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

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Abstract

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.

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Acknowledgments

This work is co-supported by the National Natural Science Foundation of China (No. 41431069, 41574002) and the National Key Basic Research Development Program (973 program) (No. 2013CB733304, 2013CB733303).

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Authors and Affiliations

  1. School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China

    Yanyan Li, Caijun Xu & Lei Yi

  2. Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan, 430079, China

    Caijun Xu

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  1. Yanyan Li
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  2. Caijun Xu
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Correspondence to Caijun Xu.

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Li, Y., Xu, C. & Yi, L. Denoising effect of multiscale multiway analysis on high-rate GPS observations. GPS Solut 21, 31–41 (2017). https://doi.org/10.1007/s10291-015-0502-0

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