2D HRR Radar Data Modeling and Processing
Multidimensional Systems and Signal Processing
Detection and Analysis of Anisotropic Scattering in SAR Data
Multidimensional Systems and Signal Processing
Adaptive polarimetry design for a target in compound-Gaussian clutter
Signal Processing
Classifying transformation-variant attributed point patterns
Pattern Recognition
Ballistic missile detection via micro-Doppler frequency estimation from radar return
Digital Signal Processing
Theory of Waveform-Diverse Moving-Target Spotlight Synthetic-Aperture Radar
SIAM Journal on Imaging Sciences
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High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behaviour predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides high-resolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a least-squares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter