Fading statistics and sensing accuracy of ocean scattered GNSS and altimetry signals

Details Fading statistics and sensing accuracy of ocean scattered GNSS and altimetry signals Fading statistics and sensing accuracy of ocean scattered GNSS and altimetry signals

Jul 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010adspr..46..208g&link_type=abstract

Advances in Space Research, Volume 46, Issue 2, p. 208-220.

Mathematics

Statistics Theory

1

Scientific paper

This paper starts with a brief review of scattering statistics theory for diffusely scattered radar signals. Following, global navigation satellite systems reflected (GNSS-R) signals are used to demonstrate the results of non-coherent signal averaging on the mean and standard deviation of the signal power fluctuations. The retrieved signal fading statistics of GNSS-R signals detected in low Earth orbit have been analyzed as a function of consecutive uncorrelated measurements (or looks) and instrument antenna gain and compared to the theoretical description presented by Ulaby, Moore and Fung for a radar remote sensing configuration. The results of this analysis include empirically determined error distributions that provide a reference to asses the potential accuracy of GNSS-R model based retrieval methods, and to what extent increasing the instrument antenna gain will improve the retrieval accuracy. Additionally, in assessing the potential of using GNSS-R signals for ocean remote sensing, it is useful to compare the signal characteristics with those of existing radar altimeter instruments. With this in mind, a fitting accuracy and parameter retrieval analysis has been undertaken using observed fading noise from successful space based altimeters. The noise observed on real waveforms has been added to a simulated ideal waveform with the intent to study the accuracy of ocean observables using a Monte Carlo simulation. By adding observed noise to ideal model waveforms in this way, it is possible to quantitatively asses the fitting errors with respect to noise variance contained in the least-squares model fitting sensing technique. Finally, the key differences between the traditional approach and the GNSS-R approach are then analyzed with regard to identifying the critical adjustments required in using GNSS-R signals for ocean remote sensing.

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