12 September 2003 Toward the Bayesian-regularization method for enhanced SAR imaging
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Abstract
In this paper, we propose to unify the Bayesian estimation strategy with the statistical regularization-based techniques for image reconstruction through developing the fused Bayesian-regularization (FBR)method for the high resolution estimation of the spatial spectrum pattern (SSP) of the wave field scattered from the probing surface. The problem is treated as it is required for enhanced radar imaging of the remotely sensed scenes via processing one sampled realization of the SAR trajectory signal. The derived optimal FBR estimator is a nonlinear solution-dependent (thus referred to as an adaptive) algorithm that also permits a concise robust simplication to the non-adaptive easy-to-implement imaging techniques. The optimal and robustified suboptimal SSP estimation algorithms imply formation of the second order sufficient statistics from the SAR trajectory data signals and their smoothing applying the window operators. The new formalism of such the sufficient statistics and windows explaining their adjustment to the metrics in a solution space, a priori nonparametric model of the desired SSP, its correlation properites and imposed regularization constraints is developed. The advantage in using the proposed method is demonstrated through simulations of enhancing the SAR images using a family of the robustified FBR-based imaging algorithms.
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Yuriy V. Shkvarko, "Toward the Bayesian-regularization method for enhanced SAR imaging", Proc. SPIE 5095, Algorithms for Synthetic Aperture Radar Imagery X, (12 September 2003); doi: 10.1117/12.485961; https://doi.org/10.1117/12.485961
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