Fusion of Bayesian Maximum Entropy Spectral Estimation and Variational Analysis Methods for Enhanced Radar Imaging
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Fecha
2007
Autores
Shkvarko, Yuriy
Villalón-Turrubiates, Iván E.
Vázquez-Bautista, René
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Editor
Springer
Resumen
Descripción
A new fused Bayesian maximum entropy–variational analysis (BMEVA) method for enhanced radar/synthetic aperture radar (SAR) imaging is addressed as required for high-resolution remote sensing (RS) imagery. The variational analysis (VA) paradigm is adapted via incorporating the image gradient flow norm preservation into the overall reconstruction problem to control the geometrical properties of the desired solution. The metrics structure in the corresponding image representation and solution spaces is adjusted to incorporate the VA image formalism and RS model-level considerations; in particular, system calibration data and total image gradient flow power constraints. The BMEVA method aggregates the image model and system-level considerations into the fused SSP reconstruction strategy providing a regularized balance between the noise suppression and gained spatial resolution with the VA-controlled geometrical properties of the resulting solution. The efficiency of the developed enhanced radar imaging approach is illustrated through the numerical simulations with the real-world SAR imagery.
Palabras clave
Radar/SAR Imaging, Bayesian Maximum Entropy-Variational Analys, Remote Sensing
Citación
Yuriy Shkvarko, René Vázquez-Bautista, Iván E. Villalón-Turrubiates, “Fusion of Bayesian Maximum Entropy Spectral Estimation and Variational Analysis Methods for Enhanced Radar Imaging”, in Advanced Concepts for Intelligent Vision Systems – Lecture Notes in Computer Science, J. Blanc-Talon et al., Ed. Alemania: Springer Berlin Heidelberg, 2007, pp. 109-120.