Prior knowledge in synthetic-aperture radar processing

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Abstract
The authors briefly review the role of models as a means of encoding prior knowledge with which to interpret data. They then examine the specific case of synthetic-aperture radar (SAR) images. They review the current state of SAR terrain clutter models, and their role in target detection. They present numerical results which demonstrate the consistency of a correlated gamma-distributed surface cross section model with SAR terrain data. They then review the theory of target super-resolution by the use of the singular-value decomposition (SVD). They emphasise the need to generalise the basic SVD technique in order to achieve success with SAR target data. Furthermore they demonstrate that the general SVD technique is a special case of a Bayesian reconstruction scheme which they interpret in terms of Shannon information theory. Numerical super-resolution results from simulated SAR data are presented.

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