Time-domain Statistics of the Electromagnetic Bias in GNSS-Reflectometry
AbstractGlobal Navigation Satellite Systems-Reflectometry (GNSS-R) is an emerging remote sensing technique that uses navigation signals reflected on the Earth’s surface as sources of opportunity for scatterometry and altimetry. The time-domain statistics of the electromagnetic bias in GNSS-R altimetry are investigated to assess the residual electromagnetic bias after averaging during the dwell time (as long as 100 s). A three-dimensional time-evolving sea surface is generated using Elfouhaily’s ocean surface height spectrum and spreading function. This surface is illuminated by a right hand circular polarization electromagnetic wave at L-band. Then, the scattered waves are computed using the Physical Optics method under the Kirchhoff Approximation. The electromagnetic bias is estimated using a numerical technique previously validated at C- and Ku-bands, and then extrapolated at L-band. Montecarlo simulations for different sea surface realizations consecutive in time are performed so as to analyze the electromagnetic bias statistics up to the 4PthP order moments. Histograms and distribution of the time domain electromagnetic bias are also used for statistical interpretation. All statistical descriptors confirmed that the electromagnetic bias has a non-Gaussian behavior. This study is important to assess the residual electromagnetic bias in future GNSS-R altimetry missions, such as the “GNSS Reflectometry, Radio Occultation and Scatterometry on board the International Space Station” experiment onboard the International Space Station. View Full-Text
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Ghavidel, A.; Camps, A. Time-domain Statistics of the Electromagnetic Bias in GNSS-Reflectometry. Remote Sens. 2015, 7, 11151-11162.
Ghavidel A, Camps A. Time-domain Statistics of the Electromagnetic Bias in GNSS-Reflectometry. Remote Sensing. 2015; 7(9):11151-11162.Chicago/Turabian Style
Ghavidel, Ali; Camps, Adriano. 2015. "Time-domain Statistics of the Electromagnetic Bias in GNSS-Reflectometry." Remote Sens. 7, no. 9: 11151-11162.