  We present a deep neural network-enabled method to accelerate near-field (NF)
antenna measurement. We develop a Near-field Super-resolution Network (NFS-Net)
to reconstruct significantly undersampled near-field data as high-resolution
data, which considerably reduces the number of sampling points required for NF
measurement and thus improves measurement efficiency. The high-resolution
near-field data reconstructed by the network is further processed by a
near-field-to-far-field (NF2FF) transformation to obtain far-field antenna
radiation patterns. Our experiments demonstrate that the NFS-Net exhibits both
accuracy and generalizability in restoring high-resolution near-field 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.
