The aim of this paper is to present a freehand scanning system with a compact mm-wave radar. In order to achieve high-resolution images, the system exploits the free movements of the radar to create a synthetic aperture. However, in contrast to conventional synthetic aperture radar (SAR), in which canonical acquisition surfaces (e.g., planes or cylinders) are used, the system allows for a given tolerance compatible with real handmade trajectories. Moreover, different techniques are studied to compensate for the impact of irregular sampling to reduce the artifacts in the image. As a result, real-time scanning can be readily performed even by inexperienced users. The scanning system, comprising a commercial motion capture system and an mm-wave module, can be easily deployed and calibrated. Several results involving different objects are shown to illustrate the performance of the system. INDEX TERMS SAR imaging, mm-wave imaging, FMCW radar, real-time imaging, freehand scanner.
Abstract-The Sources Reconstruction Method (SRM) is a noninvasive technique for, among other applications, antenna characterization. The SRM is based on obtaining a distribution of equivalent currents that radiate the same field as the antenna under test. The computation of these currents requires solving a linear system, usually ill-posed, that may be very computationally demanding for commercial antennas. Graphics Processing Units (GPUs) are an interesting hardware choice for solving compute-bound problems that are prone to parallelism. In this paper, we present an implementation on GPUs of the SRM applied to antenna characterization that is based on a compute-bound algorithm with a high degree of parallelism. The GPU implementation introduced in this work provides a dramatic reduction on the time cost compared to our CPU implementation and, in addition, keeps the low-memory footprint of the latter. For the sake of illustration, the equivalent currents are obtained on a base station antenna array and a helix antenna working at practical frequencies. Quasi real-time results are obtained on a desktop workstation.
This paper presents the first demonstrator of a portable, multi-view, high-resolution, three-dimensional (3D) and real-time microwave imaging system. The system is based on a recently-developed real-time 3D microwave camera, which performs quasi-monostatic acquisitions, equipped with an optical depth camera providing target surface profile information. Additionally, the entire system can be arbitrarily moved along the target performing microwave and depth camera synchronized acquisitions from different views with a twofold purpose, namely; a) enabling a coverage area much larger than that possible with a static imaging system, and b) allowing for incorporation of several tilt angles (or views) to enhance capturing specular reflection imaging data to improve the overall image quality. At each scanning position, the imaging data from the microwave camera are processed to build a local 3D microwave image. The information is then merged, using recently-proposed techniques for multi-view synthetic aperture imaging, to compose the global image. The synchronized optical camera depth acquisitions enable tracking the entire imager movements so that the position and attitude are known. Moreover, the data acquired by the depth camera are also use to build a complementary 3D outer surface profile model of the target, producing a combined and realistic image of the internal and external geometries of the target. Finally, the performance of the combined system is evaluated using several examples related to hidden contraband covered by clothing (i.e., people screening).
The Fast Multipole Method (FMM) is specially suitable for applications in which it is necessary to predict the acoustic scattering, e.g., aircraft noise control. This accelerated iterative method has two main parts, far interactions and near interactions. Near interactions are computationally intensive and they fit properly in the Single Instruction Multiple Threads paradigm. In this work, we present a heterogeneous parallel solution in which the near interactions are computed using Graphical Processing Units (GPUs). The performance of the proposed solution is proved using a workstation with one NVIDIA GTX480 GPU and a cluster that consists of 32 nodes HP BL465c with an Infiniband network.
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