|Abstract:||The use of Global Navigation Satellite Systems (GNSS) signals for remote sensing applications has captured t he interest of the scientific community in the last decades because of its great potential as a multistatic radar system, achieving higher tempo ral and spatial resolution than the classic active radar technologies. Many applications have been developed using the reflections of the GNSS signals (GNSS-R) over the surface of our planet to extract geophysical information for a better understanding , forecasting and sounding of the global environment. The possibility to perform altimetric measurements with GNSS-R is of great interest in applications related to mesoscale oceanography. However, the navigation signals were not designed for these kind of applications, consequently, their low power and narrow bandwidth represent a limit on the precision achievable in comparison with other monostatic radar altimeters. In this work we present a method that aims to perform precise altimetric measurements through signal processing derived using the Maximum Likelihood Estimation theory. We use a signal model that represents the GNSS signal scattered over the ocean surface that leads to a more efficient use of the available reflected signal power. We present results using signals from NASA ’s Cyclone Global Navigation Satellite System (CYGNSS) mission and compare them to the performance obtained by previous retracking algorithms, achieving a greater gain in the lower SNR cases.|
Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018)
September 24 - 28, 2018
Hyatt Regency Miami
|Pages:||2859 - 2868|
|Cite this article:||
Ozafrain, Santiago, Roncagliolo, Pedro A., Muravchik, Carlos H., "Maximum Likelihood Estimation for Altimetry with Ocean-Reflected GNSS Signals," Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018), Miami, Florida, September 2018, pp. 2859-2868.
ION Members/Non-Members: 1 Download Credit