Microwave Computational Imaging-Based Near-Field Measurement Method

A near-field (NF) measurement method stemming from the microwave computational imaging (MCI) concept is proposed in this letter. In an MCI system, the sensing matrix comprises the radiated fields of both the transmitter and receiver. We demonstrate that, with apriori knowledge of an antenna radiating quasi-random patterns tailored for MCI, the sensing matrix allows for the calculation of the NF distribution of an antenna-under-test (AUT). The proposed technique follows a two-step process. First, the sensing matrix of an MCI system with fixed calibration targets is directly predicted from the back-scattered data by using a trained Pix2pix conditional generative adversarial network (Pix2pix cGAN). Then, with the assistance of the Pix2pix cGAN and MCI-oriented antenna's NF distribution, the NF distribution of the AUT can be retrieved, avoiding time-consuming probe-based NF measurements. The proposed method is validated using various types of antennas exhibiting different polarization states as the AUTs through full-wave simulations.