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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.17244 |
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Table of Contents:
- 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.