Feature estimation performance using a two-dimensional parametric model of radar scattering

As attributed scattering center model exploits scattering phenomenology that is not accessed through traditional SAR image formation. Frequency, aspect, and polarization dependent scattering behaviors are jointly processed to provide a concise, descriptive, high resolution analysis of regions of interest. Used in conjunction with other features such as shadows, context, and image texture, attributed scattering center features hold promise for both feature-based and model-based automatic target recognition systems. In this conference paper, we present a parametric model for radar scattering as a function of frequency and aspect angle: the model is suggested by high-frequency monostatic far-field scattering solutions provided by the geometrical theory of diffraction and physical optics. The scattering model is used for analysis of synthetic aperture radar data. The estimated parameters provide a concise, physically relevant description of measured scattering for use in target recognition, data compression and scattering studies. The scattering model may be fit to either complex-valued imagery or to radar phase history data using a nonlinear least-squares estimator. Statistical analysis of the scattering model serves to characterize uncertainty of estimated scattering parameters. Feature estimation performance bounds are evaluated for X-band. K-band, and ultra wideband synthetic aperture radar scenarios.