Accuracy enhancement of high-rate GNSS positions using a complete ensemble empirical mode decomposition-based multiscale multiway PCA. (January 2019)

• Record Type: Journal Article
• Authors: Li, Yanyan; Chen, Chuanfa; Fang, Rongxin; Yi, Lei
• Is Part Of: Journal of Asian earth sciences. Volume 169(2019)
• Page Start: 67
• Page End: 78
• Publication Date: 2019-01
• Subjects: High-rate GNSS -- Denoising -- PCA -- Empirical mode decomposition; Earth sciences -- Asia -- Periodicals; Sciences de la terre -- Asie -- Périodiques; Earth sciences; Asia; Periodicals; 555.05
• DOI: 10.1016/j.jseaes.2018.07.025
• Languages: English
• ISSNs: 1367-9120

Abstract

Graphical abstract: Highlights: An adaptive and data-driven method for denoising high rate GNSS is introduced. It is based on Complete Ensemble EMD and Multiscale Multiway PCA. It can significantly eliminate the white noise and colored noise in the GNSS data. It can be well suitable for nonlinear and nonstationary time series. Abstract: In order to improve the possibility of correct interpretation of some geophysical phenomena such seismology, it is important to reduce systematic errors such as common mode error (CME) and multipath errors, and to improve the accuracy of high-rate (1–5 Hz) displacements. This paper focuses on accuracy enhancement and noise assessment of high-rate Global Navigation Satellite System (GNSS) position time series of regional network of GNSS stations. Complete ensemble empirical mode decomposition-based multiscale multiway principal component analysis (C-MSMPCA) denoising method is introduced to denoise the high-rate (1–5 Hz) GNSS position time series, whose performance was compared with those of modified sidereal filtering (MSF), stacking filtering, PCA and the recently developed multiscale multiway PCA (MSMPCA). It turns out that C-MSMPCA can significantly eliminate the high-frequency random noise and remove the low-frequency CME, and other systematic errors such as multipath errors. Furthermore, for denoising high-rate GNSS time series, C-MSMPCA is more precise than the other denoising methods. Irrespective of sampling rates and data processing strategies, C-MSMPCA can respectively reach the accuracy of submillimeter- and millimeter-levels on the horizontal and vertical components of high-rate GNSS displacements. Spectral analysis on the 1 Hz coordinate time series from the Sichuan GNSS Continuous Observing Network of China suggests that Gauss-Markov process is an adequate noise model for the noise characteristics of the filtered GNSS data using MSMPCA and C-MSMPCA. The CME magnitudes of MSMPCA are 2.61, 2.55, and 6.72 mm (east, north, and vertical), and those of C-MSMPCA are 2.69, 2.80, and 7.80 mm, respectively.