Analysis and simulation of the backscattering enhancement phenomenon from randomly distributed point scatterers

Ağar, Kartal Şahin
This thesis investigates analysis and simulation of the backscattering enhancement phenomenon from randomly distributed point scatterers. These point scatterers are randomly distributed within a cube or a sphere and then the backscattering enhancement phenomenon from both cubical and spherical distributions are examined throughout the thesis. The general characteristic differences between cubical and spherical distribution about the scattering phenomenon are observed. T-matrix method is used for analytic investigations of the backscattering enhancement and also a certain number of approximate formulas are obtained. As for Monte Carlo simulation method, it is used for simulated investigations of the backscattering enhancement. Some Monte Carlo simulations are prepared by using MATLAB programming language and verified by showing their confidence intervals. Both analytic and simulated investigations of the backscattering enhancement due to single and double scattering are analyzed; however, only simulated investigation of the backscattering enhancement due to multiple scattering are analyzed because of its computational complexity. The thesis traces differences between single scattering and multiple scattering from randomly distributed point scatterers. Effects of both incident field frequency and point scatterer density on the backscattering enhancement are indicated. The thesis seeks answers to questions such as which conditions cause the backscattering enhancement phenomenon from randomly distributed point scatterers, why we need to consider multiple scattering to examine the backscattering phenomenon and how we can discriminate the backscattering enhancement from the specular enhancement.


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Citation Formats
K. Ş. Ağar, “Analysis and simulation of the backscattering enhancement phenomenon from randomly distributed point scatterers,” M.S. - Master of Science, Middle East Technical University, 2007.