Multipath Ghosts in Through-the-Wall Radar Imaging: Challenges and Solutions

Abdalla, Abdi T.; Alkhodary, Mohammad T.; Muqaibel, Ali H.

doi:10.4218/etrij.2017-0241

Cited by 10 publications

(8 citation statements)

References 21 publications

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“…The technique may be used to determine room layout or to recognize nature of the activities executed by people in the room. TWRI offers a wide range of applications, including rescue mission in natural disasters (fire, flood, and earthquake), criminal investigation, emergency relief operation, and military operations [1][2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Aspect Dependent-based Ghost Suppression for Extended Targets in Through-the-wall Radar Imaging under Compressive Sensing Framework

Rambika

Abdalla

Amour

et al. 2021

Preprint

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Several approaches have been proposed to suppress multipath ghost in through-the-wall radar imaging (TWRI). One classical approach, called Aspect Dependent (AD), exploits locations of ghosts in the images without demanding prior knowledge of the reflecting geometry. This operation strategy makes the method superior over multipath exploitation-based approaches. However, the AD method assumes a point target that emulates unreal environment. Therefore, reconstructing extended targets with this method leads to incorrect scene interpretation. This work proposes a ghost suppression method for extended targets based on the AD feature that exploits duo sub-apertures. Firstly, we evaluate the best suppression method using a performance metric called relative clutter peak. Next, the evaluated method is extended to encompass the target extent during sub-images reconstruction. Following this strategy, an effective image fusion method suitable for extended targets is proposed. The method considers pixel neighborhood to effectively recover the given extended target. Simulation results show that the proposed method significantly improves signal-to-clutter ratio and relative clutter peak by 8.8% and 23.8%, respectively, relative to the existing AD based methods under point target assumption.

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Section: Introductionmentioning

confidence: 99%

Aspect Dependent-based Ghost Suppression for Extended Targets in Through-the-wall Radar Imaging under Compressive Sensing Framework

Rambika

Abdalla

Amour

et al. 2021

Preprint

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“…Recently, through-the-wall radar imaging (TWRI) has found wide applications in searching and rescuing missions of civilian: earthquake, hostage, and fire, among others [1][2][3][4]. TWRI facilitates detection, localization, and classification of objects behind opaque structures [5][6][7][8]. Because of their massive applications in delicate tasks, current studies in TWRI focus on producing highly resolved radar images of the scenes of interest.…”

Section: Introductionmentioning

confidence: 99%

“…Three major approaches exist to solve the CS reconstruction problem, namely convex optimization, greedy, and Bayesian [5,14].Optimization-based approaches usually use ℓ 1 -norm [15], and, given the noisy through-the-wall measurements, researchers find the Basis Pursuit De-Noising as a more suitable convex optimization method [13]. Under particular situations, ℓ 1 /ℓ 2 -norm may be employed to achieve better reconstruction [7].…”

Section: Introductionmentioning

confidence: 99%

Fast sparse image reconstruction method in through-the-wall radars using limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm

Mwisomba

Abdalla

Amour

et al. 2021

Int. J. Microw. Wireless Technol.

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Compressed sensing allows recovery of image signals using a portion of data – a technique that has drastically revolutionized the field of through-the-wall radar imaging (TWRI). This technique can be accomplished through nonlinear methods, including convex programming and greedy iterative algorithms. However, such (nonlinear) methods increase the computational cost at the sensing and reconstruction stages, thus limiting the application of TWRI in delicate practical tasks (e.g. military operations and rescue missions) that demand fast response times. Motivated by this limitation, the current work introduces the use of a numerical optimization algorithm, called Limited Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS), to the TWRI framework to lower image reconstruction time. LBFGS, a well-known Quasi-Newton algorithm, has traditionally been applied to solve large scale optimization problems. Despite its potential applications, this algorithm has not been extensively applied in TWRI. Therefore, guided by LBFGS and using the Euclidean norm, we employed the regularized least square method to solve the cost function of the TWRI problem. Simulation results show that our method reduces the computational time by 87% relative to the classical method, even under situations of increased number of targets or large data volume. Moreover, the results show that the proposed method remains robust when applied to noisy environment.

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“…For through-wall radar imaging (TWRI) based on multiinput multi-output (MIMO) array [1]- [3], the presence of furniture, walls, floors, and ceilings makes electromagnetic waves produce strong multipath reflections between targets and them. The above phenomenon generates multipath ghosts at non-target locations [4], [5]. In addition, the inter-element spacing of the antenna array in the TWRI system is usually higher than half a wavelength due to the desire for acceptable resolution and reduced complexity of the system, thereby causing grating lobes.…”

Section: Introductionmentioning

confidence: 99%

Multipath Ghost and Side/Grating Lobe Suppression Based on Stacked Generative Adversarial Nets in MIMO Through-Wall Radar Imaging

et al. 2019

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For multi-input multi-output (MIMO) through-wall radar imaging (TWRI), multipath ghosts and side/grating lobe artifacts degrade the imaging quality of the obscured targets inside an enclosed building, therein hindering target detection. In this paper, an approach based on two stacked generative adversarial nets (GAN) is proposed to achieve multipath and side/grating lobe suppression with regard to MIMO TWRI. Specifically, the Stage-I GAN constructs a spatial structure mapping from the original input images to the Stage-I GAN output images with the suppressed multipath ghosts. However, the side/grating lobe artifacts are intentionally preserved in the stage-I GAN as additional constraint information to prevent uncontrollable over-fitting. Then, the Stage-II GAN takes the output images of Stage-I GAN as input to suppress the side/grating lobe artifacts. Extensive electromagnetic simulations and comparisons demonstrate that the proposed approach achieves better suppression of multipath ghosts and side/grating lobe artifacts and other significant superiorities, including priori wall information not being required, the preservation of weak targets, and robustness for different array deployments and building layouts.INDEX TERMS Stacked generative adversarial nets, multi-input multi-output, through-wall radar imaging, multipath ghosts, side/grating lobe artifacts.

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Multipath Ghosts in Through-the-Wall Radar Imaging: Challenges and Solutions

Cited by 10 publications

References 21 publications

Aspect Dependent-based Ghost Suppression for Extended Targets in Through-the-wall Radar Imaging under Compressive Sensing Framework

Aspect Dependent-based Ghost Suppression for Extended Targets in Through-the-wall Radar Imaging under Compressive Sensing Framework

Fast sparse image reconstruction method in through-the-wall radars using limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm

Multipath Ghost and Side/Grating Lobe Suppression Based on Stacked Generative Adversarial Nets in MIMO Through-Wall Radar Imaging

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