Study on the GPS Data De-noising Method Based on Wavelet Analysis

Yuan, Debao; Cui, Ximin; Guo, Wu; Jin, Jingjing; Fan, Dongli; Jia, Xiaogang

doi:10.1007/978-3-642-27278-3_42

Cited by 2 publications

(2 citation statements)

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“…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.…”

Section: Applicationmentioning

confidence: 99%

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

et al. 2019

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In the past few decades, global navigation satellite system (GNSS) technology has been widely used in structural health monitoring (SHM), and the monitoring mode has evolved from long-term deformation monitoring to dynamic monitoring. This paper gives an overview of GNSS-based dynamic monitoring technologies for SHM. The review is classified into three parts, which include GNSS-based dynamic monitoring technologies for SHM, the improvement of GNSS-based dynamic monitoring technologies for SHM, as well as denoising and detrending algorithms. The significance and progress of Real-Time Kinematic (RTK), Precise Point Position (PPP), and direct displacement measurement techniques, as well as single-frequency technology for dynamic monitoring, are summarized, and the comparison of these technologies is given. The improvement of GNSS-based dynamic monitoring technologies for SHM is given from the perspective of multi-GNSS, a high-rate GNSS receiver, and the integration between the GNSS and accelerometer. In addition, the denoising and detrending algorithms for GNSS-based observations for SHM and corresponding applications are summarized. Challenges of low-cost and widely covered GNSS-based technologies for SHM are discussed, and problems are posed for future research.

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Section: Applicationmentioning

confidence: 99%

“…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].…”

Section: Applicationmentioning

confidence: 99%