Abstract
In this paper, a novel microwave tomography (MWT) system that utilizes a phased-array transmitter antenna is presented. In contrast to contemporary MWT systems, the system presented herein consists of a single transmitter and multiple receivers. The single transmitter is a four-element array antenna; by varying the phase for each element in the array, the field distribution inside the imaging chamber varies, which produces a different set of measurements per phase configuration. Furthermore, the individual elements of the transmitting array antenna and the receiving antennas are of different types; the receiving antenna type is selected to maximize the coupling between the transmitter and the receiver. Due to the system’s complexity, the system is modeled using a neural network; the trained network is used as the forward solver for an inversion algorithm based on a Bayesian regularized Levenberg–Marquardt algorithm. The system along with the inversion algorithm is tested using several targets. Furthermore, the performance of the algorithm against various levels of experimental noise is analyzed and evaluated.
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