A Near-Field Super-Resolution Network for Accelerating Antenna Characterization

We present a deep-neural-network-enabled method to accelerate near-field (NF) antenna measurement. Coupled with a large synthetic dataset of antenna field maps and novel magnitude and phase loss functions, we develop an NF super-resolution network (NFS-Net) to reconstruct significantly undersampled NF data into high-resolution data. This approach considerably reduces the number of sampling points required for NF measurement and thus improves measurement efficiency. The high-resolution NF data reconstructed by the network are further processed by a near-field-to-far-field (NF2FF) transformation to obtain far-field (FF) antenna radiation patterns. Our experiments demonstrate that the NFS-Net exhibits both accuracy and generalizability in restoring high-resolution NF data from low-resolution input. The NF measurement workflow that combines the NFS-Net and the NF2FF algorithm enables accurate radiation pattern characterization with only 11% of the Nyquist rate samples. Though the experiments in this study are conducted on a planar setup with a uniform grid, the proposed method can serve as a universal strategy to accelerate measurements under different setups and conditions.