A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data

Abstract

An approach based on the Green function and the Born approximation is used for impulsive radio ultra-wideband (UWB) microwave imaging, in which a permittivity map of the illuminated scenario is estimated using the scattered fields measured at several positions. Two algorithms are applied to this model and compared: the first one solves the inversion problem using a linear operator. The second one is based on the Bayesian compressive sensing (BCS) technique, where the sparseness of the contrast function is introduced as extit{a priori} knowledge in order to improve the inverse mapping. In order to compare both methods, measurements in real scenarios are taken using an UWB radar prototype. The results with real measurements illustrate that, for the considered scenarios, the BCS imaging algorithm has a better performance in terms of range and cross-range resolution allowing object detection and shape reconstruction, with a reduced computational burden, and fewer space and frequency measurements, as compared to the linear operator.