Acta Geodaetica et Cartographica Sinica >

2016 , Vol. 45 >Issue S2: 22 - 30

DOI: https://doi.org/10.11947/j.AGCS.2016.F022

An Improved Semisoft Threshold Algorithm and Its Evaluation for Denoising Random Walk in GNSS Time Series

  • WU Hao ,
  • CAO Tingquan ,
  • HUA Xianghong ,
  • ZOU Jingui ,
  • SHI Wenzhong ,
  • LU Nan
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  • 1. School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China;
    2. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
    3. Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China

Received date: 2016-11-25

  Revised date: 2016-12-20

  Online published: 2017-05-20

Supported by

The Key Consulting Project of Chinese Academy of Engineering (No.2016-XZ-13);The National Natural Science Foundation of China (No. 41671406);The Hubei Provincial Natural Science Foundation of China (Nos. 2016CFA013;2016AHB015)

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Abstract

The differences in the satellite orbit and signal quality of global navigation satellite positioning system, resulting in the complexity of random walk noise in GNSS time series, has become a bottleneck problem in applying GNSS technology to the high precision deformation monitoring industry. For the complex characteristics of random walk noise, small magnitude, low frequency and low sensitivity, an improved semisoft threshold algorithm is presented. Then it forms a unified system of semisoft threshold function, so as to improve the adaptability of conventional semisoft threshold for random walk noise. In order to verify and evaluate the effect of improved semisoft threshold algorithm, MATLAB platform is used to generate a linear trend, periodic and random walk noise of the GNSS time series, a total of 1700 epochs. The results show that the improved semisoft threshold method is better than the classical method, and has better performance in the SNR and root mean square error. The evaluation results show that the morphological character has been performanced high consistency between the noise reduced by improved method with random walk noise. Further from the view of quantitative point, the evaluation results of spectral index analysis verify the applicability of the improved method for random walk noise.

Key words： GNSS; time series analysis; random walk noise; improved semisoft threshold algorithm

Cite this article

WU Hao , CAO Tingquan , HUA Xianghong , ZOU Jingui , SHI Wenzhong , LU Nan . An Improved Semisoft Threshold Algorithm and Its Evaluation for Denoising Random Walk in GNSS Time Series[J]. Acta Geodaetica et Cartographica Sinica, 2016 , 45(S2) : 22 -30 . DOI: 10.11947/j.AGCS.2016.F022

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