2014
DOI: 10.1364/oe.22.013515
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Adaptive millimeter-wave synthetic aperture imaging for compressive sampling of sparse scenes

Abstract: Abstract:We apply adaptive sensing techniques to the problem of locating sparse metallic scatterers using high-resolution, frequency modulated continuous wave W-band RADAR. Using a single detector, a frequency stepped source, and a lateral translation stage, inverse synthetic aperture RADAR reconstruction techniques are used to search for one or two wire scatterers within a specified range, while an adaptive algorithm determined successive sampling locations. The two-dimensional location of each scatterer is t… Show more

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Cited by 4 publications
(2 citation statements)
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“…Moreover, in this paper, in order to improve the performance of the cylindrical MMW imaging system in terms of the total cost and image quality, a sparse multi-static 1D antenna array has been designed and implemented. There have been some previous efforts for implementing sparse MMW imaging systems [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. In a group of works, compressive sensing theory has been utilised in order to reduce the number of required samples for obtaining an acceptable number of antennas [12][13][14][15][16].…”
Section: Introductionmentioning
confidence: 99%
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“…Moreover, in this paper, in order to improve the performance of the cylindrical MMW imaging system in terms of the total cost and image quality, a sparse multi-static 1D antenna array has been designed and implemented. There have been some previous efforts for implementing sparse MMW imaging systems [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. In a group of works, compressive sensing theory has been utilised in order to reduce the number of required samples for obtaining an acceptable number of antennas [12][13][14][15][16].…”
Section: Introductionmentioning
confidence: 99%
“…This loss of data is compensated in adaptive image reconstruction algorithms. These methods maintain the information available in acquired data and do not need a priori knowledge about the specimen under test [17][18][19][20][21]. Still, the second shortcoming is the computational cost of the related optimisation algorithms, which is much greater than conventional image reconstruction methods.…”
Section: Introductionmentioning
confidence: 99%