2 September 2004 Feature association and occlusion model estimation for synthetic aperture radar
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We develop a radar-based automatic target recognition approach for partially occluded objects. The approach may be variously posed as an optimization problem in the phase history, scene reflectivity and feature domains. The latter consists of point scattering features estimated from the phase histories or corresponding images. We adopt simple occlusion models in which the physical scattering responses (isotropic scattering centers, attributed scatterers, etc.) can be occluded in any combination. The formulation supports the use of prior occlusion models (e.g., that occlusion is spatially correlated rather than randomly distributed). We introduce a physics-based noise covariance model for use in cost or objective functions. Occlusion model estimation is a combinatorial problem since the optimal subset of scatterers must be discovered from a potentially much larger set. Further, the number of occluded scatterers must be estimated as a part of the solution. We apply a genetic algorithm to solve the combinatorial problem, and we provide a simple demonstration example using synthetic data.
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Eugene M. Lavely, Eugene M. Lavely, Peter B. Weichman, Peter B. Weichman, } "Feature association and occlusion model estimation for synthetic aperture radar", Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery XI, (2 September 2004); doi: 10.1117/12.555516; https://doi.org/10.1117/12.555516

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