Modeling and simulation of the fused Bayesian-regularization method for remote sensing imagery with synthetic aperture arrays
DOI:
https://doi.org/10.1109/ICATT.2003.1239158Keywords:
Bayes methods, antenna arrays, radar antennas, radar imaging, remote sensing by radar, synthetic aperture radarAbstract
A new fused Bayesian regularization (FBR) method for enhanced remote sensing imaging based on a new concept of aggregated statistical-deterministic regularization was developed recently. In this study, we represent the results of modeling and extensive simulation of the FBR algorithms for enhanced reconstruction of the spatial spectrum patterns (SSP) of the point-type and spatially distributed wavefield sources as it is required for the remote sensing imagery with synthetic aperture arrays. The simulations were performed in the MATLAB computational environment for the family of the SAR imaging algorithms that employed different modifications of the FBR method. The presented results enable one to evaluate the operational performance of the FBR method that was not previously reported in the literature.References
Cutrona, L.G. Synthetic Aperture Radar. Radar Handbook, 2nd ed., M. I. Skolnik. MA : McGraw Hill, 1990.
Haykin, S.; Steinhardt, A. (eds.), Adaptive Radar Detection and Estimation. New York: Wiley, 1992.
Astola, J.; Kuosmanen, P. Fundamentals of Nonlinear Digital Filtering. Boca Raton, FL: CRC, 1997.
Henderson, F.M.; Lewis, A.V. (eds.). Principles and Applications of Imaging Radar, Manual of Remote Sensing, 3rd ed. New York : Willey, 1998, Vol. 3.
Shkvarko, Y.V. Estimation of wavefield power distribution in the remotely sensed environment: Bayesian maximum entropy approach. IEEE Trans. Signal Proc., Vol. 50, pp. 2333-2346, Sep. 2002.
Shkvarko, Y.V. Towards the Bayesian-Regularization Method for Enhanced SAR Imaging. Proc. of the SPIE Aero-Sense 2003 Int. Symp., Orlando, FL, USA, SPIE Proc., Vol. 5095, 2003.
Shkvarko, Y.V. Theoretical Aspects of Unifying Regularization and Bayesian Estimation Methods for Enhanced Imaging with Remotely Sensed Data. IEEE Trans. Geoscience and Remote Sensing, (to be published), 2003.
