The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 153–158, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-153-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 153–158, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-153-2021

  30 Jun 2021

30 Jun 2021

ANALYSIS THE INFLUENCE OF MODULATED AMPLITUDE ON COMMON MODE ERROR BASED ON GPS DATA

F. Wang, P. Zhang, Z. Sun, Q. Zhang, and J. Liu F. Wang et al.
  • National Geomatics Center of China, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China

Keywords: GPS, amplitude modulation, time series, common mode error, PCA

Abstract. Time series analysis uses constant amplitude models to estimate seasonal changes, while the actual seasonal changes of station coordinates have varying degrees of modulation. The difference between the real modulation amplitude and the estimated constant amplitude enters the residual sequence. We analysed the contribution of the modulation amplitude to the regional CME characteristics based on the 410 GPS stations which located in China. The PCA method is used to carry out regional common-mode error analysis on the obtained residuals time series which is after deduction of deformation signals such as tectonic movements. The spectral analysis shows that the CME considering the amplitude modulation significantly weakens the characteristics of the annual cycle. The annual spectral peaks of the north components are reduced by 50%, the east components with a reduction of 80% and a reduction of 60% in the elevation component. The results of noise analysis show that the FN in CME that considers amplitude modulation is significantly lower than that of constant amplitude. This indicate that in time series analysis, the ‘signal’ that has not been estimated due to the oversimplification of the parameters is filtered in the area time will be evolved into CME, which means that CME not only contains errors, but also ‘signals’, that is, ‘signals’ that are not correctly modelled will affect the regional filtering effect.