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Stochastic model for ocean surface reflected GPS signals and satellite remote sensing applications

Purdue University
Publication Date
  • Engineering
  • Aerospace|Remote Sensing
  • Earth Science
  • Mathematics
  • Physics


Information about the roughness of the ocean surface and related geophysical parameters, such as wind speed, is present in the shape of the code-correlation wave-form of forward scattered Global Positioning System (CPS) signals. Measurements of those waveforms can be inverted to estimate the surface roughness. It is desired to understand the limitations of the accuracy of these retrievals. This research investigated the statistics of the individual samples of the waveform which are the most relevant to predict the accuracy of these retrievals. A stochastic model for the wave-form time series measurements was developed to give the complete autocorrelation function as a function of the code delay (or lag) and local compensation frequency. The model of voltage and power signals can be applied to determine the upper limit for predetection integration time, and the time between independent waveform samples, respectively. Model predictions were validated at multiple delay-Doppler bins by comparing the predicted autocorrelation function of subsequent waveform measurements against the autocorrelation computed from experimental waveforms collected from an airborne receiver near Puerto Rico in 1999. It was demonstrated that this model provides a valid model for the complete autocorrelation of the complex waveform measurements, and that it correctly predicts the dependence of correlation time on satellite geometry and integration time. This model was furthermore extended to the case in which the receiver is in a satellite orbit for use in future studies. The correlation time of the ocean surface, at L-band wavelengths, could possibly limit the integration time to about 5 msec. The separation between independent samples collected from low earth orbit satellites would be of the order of 1 meter. ^

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