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Open AccessArticle

Circular Regression Applied to GNSS-R Phase Altimetry

Laboratoire d’Informatique, Signal et Image de la Côte d’Opale (LISIC, EA 4491), Université du Littoral Côte d’Opale (ULCO), F-62228 Calais, France
Authors to whom correspondence should be addressed.
Academic Editors: Weimin Huang and Xiaofeng Li
Remote Sens. 2017, 9(7), 651;
Received: 5 May 2017 / Revised: 15 June 2017 / Accepted: 20 June 2017 / Published: 23 June 2017
(This article belongs to the Section Ocean Remote Sensing)
This article is dedicated to the design of a linear-circular regression technique and to its application to ground-based GNSS-Reflectometry (GNSS-R) altimetry. The altimetric estimation is based on the observation of the phase delay between a GNSS signal sensed directly and after a reflection off of the Earth’s surface. This delay evolves linearly with the sine of the emitting satellite elevation, with a slope proportional to the height between the reflecting surface and the receiving antenna. However, GNSS-R phase delay observations are angular and affected by a noise assumed to follow the von Mises distribution. In order to estimate the phase delay slope, a linear-circular regression estimator is thus defined in the maximum likelihood sense. The proposed estimator is able to fuse phase observations obtained from several satellite signals. Moreover, unlike the usual unwrapping approach, the proposed estimator allows the sea-surface height to be estimated from datasets with large data gaps. The proposed regression technique and altimeter performances are studied theoretically, with further assessment on both synthetic and real data. View Full-Text
Keywords: GNSS-reflectometry; altimetry; circular statistics; GNSS signal processing GNSS-reflectometry; altimetry; circular statistics; GNSS signal processing
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MDPI and ACS Style

Kucwaj, J.-C.; Reboul, S.; Stienne, G.; Choquel, J.-B.; Benjelloun, M. Circular Regression Applied to GNSS-R Phase Altimetry. Remote Sens. 2017, 9, 651.

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