the main parameter for signal denoising [33,34].…”