Compressive Sensing for Ground Based Synthetic Aperture Radar
AbstractCompressive 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
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Pieraccini, M.; Rojhani, N.; Miccinesi, L. Compressive Sensing for Ground Based Synthetic Aperture Radar. Remote Sens. 2018, 10, 1960.
Pieraccini M, Rojhani N, Miccinesi L. Compressive Sensing for Ground Based Synthetic Aperture Radar. Remote Sensing. 2018; 10(12):1960.Chicago/Turabian Style
Pieraccini, Massimiliano; Rojhani, Neda; Miccinesi, Lapo. 2018. "Compressive Sensing for Ground Based Synthetic Aperture Radar." Remote Sens. 10, no. 12: 1960.
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