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 January 17, 2025)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Multi-frequency Antenna Metrology with Sparse Measurements
Published
Author(s)
, Angela Folz, Michael Wakin,
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
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created July 10, 2024, Updated December 10, 2024