The Use of Compressive Sensing to Reconstruct Radiation Characteristics of Wide-Band Antennas from Sparse Measurements

Report No. ARL-TR-7328
Authors: Patrick Debroux and Berenice Verdin
Date/Pages: June 2015; 28 pages
Abstract: Characterization measurements of wide-band antennas can be time intensive and expensive as many data points are required both in the angular and the frequency dimensions. Compressive sensing is proposed to reconstruct the radiation patterns and frequency behavior of antennas from a sparse and random data set of measurements. Parallel compressive sensing is used to reconstruct the desired 2-dimensional (2-D) far-field, radiation-frequency pattern from a randomly distributed, limited set of measurements. Three antenna models were constructed—a pyramidal horn, a Vivaldi, and a bicone—and their 2-D far-field radiation patterns were modeled over a large frequency range using a high-frequency structural simulator. Analyses of uniform- versus nonuniform-pattern reconstruction, of transform function used, and of minimum randomly distributed measurements needed to reconstruct the antennas radiation characteristics showed the radiation-frequency patterns of the 3 antennas are better reconstructed with the discrete Fourier transform in the angular dimension and with the discrete cosine transform in the frequency dimension. Further, little difference was found in the radiation-frequency patterns reconstruction using uniform and nonuniform randomly distributed samples even though the pattern error manifests itself differently. The radiation-frequency patterns of the 3 antennas were adequately reconstructed using as little as 30% of calculated points.
Distribution: Approved for public release
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Last Update / Reviewed: June 1, 2015