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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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing 2024-05, Vol.18 (4), p.572-586
Main Authors: Valdez, Marc Andrew, Rezac, Jacob D., Wakin, Michael B., Gordon, Joshua A.
Format: Article
Language:English
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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.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2024.3424310