Publication | Closed Access
On the superresolution identification of observables from swept-frequency scattering data
53
Citations
30
References
1997
Year
Spectral TheoryEngineeringSuperresolution IdentificationSpectrum EstimationMulti-resolution MethodImaging RadarCone DiffractionComputational ImagingRadar Signal ProcessingComputational ElectromagneticsSynthetic Aperture RadarSuperresolution ProcessingInverse Scattering TransformsInverse ProblemsSignal ProcessingRadarRadar ScatteringWave ScatteringHigh-frequency ApproximationRadar Image ProcessingMatrix-pencil Algorithm
A superresolution signal processing algorithm is used for the identification of wavefronts from the fields scattered from several canonical targets. Particular wave objects that are examined are single and multiple edge diffraction, scattering from flat and curved surfaces, cone diffraction, and creeping waves. The scattering data are computed numerically via the method of moments (MoM) and are processed using a modified matrix-pencil algorithm. General properties of superresolution processing of such data-independent of the particular algorithm used-are assessed through an examination of the Cramer-Rao (C-R) bounds for basic scattering scenarios.
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