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An adaptive scanning algorithm is proposed for reducing scanning time associated with generating synthetic aperture radar (SAR) images, for nondestructive evaluation (NDE) applications, while preserving optimal cross-range resolution and image quality. The proposed method is based on the fact that optimum SAR cross-range resolution is target depth, synthetic aperture dimension, and scanning step size dependent. Hence, in the proposed method, the target properties, such as position and depth, are first estimated using an initial coarse scan of the scene of interest. Then, a localized dense scan is performed for obtaining optimal spatial resolution and image quality. During this procedure, the synthetic aperture dimension of the localized dense scan is adaptively increased (optimized) by reducing pixel-by-pixel image root-mean-square (rms) intensity difference between the images produced before and after each increment increase in the synthetic aperture dimension of the localized dense scan. At the same time, scanning step size is determined in a manner, which is related to the target depth in order to reduce scanning time and to insure obtaining (close to) optimal spatial sampling. Results of several illustrative measurements are provided by which the efficacy of the proposed method is demonstrated. The proposed algorithm, which mainly applies for imaging a localized flaw or a strong-scattering target in a relatively homogenous environment, is common in NDE applications and can be implemented using an automated and adaptive process. [ABSTRACT FROM AUTHOR]

Detecting covered cracks in metals is critical in many applications. In this article, a single dual-polarized circular aperture antenna and synthetic aperture imaging are devised to detect arbitrarily oriented cracks covered under thick dielectric layer. The devised monostatic quad-polarized system facilitates measurements of the reflection (copolarized) and transmission (cross-polarized) parameters which form the basis for reliable detection. Through analysis and measurements involving dielectric covers with thicknesses ranging from 4.25 to 12.7 mm, it will be demonstrated that the background clutter and the effect of the cover are largely suppressed in the images formed using the cross-polarized parameter. On the other hand, it will be highlighted that the crack could be actually masked by the clutter in the image formed using the reflection measurements commonly used for crack detection. [ABSTRACT FROM AUTHOR]

In this study, we propose a synthetic aperture imaging technique for near-field scatterers mutually coupled with an antenna array, such as supporting structures, fixtures of the antenna, and a radome enclosing the antenna. Mutual coupling is a phenomenon, in which the scattered and reradiated waves from the array elements and the near-field scatterers are received by the array elements, and it is known to cause undesired effects in array signal processing, such as direction-of-arrival estimation. Therefore, we need to design the antenna array with the goal of reducing the unwanted scattered and reradiated waves as much as possible, and we require a method to evaluate the scattering intensity for validation of the antenna design. For this purpose, we utilize the bistatic synthetic aperture radar measurement, whereby we mount the receiving antenna array under test on a turntable for azimuth rotation and irradiate this antenna with a fixed transmitting antenna. The measured signals are synthesized to image an area that contains both the array elements and scatters of interest. Because the spatial separations between the array elements and the scatterers are small, the direct waves from the transmitting antenna to the receiving elements result in serious artifacts in the reconstructed image. To suppress this effect, we apply a direct wave filter as a preprocessing method for image reconstruction. We demonstrate the proposed imaging scheme via data sets of numerical electromagnetic simulations and anechoic chamber measurements. [ABSTRACT FROM AUTHOR]

Microwave and millimeter wave synthetic aperture radar (SAR) imaging techniques are commonly used to generate high-resolution qualitative images showing the reflectivity (or complex permittivity) contrast in a sample-under-test (SUT). These techniques have been successfully applied for many diverse applications. However, these images lack quantitative information about the SUT, namely; its complex permittivity distribution. In this article, a novel method to extract the spatial distribution of complex permittivity from SAR images is proposed. The principle of the proposed method is outlined, then verified by a series of electromagnetic simulations, demonstrating that the proposed method can accurately extract the complex permittivity. Additionally, it is shown that the results are robust with respect to most critical parameters of the SAR imaging technique. Subsequently, measurements were performed to verify the efficacy of the proposed method on two SUTs with different complex permittivities, corroborating the utility of the proposed method. Moreover, the measurement results of one of the SUTs showed that the proposed method is capable of detecting local inhomogeneities with a relatively high spatial resolution. Then, an SUT with artificial defect was prepared to investigate this capability. Compared to quantitative methods based on inverse scattering techniques, this proposed method requires much less computational resources and has the potential for becoming an effective material characterization technique in the nondestructive evaluation (NDE) field. [ABSTRACT FROM AUTHOR]

Multistatic millimeter-wave imaging structures are superior to their monostatic counterparts for imaging natural objects of sudden profile variations. Multistatic image reconstruction is conventionally performed via synthetic aperture radar (SAR)-based methods which, in spite of their high accuracy, are computationally burdensome. On the other side, the Fourier-based image reconstruction in multistatic systems also faces few challenges including multidimensional interpolation, a plane-wave approximation of spherical waves, and $k$ -space partitioning. This paper presents a fast implementation of the SAR-based image reconstruction method in case of 1-D and 2-D multistatic arrays. The proposed implementation is Fourier based across the uniform direction in $k$ -space and SAR based along the nonuniform direction. Both methods are fully parallelizable and are arranged into a vector format to maximize memory usage and minimize the computational time. The extensive validation and benchmarking have been performed with both simulation and experimental data, which proves a $256\times $ improvement in the reconstruction time for the worst case scenario compared to that of in SAR-based methods with the same image quality. Furthermore, the reconstructed image performance is around $10\times $ better than the most recent Fourier-based reconstruction method, in terms of root-mean-square error metric, and with $28\times $ less computational time. [ABSTRACT FROM AUTHOR]