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Proceedings Paper

Near-field SAR imaging with dynamic metasurface antennas using an adapted range migration algorithm
Author(s): Aaron V. Diebold; Laura Pulido-Mancera; Timothy Sleasman; Michael Boyarsky; Mohammadreza F. Imani; David R. Smith
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Paper Abstract

Synthetic aperture radar (SAR) is a well-established approach for retrieving images with high resolution. How- ever, common hardware used for SAR systems is usually complex and costly, and can suffer from lengthy signal acquisition. In near-field imaging, such as through-wall-sensing and security screening, simpler and faster hardware can be found in the form of dynamic metasurface antennas (DMAs). These antennas consist of a waveguide-fed array of tunable metamaterial elements whose overall radiation patterns can be altered by DC signals. By sweeping through a set of tuning states, near-field imaging can be accomplished by multiplexing scene information into a collection of measurements, which are post-processed to retrieve scene information. While DMAs simplify hardware, the post-processing can become cumbersome, especially when DMAs are moving in a fashion similar to SAR. In this presentation, we address this problem by modifying the range migration algorithm (RMA) to be compatible with DMAs. To accommodate complex patterns generated by DMAs in the RMA, a pre-processing step is introduced to transform the measurements into an equivalent set corresponding to an effective multistatic configuration, for which specific forms of the algorithm have been derived. As we are operating in the near field of the antennas, some approximations made in the classical formulation of RMA may not be valid. In this paper, we examine the effect of one such approximation: the discarding of amplitude terms in the signal-target Fourier relationship. We demonstrate the adaptation of the RMA to near field imaging using a DMA as central hardware of a SAR system, and discuss the effects of this approximation on the resulting image quality.

Paper Details

Date Published: 14 May 2018
PDF: 8 pages
Proc. SPIE 10669, Computational Imaging III, 106690S (14 May 2018); doi: 10.1117/12.2305067
Show Author Affiliations
Aaron V. Diebold, Duke Univ. (United States)
Laura Pulido-Mancera, Duke Univ. (United States)
Timothy Sleasman, Duke Univ. (United States)
Michael Boyarsky, Duke Univ. (United States)
Mohammadreza F. Imani, Duke Univ. (United States)
David R. Smith, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 10669:
Computational Imaging III
Abhijit Mahalanobis; Amit Ashok; Lei Tian; Jonathan C. Petruccelli, Editor(s)

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