Multi-Frequency Spherical Near-Field Antenna Measurements Using Compressive Sensing
We propose compressive sensing approaches for broadband spherical near-field measurements that reduce measurement demands beyond what is achievable using conventional single-frequency compressive sensing. Our approaches use two different compressive signal models-sparsity-based and low-rank-based-wh...
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Published in: | IEEE journal of selected topics in signal processing 2024-05, Vol.18 (4), p.572-586 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | We propose compressive sensing approaches for broadband spherical near-field measurements that reduce measurement demands beyond what is achievable using conventional single-frequency compressive sensing. Our approaches use two different compressive signal models-sparsity-based and low-rank-based-whose viability we establish using a simulated standard gain horn antenna. Under mild assumptions on the device being tested, we prove that sparsity-based broadband compressive sensing provides significant measurement number reductions over single-frequency compressive sensing. We find that our proposed low-rank model also provides an effective means of achieving broadband compressive sensing, using numerical experiments, with performance on par with the best broadband sparsity-based method. Exemplifying these best-case results, even in the presence of measurement noise, the methods we propose can achieve relative errors of −40 dB using about 1/4 of the measurements required for conventional sampling. This is equivalent to about 1/2 sample per unknown, whereas traditional spherical near-field measurements require a minimum of roughly 2 measurements per unknown. |
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ISSN: | 1932-4553 1941-0484 |
DOI: | 10.1109/JSTSP.2024.3424310 |