Learning Assisted Sensing Matrix Calculation in Computational Microwave Imaging

While computational microwave imaging (CMI) addresses several limitations of conventional microwave imaging techniques, such as hardware complexity, it is still constrained by substantial computational resources required for image re-construction. This paper presents a convolutional neural network (CNN)-based approach to enhance the computational efficiency of CMI. The proposed network directly computes the transfer function, or sensing matrix, from the aperture fields of antennas within a CMI system. To improve information extraction, convolutional block attention modules (CBAMs) are integrated into the architecture. Numerical results on a testing dataset demonstrate an average normalized mean squared error (NMSE) of 0.054. Compared to conventional methods, the proposed network reduces computation time by 69% in generating the sensing matrix. Overall, the network generates the sensing matrix from two different sets of aperture field distributions with high precision, achieving considerable computational savings for CMI applications.