|Title||Signal processing methods for ultra high resolution scatterometry|
This dissertation approaches high resolution scatterometry from a new perspective. Three related general topics are addressed: high resolution sigma 0 imaging, wind estimation from high resolution sigma0 images over the ocean, and high resolution wind estimation directly from the scatterometer measurements. Theories of each topic are developed, and previous approaches are generalized and formalized. Improved processing algorithms for these theories are developed, implemented for particular scatterometers, and analyzed. Specific results and contributions are noted below. The sigma0 imaging problem is approached as the inversion of a noisy aperture-filtered sampling operation–extending the current theory to deal explicitly with noise. A maximum aposteriori MAP) reconstruction estimator is developed to regularize the problem and deal appropriately with noise. The method is applied to the SeaWinds scatterometer and the Advanced Scatterometer ASCAT). The MAP approach produces high resolution sigma 0 images without introducing the ad-hoc processing steps employed in previous methods. An ultra high resolution UHR) wind product has been previously developed and shown to produce valuable high resolution information, but the theory has not been formalized. This dissertation develops the UHR sampling model and noise model, and explicitly states the implicit assumptions involved. Improved UHR wind retrieval methods are also developed. The developments in the sigma0 imaging problem are extended to deal with the nonlinearities involved in wind field estimation. A MAP wind field reconstruction estimator is developed and implemented for the SeaWinds scatterometer. MAP wind reconstruction produces a wind field estimate that is consistent with the conventional product, but with higher resolution. The MAP reconstruction estimates have a resolution similar to the UHR estimates, but with less noise. A hurricane wind model is applied to obtain an informative prior used in MAP estimation, which reduces noise and ameliorates ambiguity selection and rain contamination. Keywords: scatterometry, image reconstruction, irregular sampling, aperture-filtered sampling, wind, ambiguity selection, hurricane, maximum aposteriori estimation, inverse problems
How to get this paper's electronic documents?
1, Click the "Buy Now" button to complete the online payment
2, Download the paper's electronic document from the successful payment return page/Or the system will send this paper's electronic document to your E-Mail within 24 hours
|Favorite||ADD TO FAVORITE|
Perhaps You will be interested in these papers
2012-03-13 Data fusion in scientific data mining