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Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis

College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
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Remote Sens. 2020, 12(6), 992; https://doi.org/10.3390/rs12060992
Received: 21 February 2020 / Revised: 13 March 2020 / Accepted: 17 March 2020 / Published: 19 March 2020
(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))
The daily position time series derived by Global Navigation Satellite System (GNSS) contain nonlinear signals which are suitably extracted by using wavelet analysis. Considering formal errors are also provided in daily GNSS solutions, a weighted wavelet analysis is proposed in this contribution where the weight factors are constructed via the formal errors. The proposed approach is applied to process the position time series of 27 permanent stations from the Crustal Movement Observation Network of China (CMONOC), compared to traditional wavelet analysis. The results show that the proposed approach can extract more exact signals than traditional wavelet analysis, with the average error reductions are 13.24%, 13.53% and 9.35% in north, east and up coordinate components, respectively. The results from 500 simulations indicate that the signals extracted by proposed approach are closer to true signals than the traditional wavelet analysis. View Full-Text
Keywords: GNSS position time series; signal extraction; formal errors; wavelet analysis GNSS position time series; signal extraction; formal errors; wavelet analysis
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Ji, K.; Shen, Y.; Wang, F. Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis. Remote Sens. 2020, 12, 992.

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