Aggregating descriptive regularization and Bayesian nonparametric spectral estimation approaches for enhanced radar imaging
DOI:
https://doi.org/10.3103/S0735272710040047Keywords:
radar/SAR imaging, Bayesian estimation, regularizationAbstract
In this paper, we address and discuss a novel look at the high-resolution array radar/SAR imaging as an ill-conditioned inverse spatial spectrum pattern (SSP) estimation problem. The system-oriented theoretical developments are addressed to as an aggregated descriptive regularization-Bayesian (DRB) method for radar/SAR image formation/reconstruction. We exemplify how this aggregated method leads to new robust adaptive computational techniques that enable one to derive efficient and consistent estimates of the SSP via unifying the Bayesian minimum risk nonparametric spectral estimation strategy with the maximum entropy randomized a priori image model and other projection-type regularization constraints imposed on the solution. The reported simulation results demonstrate the efficiency of the addressed DRB-related radar/SAR-oriented enhanced imaging techniques.
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