Denoising effect of multiscale multiway analysis on high-rate GPS observations

• Published in: GPS solutions Vol. 21; no. 1; pp. 31 - 41
• Main Authors: Li, Yanyan, Xu, Caijun, Yi, Lei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2017; Springer Nature B.V
• Subjects: Amplitudes; Atmospheric Sciences; Automotive Engineering; Component reliability; Earth and Environmental Science; Earth Sciences; Electrical Engineering; Flicker; Geophysics/Geodesy; Global positioning systems; GPS; Multiscale analysis; Noise; Noise reduction; Original Article; Performance assessment; Power law; Principal components analysis; Random noise; Reliability analysis; Satellite navigation systems; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Spatial data; Spectral analysis; Spectrum analysis; White noise; Spatial analysis; GPS; Noise; Wavelet denoising; Spectral analysis; Principal component analysis
• ISSN: 1080-5370; 1521-1886
• DOI: 10.1007/s10291-015-0502-0

In general, high-rate GPS data sets are subject to common mode error (CME), multipath error, and high-frequency random noise, which adversely affect the GPS positioning accuracy. In order to improve the precision and reliability of GPS positioning, a multiscale multiway principal component analysis (MSMPCA) denoising method is introduced here. The 1-Hz GPS coordinate time series at ten stations from the California Real Time GPS Network are employed to assess the performance of MSMPCA. Its results are compared with those of the classical denoising methods, including wavelet denoising, PCA, multiway PCA, and multiscale PCA. The results indicate that MSMPCA is able to eliminate not only high-frequency random noise but also low-frequency errors and CME. Furthermore, quantitative analysis shows that MSMPCA is more accurate than the classical denoising methods. Spectral analysis shows that a combination of white plus flicker noise is considered to be the adequate model for the noise characteristics of all three components. Both white and power-law noise amplitudes are smallest in the north component and largest in the vertical component. MSMPCA decreases the mean amplitudes of white noise from 1.3 to 0.0, 0.9 to 0.0, and 2.8 to 0.1 mm in north, east, and vertical components, and those of power-law noise from 4.6 to 1.2, 3.9 to 1.1, and 19.9 to 8.4 mm, respectively. MSMPCA is a promising alternative for removing noise of various frequencies (0.00025–0.5 Hz) from high-rate GPS signals.