We present a parametric model for radar scattering as a function of frequency and aspect angle. The model is used for analysis of synthetic aperture radar measurements. 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 and an image domain estimation algorithm are applied to two measured data examples.
Early time returns affect the estimation of complex natural resonance (CNR) frequencies associated with a target. This is especially true when there is a small separation between the early time returns and the late time response of the target and the CNRs are low Q mechanisms. A good example of this scenario is antipersonnel mines. In this situation, it helps to remove the early time returns from the total scattered field. A new technique to accomplish this is presented here. Using some numerical data and some experimental data, it is demonstrated that this technique is very effective in removing the early time returns. The modified scattered field data then yields better estimates of CNR frequencies.
In this paper, a signal processing technique is developed to reduce clutter due to ground bounce in ground penetrating radar (GPR) measurements. This technique is especially useful when a GPR is used to detect subsurface antipersonnel mines. The GPR clutter is modeled using a simple parametric model. Buried mine and clutter contributions are separated through a pair of coupled iterative procedures. The algorithm outperforms existing clutter reduction approaches and also yields target features that are useful for detection and identification of these mines. The proposed technique effectively reduces clutter resulting in a significant decrease in false alarm rates.
An 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 featurebased and model-based automatic target recognition systems. In this conference paper. we 1)Ise11t a p?L1a11(tr1( 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.
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