RESOLUTION, COMPUTATIONAL EFFICIENCY, AND COST ANALYSIS OF BACKPROPAGATION AND HOLOGRAPHIC ALGORITHMS FOR CONCEALED OBJECT DETECTION THROUGH VARIOUS MATERIALS USING MONOSTATIC MICROWAVE IMAGING

Abstract

Microwave imaging is a non-invasive and non-ionizing technique with significant applications in security screening, material characterization, and non-destructive testing. This study presents a monostatic microwave imaging system utilizing an ultra-wideband (UWB) horn antenna operating between 15 GHz and 22 GHz to detect concealed metallic targets behind various materials. The experimental setup employed a Vector Network Analyzer (VNA) to measure the backscattered S-parameters while an automated two-dimensional (2-D) scanner systematically moved the antenna across a 50 cm × 50 cm aperture with a 1 cm step size. Different concealment materials, including dry cloth, wet cloth, wood, granite, and tiles of varying thicknesses, were introduced to evaluate their effects on target visibility and image quality. Two image reconstruction techniques—Backpropagation Algorithm (BPA) and Holographic (FFT-based) processing—were applied to the measured data to assess their performance in terms of spatial resolution, computational efficiency, and noise suppression. The BPA demonstrated superior spatial resolution, effectively resolving the shapes and locations of the targets, while the FFT-based method provided faster image reconstruction, making it more suitable for large-scale imaging. These findings contribute to the development of high-resolution and computationally efficient microwave imaging techniques for concealed object detection in security and industrial applications.