Application of the CBFM-ACA approach to the scattering from natural media consisting of randomly distributed discrete scatterers
Résumé
In recent years, the characterization of ElectroMagnetic (EM) scattering from randomly distributed discrete particles has attracted much
interest owing to its extensive applications to radar remote of natural media such as snow and ice [1, 2]. A discrete random medium can
be sparse or dense according to its particle concentration. In a sparse medium, the particle concentration is quite low such that particle
positions are uncorrelated. That way, the response of a sparse medium can be reduced to the sum of particle individual responses. The sparse
medium response is then only affected by the individual properties of the scatterers such as sizes, shapes, and permittivities. However, in a
dense medium, the particles are packed closely together and the correlation of particle positions could significantly affect the response of the
medium. Therefore, multiple scattering effects must be included in the analysis of a radar signal interaction with the medium. In this paper,
a rigorous scattering model based on a Monte Carlo simulation of scattering from randomly distributed discrete spheroids is developed. The
electromagnetic scattering problem is formulated using the volumetric Electric Field Integral Equation (vEFIE) that is afterward discretized
by means of the Method of Moments (MoM) with dipole basis functions. The MoM matrix equation is then solved by the Characteristic
Basis Functions Method (CBFM) [3] which can deal with large number of unknowns (large number of particles). The CBFM is based on
excitation-independent Characteristic Basis Functions (CBFs) defined on macro domains (blocks) and includes the mutual interaction effects
rigorously and systematically. The CBFM is also hybridized with the adaptive cross approximation (ACA) algorithm [4] in order to construct
a reduced matrix equation in a time-efficient manner.