Amplitude and Phase Measurements of Scattered Fields for Quantitative Imaging in the W-band

Zeitler, A.; Migliaccio, Claire; Moynot, Amael; Aliferis, Iannis; Brochier, Laurent; Dauvignac, Jean-Yves; Pichot, Christian

doi:10.1109/tap.2013.2255113

Cited by 8 publications

(2 citation statements)

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“…Techniques to improve the spatial resolution [16,17] and human discrimination [18,19] have also been proposed, through frequency spectral analysis. The implementation of full-wave inverse scattering approaches for quantitative TW imaging is still an open issue, as most algorithms for solving the non-linear inverse scattering problem are usually developed for free-space applications [20][21][22][23][24][25][26][27][28][29][30] and must be properly adapted to include the presence of the wall.…”

Section: Introductionmentioning

confidence: 99%

Through-the-Wall Microwave Imaging: Forward and Inverse Scattering Modeling

Fedeli

Pastorino

Ponti

et al. 2020

Sensors

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The imaging of dielectric targets hidden behind a wall is addressed in this paper. An analytical solver for a fast and accurate computation of the forward scattered field by the targets is proposed, which takes into account all the interactions of the electromagnetic field with the interfaces of the wall. Furthermore, an inversion procedure able to address the full underlying non-linear inverse scattering problem is introduced. This technique exploits a regularizing scheme in Lebesgue spaces in order to reconstruct an image of the hidden targets. Preliminary numerical results are provided in order to initially assess the capabilities of the developed solvers.

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Section: Introductionmentioning

confidence: 99%

Through-the-Wall Microwave Imaging: Forward and Inverse Scattering Modeling

Fedeli

Pastorino

Ponti

et al. 2020

Sensors

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“…Yurduseven et al [11] emphasize the interest of holographic antennas for high resolution images, while Imani et al [12] summarize advances in the fusion of metasurface antenna design and computational imaging. Computational aspects of image reconstruction are also considered in the work of Beasley et al [13], Zeitler et al [14], Nsengiyumva et al [15]. In the book from Nsengiyumva et al [16], qualitative reconstruction from scattered field synthetic data and measurements is performed using a 'Backpropagation' algorithm.…”

Section: Introductionmentioning

confidence: 99%

Joint Design of the Hardware and the Software of a Radar System with the Mixed Grey Wolf Optimizer: Application to Security Check

et al. 2020

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The purpose of this work is to perform the joint design of a classification system including both a radar sensor and an image processing software. Experimental data were generated with a three-dimensional scanner. The criterion which rules the design is the false recognition rate, which should be as small as possible. The classifier involved is support vector machines, combined with an error correcting code. We apply the proposed method to optimize security check. For this purpose we retain eight relevant parameters which impact the recognition performances. To estimate the best parameters, we adapt our adaptive mixed grey wolf algorithm. This is a computational technique inspired by nature to minimize a criterion. Our adaptive mixed grey wolf algorithmwas found to outperform comparative methods in terms of computational load on simulations and with real-world data.

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