Subsurface detection based on enhanced SAR signatures using angular correlation function

Presents the simulation of synthetic aperture radar (SAR) images using angular correlation function (ACF) measurements for the subsurface detection of buried objects. The scenario consists of a perfectly conducting two-dimensional cylinder buried under a one-dimensional dielectric random rough surface. In order to account for the coherent interactions between buried cylinder and rough surface, the method of moments is used to solve the integral equations. Ultra-wideband radar backscattering fields at different angles are simulated from the integral equation solutions. By choosing a sub-portion of the frequency band and shifting its center frequency, two sets of SAR images of different incidence angles with different center frequencies are formed using the back-projection algorithm. The frequency-averaged ACF technique is then applied on the two sets of SAR images pixel by pixel to obtain the final ACF-SAR image. From the simulation, the authors observe a roughly 5 dB enhancement on the target to clutter ratio when the ACF-SAR images are compared with the conventional SAR images.