Microwave Stepped Frequency Head Imaging Using Compressive Sensing With Limited Number of Frequency Steps

Abstract

Compressive sensing (CS) can be used to recover sparse data (signal) from limited measurements by solving a constrained convex optimization problem. If this approach is applied on microwave stepped frequency imaging technique, the required number of frequency steps to get clear images can be significantly reduced resulting in simple systems with fast data acquisition and real time results. To that end, three different CS techniques are applied on head imaging systems aiming at the detection of brain injuries by utilizing the sparse characteristic of the correlated time domain scattered signals. The presented measured results using a head imaging system indicate that the time domain correlation signals are indeed sparse and thus can be recovered using a limited number of frequency steps. Those recovered signals are then used to successfully generate clear images that show brain injuries. A comparison between using the proposed and the traditional approaches using two quality metrics indicates superiority of the presented CS-based approach in not just the limited needed frequency steps, but also in the quality of the obtained images.