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Abstract:
This research effort will utilize a hierarchical dictionary-based approach for canonical shape classification within measured synthetic aperture radar (SAR) phase history data. This primary goal of this research is to develop an efficient framework for dictionary based SAR feature extraction using modi ed 3-D radar scattering models. Previous work in this area relies on maximum likelihood (ML) estimation and similar approaches to extract shapes using 2-D signal models. We include characterizations of shape model redundancies caused by similar shape scattering responses. Simulated SAR collection methods, including frequency, elevation aspect, and polarization diversities, are modeled to show reductions in inter-atom correlation. A molecule method is used to combine highly correlated atoms to support a basis pursuit (BP) method of feature identifcation. Finally, a Bayesian approach is used to determine a maximum a posteriori (MAP) estimate for each atom, leading to feature classi cation and parameter identifcation.
| Limitations: |
 APPROVED FOR PUBLIC RELEASE |
| Description: |
Master's thesis |
| Pages: |
221 |
| Report Date: |
22 Mar 2012 |
| Report Number: |
A032755 |
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