Results 141 to 150 of about 2,457,520 (174)
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Signal Processing for Microwave Array Imaging: TDC and Sparse Recovery
IEEE Transactions on Geoscience and Remote Sensing, 2012Unlike 1-D and 2-D microwave images, 3-D microwave image behaves typical sparsity. Consequently, sparse recovery technique can be used for 3-D microwave signal processing. Three popular signal processing techniques, the time-domain correlation method (TDC), pseudo-inverse method (PI), and compressed sensing method (CS), are discussed in this paper.
Jun Shi, Xiaoling Zhang
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On the Use of Spherical Harmonics in Sparse Microwave Imaging
2020 14th European Conference on Antennas and Propagation (EuCAP), 2020In this paper, we introduce a novel microwave imaging method using spherical harmonics and the sparse processing. As the sources of the spherical harmonics, we use the multipoles of different order. Based on theoretical considerations and as shown by numerical examples this approach is capable of retrieving concave shapes, which is particularly ...
Vojnovic Nebojsa +2 more
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Sparse microwave breast imaging with differently polarized arrays
2016 24th European Signal Processing Conference (EUSIPCO), 2016We investigate the role of polarization in sparse differential microwave imaging for the breast-cancer localization. We consider two types of antenna arrays, placed around realistic inhomogeneous breast models. In the first case, the antennas are vertical with respect to the chest wall, whereas in the second case, the antennas are located in the ...
Marija Nikolic Stevanovic +3 more
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Sparse array imaging for microwave gauging by compressed sensing
IEEE Transactions on Aerospace and Electronic Systems, 2014The reliability of microwave level gauging can be improved by radar imaging techniques. An antenna array with a large aperture must be used, in order to achieve the desired resolution. At the same time, the number of array elements is limited to only a few, which leads to ambiguities due to spatial undersampling.
Stephan Kolb, Reinhard Stolle
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Microwave radiation imaging approach based on sparse representation
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Based on sparsity or compressibility of microwave radiation images, Compressive Sensing in this paper is adopted to achieve microwave radiation imaging in order to reduce the complexity and hardware cost of the imaging system and get the image of high spatial resolution.
Xiaocheng Yang +6 more
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Higher Order Sparse Microwave Imaging of PEC Scatterers
IEEE Transactions on Antennas and Propagation, 2016In this paper, we present an innovative algorithm for sparse imaging of perfect electric conducting (PEC) targets. The algorithm is based on higher order sources or multipoles, rather than zero-order ones as in the standard sparse imaging. We demonstrate that, due to the directivity of higher order sources, the imaging capabilities of the algorithm are
Arye Nehorai +2 more
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Application of Optimized Sparse Antenna Array in Near Range 3D Microwave Imaging
Weixian Tan, Wen Hong, Xueming Peng
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High-Resolution Microwave Photonic Sparse Imaging Radar
IEEE Transactions on Microwave Theory and TechniquesCong Ma, Xiangchuan Wang, Shilong Pan
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Microwave synthetic aperture radar imaging using sparse measurement
2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, 2016This paper evaluates Compressed Sensing (CS) techniques for image recovery from sparse measurement of wideband microwave synthetic aperture radar. A specimen under test (SUT) consists of a tray of small rocks of different densities and with/without one piece that is wrapped in aluminum foil.
Xiahan Yang +4 more
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Application of sparse array and MIMO in near-range microwave imaging
SPIE Proceedings, 2011Near range microwave imaging systems have broad application prospects in the field of concealed weapon detection, biomedical imaging, nondestructive testing, etc. In this paper, the techniques of MIMO and sparse line array are applied to near range microwave imaging, which can greatly reduce the complexity of imaging systems.
Yaolong Qi +3 more
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