Multi-frequency Antenna Metrology with Sparse Measurements

Published

July 10, 2024

Author(s)

Marc Valdez, Angela Folz, Michael Wakin, Joshua Gordon

Abstract

We examine different approaches for compressive sensing in broadband antenna radiation pattern measurements, a common tool in antenna metrology. We focus on spherical near-field measurements and seek improved reductions in the needed number of measurements by extending conventional single-frequency compressive sensing to the multi-frequency domain. We compare different signal models, sparsity-based and low-rank-based, for a simulated standard gain horn antenna, establishing the models' ability for high rates of compression. We prove that, under mild assumptions on the device being tested, sparsity-based compressive sensing in the multi-frequency context provides measurement number reductions over single-frequency compressive sensing. Using numerical experiments, we also find that a low-rank model provides an effective means of achieving compressive sensing. In particular, low-rank-based compressive sensing can have performance on par with the best multi-frequency sparsity-based compressive sensing method, though, without a corresponding theoretical guarantee. To exemplify these best-case compressive sensing approaches, in the presence of measurement noise, the best methods we test can achieve results accurate to -40 dB using only about 1/10 of the measurements of conventional sampling. Moreover, the best results improve accuracy to -60 dB at 1/5 of the total conventional samples and continue to improve beyond -80 dB while still requiring fewer samples than one needs in non-compressive approaches.

Citation

IEEE Journal of Selected Topics in Signal Processing

Volume

18

Issue

4

Pub Type

Journals

Download Paper

https://doi.org/10.1109/JSTSP.2024.3424310

Local Download

Citation

Valdez, M. , Folz, A. , Wakin, M. and Gordon, J. (2024), Multi-frequency Antenna Metrology with Sparse Measurements, IEEE Journal of Selected Topics in Signal Processing, [online], https://doi.org/10.1109/JSTSP.2024.3424310, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956825 (Accessed October 17, 2025)