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Sensors 2016, 16(5), 679; doi:10.3390/s16050679

Assessment of Aliasing Errors in Low-Degree Coefficients Inferred from GPS Data

GNSS Research Center, Wuhan University, Wuhan 430079, Hubei, China
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 28 March 2016 / Revised: 29 April 2016 / Accepted: 5 May 2016 / Published: 11 May 2016
(This article belongs to the Section Remote Sensors)
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With sparse and uneven site distribution, Global Positioning System (GPS) data is just barely able to infer low-degree coefficients in the surface mass field. The unresolved higher-degree coefficients turn out to introduce aliasing errors into the estimates of low-degree coefficients. To reduce the aliasing errors, the optimal truncation degree should be employed. Using surface displacements simulated from loading models, we theoretically prove that the optimal truncation degree should be degree 6–7 for a GPS inversion and degree 20 for combing GPS and Ocean Bottom Pressure (OBP) with no additional regularization. The optimal truncation degree should be decreased to degree 4–5 for real GPS data. Additionally, we prove that a Scaled Sensitivity Matrix (SSM) approach can be used to quantify the aliasing errors due to any one or any combination of unresolved higher degrees, which is beneficial to identify the major error source from among all the unresolved higher degrees. Results show that the unresolved higher degrees lower than degree 20 are the major error source for global inversion. We also theoretically prove that the SSM approach can be used to mitigate the aliasing errors in a GPS inversion, if the neglected higher degrees are well known from other sources. View Full-Text
Keywords: optimal truncation degree; scaled sensitivity matrix approach (SSM); simulation study optimal truncation degree; scaled sensitivity matrix approach (SSM); simulation study

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wei, N.; Fang, R. Assessment of Aliasing Errors in Low-Degree Coefficients Inferred from GPS Data. Sensors 2016, 16, 679.

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