A new beamformer based on phase dispersion to improve 2D sparse array response

Martínez-Graullera, Óscar; Romero-Laorden, David; Ibanez, A.; Ullate, L.G.

doi:10.1109/sam.2012.6250532

Cited by 3 publications

(2 citation statements)

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“…Extremely high sparsity can be achieved by irregularly distributed MIMO arrays, since the randomness of element layouts tends to share the energy of sidelobes amongst the elements, and the sidelobes and grating lobes will be suppressed according to the projection slice method [ 12 ]. Finding approaches to improve the precision of 3D images has been an area of active research focus for irregularly distributed arrays, resulting in a number of methods such as the modified Kirchhoff Migration Algorithm [ 13 ], and the grating lobes migration technology [ 14 , 15 , 16 ]. In terms of efficiency, fast back projection algorithms seem to be attractive [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Dimension-Factorized Range Migration Algorithm for Regularly Distributed Array Imaging

Guo

Wang

Chang

et al. 2017

Sensors

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The two-dimensional planar MIMO array is a popular approach for millimeter wave imaging applications. As a promising practical alternative, sparse MIMO arrays have been devised to reduce the number of antenna elements and transmitting/receiving channels with predictable and acceptable loss in image quality. In this paper, a high precision three-dimensional imaging algorithm is proposed for MIMO arrays of the regularly distributed type, especially the sparse varieties. Termed the Dimension-Factorized Range Migration Algorithm, the new imaging approach factorizes the conventional MIMO Range Migration Algorithm into multiple operations across the sparse dimensions. The thinner the sparse dimensions of the array, the more efficient the new algorithm will be. Advantages of the proposed approach are demonstrated by comparison with the conventional MIMO Range Migration Algorithm and its non-uniform fast Fourier transform based variant in terms of all the important characteristics of the approaches, especially the anti-noise capability. The computation cost is analyzed as well to evaluate the efficiency quantitatively.

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Section: Introductionmentioning

confidence: 99%

Dimension-Factorized Range Migration Algorithm for Regularly Distributed Array Imaging

Guo

Wang

Chang

et al. 2017

Sensors

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“…By introducing Phase Coherence Factors in sparse array imaging, the grating lobes of beams are effectively inhibited along with the improvement of lateral resolution and SNR [16] . And, a new beam forming method based on the principle of Phase Dispersion improves the two-dimensional sparse array imaging contrast of 50dB [17] .…”

Section: Introductionmentioning

confidence: 99%

Efficiency improvement of total focusing imaging based on sparse array method

Peng

et al. 2014

2014 IEEE Far East Forum on Nondestructive Evaluation/Testing

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In this paper, a design concept of sparse array is applied to total focusing imaging technique. During the process, the elements of a phased array are partially selected according to the theory of effective aperture, in order to reduce the amount of captured data and shorten the processing time of TFM imaging. Simulated imaging with Field II program and experimental verification on two standard phased array specimens are performed, based on three selected arrays which are sparsely as 1/2, 1/4 and 1/8. The results compared with the full array in terms of image quality and computing efficiency, show that the computing efficiency of TFM imaging can get a rise of 40 percent by adopting the sparse array design. Meanwhile, an approximate resolution can be achieved at the lowest array sparse rate of 1/4. Affected by the side-lobes of the ultrasonic beam, contrast of the image may suffer little loss.

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