Adaptive algorithm-based fused Bayesian maximum entropy-variational analysis methods for enhanced radar imaging

Authors:
R. F. Vázquez-Bautista;L. J. Morales-Mendoza;R. Ortega-Almanza;A. Blanco-Ortega
Affiliations:
FIEC, Universidad Veracruzana, Poza Rica, Ver;FIEC, Universidad Veracruzana, Poza Rica, Ver;FIEC, Universidad Veracruzana, Poza Rica, Ver;CENIDET, Ingeniería Mecatrónica, Cuernavaca, Morelos, C.P.
Venue:
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Year:
2010

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Abstract

In this paper we address an adaptive computational algorithm to improve the Bayesian maximum entropy-variational analysis (BMEVA) performance for high resolution radar imaging and denoising. Furthermore, the variational analysis (VA) approach is aggregated by imposing the metrics structures in the corresponding signal spaces. Then, the formalism for combining the Bayesian maximum entropy strategy with the VA paradigm is presented. Finally, the image enhancement and denoising benefits produced by the proposed Adaptive Bayesian maximum entropy-variational analysis (ABMEVA) method are showed via simulations with real-world radar scene