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Remote Sens. 2018, 10(12), 1960; https://doi.org/10.3390/rs10121960

Compressive Sensing for Ground Based Synthetic Aperture Radar

Department of Information Engineering, University of Florence, via Santa Marta, 3, 50139 Firenze, Italy
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Received: 10 October 2018 / Revised: 30 November 2018 / Accepted: 2 December 2018 / Published: 5 December 2018
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Abstract

Compressive sensing (CS) is a recent technique that promises to dramatically speed up the radar acquisition. Previous works have already tested CS for ground-based synthetic aperture radar (GBSAR) performing preliminary simulations or carrying out measurements in controlled environments. The aim of this article is a systematic study on the effective applicability of CS for GBSAR with data acquired in real scenarios: an urban environment (a seven-storey building), an open-pit mine, and a natural slope (a glacier in the Italian Alps). The authors tested the most popular sets of orthogonal functions (the so-called ‘basis’) and three different recovery methods (l1-minimization, l2-minimization, orthogonal pursuit matching). They found that Haar wavelets as orthogonal basis is a reasonable choice in most scenarios. Furthermore, they found that, for any tested basis and recovery method, the quality of images is very poor with less than 30% of data. They also found that the peak signal–noise ratio (PSNR) of the recovered images increases linearly of 2.4 dB for each 10% increase of data. View Full-Text
Keywords: compressive sensing; ground based synthetic aperture radar; radar; synthetic aperture radar compressive sensing; ground based synthetic aperture radar; radar; synthetic aperture radar
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Pieraccini, M.; Rojhani, N.; Miccinesi, L. Compressive Sensing for Ground Based Synthetic Aperture Radar. Remote Sens. 2018, 10, 1960.

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