Sensing matrix prediction from aperture distribution fields in computational microwave imaging with coded-apertures

Conventional microwave imaging-based approaches can produce high quality image reconstructions. At the same time, these techniques typically suffer from increased hardware complexity, cost and slow data acquisition speeds. Although computational imaging (CI)-based systems have been developed as an alternative, they may demand significant computational power and time, especially in the calculation and the storage of the transfer function (or the sensing matrix) of the CI system. However, the previous method considers the scenario where the transmitter and receiver share the same set of aperture distribution fields. To address this challenge, this paper presents a new technique, where the sensing matrix is calculated directly from the aperture fields of the antennas in a CI system. Here, the transmitter and the receiver apertures can be different and they do not necessarily need to have the same field distributions. With the testing dataset, the average value of the normalized mean squared error (NMSE) is 0.0243. In addition, compared to the traditional method, the proposed network reduces the computation time for the sensing matrix by approximately 67%. The proposed network can predict the sensing matrix from two different sets of aperture distribution fields with high accuracy while significantly saving the computation time.