Multilayered Circular Dielectric Structure SAR Imaging Using Time-Reversal Compressed Sensing Algorithms Based on Nonuniform Measurement
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Circular synthetic aperture radar
compressed sensing (CS)
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In this letter, an algorithm for synthetic aperture radar (SAR) imaging of electrically small targets embedded in multilayered cylindrical geometries (e.g., pipes) using non-uniform measurement points is presented. In contrast to previous approaches, this algorithm is able to efficiently handle non-uniform points without exhibiting relevant side lobes in the image. For these purposes, the approach exploits a flexible time-reversal (TR) algorithm enhanced by compressed sensing (CS). The theoretical performance of the algorithm is studied in terms of the point spread function (PSF) and several images are presented using synthetic full-wave data from CST Microwave Studio. In addition, the approach is empirically validated by performing microwave imaging of a PVC pipe. The results demonstrated that the TR-CS algorithm provides an effective focusing technique for dense non-uniform measurement points, as well as for sparse non-uniform measurement points.
In this letter, an algorithm for synthetic aperture radar (SAR) imaging of electrically small targets embedded in multilayered cylindrical geometries (e.g., pipes) using non-uniform measurement points is presented. In contrast to previous approaches, this algorithm is able to efficiently handle non-uniform points without exhibiting relevant side lobes in the image. For these purposes, the approach exploits a flexible time-reversal (TR) algorithm enhanced by compressed sensing (CS). The theoretical performance of the algorithm is studied in terms of the point spread function (PSF) and several images are presented using synthetic full-wave data from CST Microwave Studio. In addition, the approach is empirically validated by performing microwave imaging of a PVC pipe. The results demonstrated that the TR-CS algorithm provides an effective focusing technique for dense non-uniform measurement points, as well as for sparse non-uniform measurement points.
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This work was supported in part by the Ministerio de Ciencia, Innovacinó y Universidades of Spain/FEDER under Project RTI2018-095825-B-I00, in part by the Gobierno del Principado de Asturias under Project GRUPINIDI-2018-000191, and in part by FPU under Grant FPU15/06431