In this paper, the problem of scattering and radiation in the presence of a material half-space is solved using the Transient spectral domain method (TSDM). The TSDM is a general theoretical approach for exact solution to the time-dependent sources radiation in the presence of stratified media. A source used is an electrically short magnetic dipole with impulse current distribution in time domain. It is located in air region in the vicinity of lossy half-space. The method requires the proper spatial Fourier transform for theoretical formulation of the problem. The new closed-form expressions is achieved in terms of spectral domain variable and time. Also a number of special cases are presented for verification of this procedure.

This study is concerned with the development of a model to describe microwave emission from dry snow cover. The model is based on the radiative transfer and the strong fluctuation theory. In the model, a spherical symmetric correlation function with an exponential form is used to describe the random permittivity fluctuations. The phase matrix and extinction coefficient of snowpack for a spherical symmetric correlation function are obtained by employing the strong fluctuation theory. The vector radiative transfer equation for a layer of a random medium is solved by using Gaussian quadrature and eigen analysis. Comparisons with brightness temperature data at 5, 10.7, 18, 37 GHz are made. It is shown that the model fits the experimental data by using physical parameters of the dry snow as obtained from ground truth measurements.

A technique that allows to locate defective elements in planar arrays by using some samples of the degraded far-field power pattern is described. This approach uses genetic algorithms to minimize the square of the difference between the far-field power pattern obtained for a given configuration of failed elements and the measured one. The method also allows to detect defective elements that don't fail completely

An adaptive wavelet packet transform (AWPT) algorithm is proposed to process synthetic aperture radar (SAR) imagery and remove background clutters from target images. Since target features are more efficiently represented using the wavelet packet bases, higher signal-to-clutter ratios (SCR) can be achieved in the wavelet transform domain. Consequently, clutters can be more effectively separated from the desired target features in the transform domain than in the original SAR domain. The processed results based on the MSTAR data set demonstrate the effectiveness of this algorithm for SAR clutter reduction.