2011
DOI: 10.1109/tim.2011.2169177
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Optimum Two-Dimensional Uniform Spatial Sampling for Microwave SAR-Based NDE Imaging Systems

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Cited by 89 publications
(46 citation statements)
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“…In the SAR images, white indicated the presence of a strong return where black indicated the absence of a return. The SAR algorithm also subtracted the spatial average of all the measurement points [14]. The average was approximately the value of the metal plate below the sample, because the overall scan area was much greater than the size of the targets in the sample.…”
Section: B Sample 1: Targets On Foam Postsmentioning
confidence: 99%
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“…In the SAR images, white indicated the presence of a strong return where black indicated the absence of a return. The SAR algorithm also subtracted the spatial average of all the measurement points [14]. The average was approximately the value of the metal plate below the sample, because the overall scan area was much greater than the size of the targets in the sample.…”
Section: B Sample 1: Targets On Foam Postsmentioning
confidence: 99%
“…For NDT&E applications in particular, synthetic aperture radar (SAR) imaging algorithms (based on backpropagation methods), capable of producing high spatial resolution images of objects and structures, have been extensively incorporated into a number of imaging systems including those with real-time image production capabilities [14]- [17]. To produce a SAR image, commonly one needs to measure vector (magnitude and phase) scattered field distribution over a known 1-D or 2-D grid.…”
Section: Introductionmentioning
confidence: 99%
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“…Then, a forward SAR transform computes the high-resolution, uniformly-spaced volumetric image s(x, y, z) from F(k x , k y , ω) by the NUFFT-based SAR algorithm [9][10][11] s(x, y, z) = F −1…”
Section: Cs Algorithms For 3d Sarmentioning
confidence: 99%
“…Although the NUFFT-based SAR algorithms have been shown to achieve higher accuracy with slightly higher computational complexity than the Stolt interpolation [9][10][11][12], we discover that utilising both NUFFT and inverse NUFFT in each CS iteration results in significant errors because of an inherent truncation window implied in the inverse NUFFT of the forward SAR. If not compensated, the error is accumulated via NUFFT in the R-SAR in each iteration of the CS, and it results in non-convergence of the CS algorithm for reconstruction of high resolution volumetric images.…”
Section: Introductionmentioning
confidence: 98%