Green's function for electromagnetic scattering from a random rough surface

1974 ◽  
Vol 15 (3) ◽  
pp. 283-288 ◽  
Author(s):  
John A. DeSanto
2018 ◽  
pp. 197-250
Author(s):  
Andrei V. Lavrinenko ◽  
Jesper Lægsgaard ◽  
Niels Gregersen ◽  
Frank Schmidt ◽  
Thomas Søndergaard

1994 ◽  
Vol 72 (1-2) ◽  
pp. 20-29 ◽  
Author(s):  
Saba Mudaliar

The problem of wave propagation and scattering over a randomly rough surface is considered from a multiple-scattering point of view. Assuming that the magnitude of irregularities are small an approximate boundary condition is specified. This enables us to derive an integral equation for the dyadic Green's function (DGF) in terms of the known unperturbed DGF. Successive iteration of this integral equation yields the Neumann series. On averaging this and using a diagram method the Dyson equation is derived. Bilocal approximation to the mass operator leads to an integral equation whose kernel is of the convolution type. This is then readily solved and the results are presented in a simplified and useful form. It is observed that the coherent reflection coefficients involve infinite series of multiple scattering. Two special cases are considered the results of which are in agreement with our expectations.


2014 ◽  
Vol 989-994 ◽  
pp. 3777-3781
Author(s):  
Lin Li ◽  
Wen Chao Zheng

In this paper, an improved complex image method to derive closed form periodic Green’s function for problem of scattering from perfectly conducting periodic surfaces is considered. The complex image technique represents a close-form periodic Green’s function consisting of a finite series of real sources and two finite series of complex sources with complex locations and amplitudes. Then the integral equation with the complex images periodic Green’s function in the kernel is solved by the method of moments. Results and computational times obtained by this improved complex image method and the previous one are compared with those obtained by the spectral Kummer-Poisson’s method. Results show that the improved complex image method is more accurately than the previous one.


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