A Sparse Bayesian Learning Approach for Through-Wall Radar Imaging of Stationary Targets

Tang, Van Ha; Phung, Son Lam; Tivive, Fok Hing Chi; Bouzerdoum, A.

doi:10.1109/taes.2017.2701646

Cited by 21 publications

(15 citation statements)

References 49 publications

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“…In SAR imaging, the number of dominated scattering centers are generally unknown, thus, the algorithms based on the sparsity of the signal are impracticable. As analyzed in [ 22 , 23 , 24 ], sparse Bayesian learning (SBL) is an effective approach for sparse recovery. Compared with other

Section: 3d Imaging Methodologymentioning

confidence: 99%

“…Compared with other

-norm minimization algorithms, the SBL-based algorithm adopts iteration during the procedure to avoid convergence to the local minimum and produces a full posterior distribution as the solution. Based on the Gaussian scale mixture (GSM), the prior probability of the distribution of scattering coefficient

is represented as [ 23 ]:

where

is the hyperparameter that governs

.…”

Section: 3d Imaging Methodologymentioning

confidence: 99%

“…However, the noise variance

is a nuisance parameter and the estimate may be highly inaccurate [ 22 ]. According to [ 23 ],

can be integrated out by introducing a prior distribution, which is a gamma distribution given by

where a and b are deterministic small value, and

is a gamma function defined as

. With the prior probability of

, the posterior of

is yielded as

…”

Section: 3d Imaging Methodologymentioning

confidence: 99%

“…In Ref. [ 23 ], SBL was applied to through-wall imaging with missing data measurements, and in [ 24 ], a local structural SBL was adopted to form ISAR images of maneuvering targets with compressively sampled data. The desired results were obtained in both of the studies.…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Imaging of Terahertz Circular SAR with Sparse Linear Array

Hao¹,

Li²,

Pi³

2018

Sensors

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Due to the non-contact detection ability of radar and the harmlessness of terahertz waves to the human body, three-dimensional (3D) imaging using terahertz synthetic aperture radar (SAR) is an efficient method of security detection in public areas. To achieve high-resolution and all aspect imaging, circular trajectory movement of radar and linear sensor array along the height direction were used in this study. However, the short wavelength of terahertz waves makes it practically impossible for the hardware to satisfy the half-wavelength spacing condition to avoid grating lobes. To solve this problem, a sparse linear array model based on the equivalent phase center principle was established. With the designed imaging geometry and corresponding echo signal model, a 3D imaging algorithm was derived. Firstly, the phase-preserving algorithm was adopted to obtain the 2D image of the ground plane for each sensor. Secondly, the sparse recovery method was applied to accomplish the scattering coefficient reconstruction along the height direction. After reconstruction of all the range-azimuth cells was accomplished, the final 3D image was obtained. Numerical simulations and experiments using terahertz radar were performed. The imaging results verify the effectiveness of the 3D imaging algorithm for the proposed model and validate the feasibility of terahertz radar applied in security detection.

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Section: 3d Imaging Methodologymentioning

confidence: 99%

“…Compared with other

is represented as [ 23 ]:

where

is the hyperparameter that governs

.…”

Section: 3d Imaging Methodologymentioning

confidence: 99%

“…However, the noise variance

is a nuisance parameter and the estimate may be highly inaccurate [ 22 ]. According to [ 23 ],

can be integrated out by introducing a prior distribution, which is a gamma distribution given by

where a and b are deterministic small value, and

is a gamma function defined as

. With the prior probability of

, the posterior of

is yielded as

…”

Section: 3d Imaging Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Three-Dimensional Imaging of Terahertz Circular SAR with Sparse Linear Array

Hao¹,

Li²,

Pi³

2018

Sensors

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show abstract

“…Due to the potential of revealing targets behind an opaque obstacle, the technology of through-the-wall radar imaging (TWRI) has attracted much interest for public safety and defense applications [ 1 , 2 , 3 , 4 , 5 , 6 ]. TWRI is especially useful in behind-the-wall reconnaissance, law enforcement and various earthquake and avalanche rescue missions [ 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Interaction Multipath in Through-the-Wall Radar Imaging Based on Compressive Sensing

Hong

Zhu

2018

Sensors

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Clutters caused by multipath have been widely researched in through-the-wall radar imaging (TWRI). The existing research work of multipath only consider reflections from the wall, while in the condition of a small scene, with the increasing number of targets, multipath from targets to targets, named interaction multipath, usually generates ghosts, which degrades the performance of TWRI. In order to mitigate the effect of interaction multipath, considering fast data acquisition and measurement reduction, we made use of the propagation characteristic of interaction multipath to build the sparse model of the target scene and developed a compressive sensing (CS)-based method, which is referred to as ‘interaction CS’. For the number of point targets increasing from 5–8, intensive evaluation and direct comparison of the imaging results with existing methods are conducted to show that the proposed interaction CS performs better at ghost suppression in the same condition of the signal-to-noise ratio (SNR).

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Towards real‐time through‐obstacle imaging based on compressed sensing for sparse objects

Zhou

Shen

Meng

et al. 2019

IET Microwaves, Antennas & Propagation

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Although significant progress has been made in microwave imaging, real‐time imaging, especially for objects behind walls or closed obstacles, remains a technical challenge. In this work, highly efficient imaging for complex‐structured objects surrounded by a closed obstacle was experimentally demonstrated. The imaging equations are derived based on a combination of the inverse‐scattering problem and the concept of compressed sensing. Making use of the spatial sparsity of objects and obstacles, the compressed imaging can be implemented using a time‐division multi‐antenna setup with reduced transmitting antennas. Owing to the spatial compressed sensing applied to the sparse imaging region and objects, the imaging time can be reduced by two orders of magnitude compared with the conventional twofold subspace‐based optimisation method with a comparable imaging quality. Taking advantage of the sparsity of the entire imaging area, objects with larger relative permittivity can also be reconstructed. The proposed method can be potentially used in applications such as security examination through boxes. It also provides a new clue for solving the practicability difficulty faced by existing microwave imaging systems.

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A Sparse Bayesian Learning Approach for Through-Wall Radar Imaging of Stationary Targets

Cited by 21 publications

References 49 publications

Three-Dimensional Imaging of Terahertz Circular SAR with Sparse Linear Array

Three-Dimensional Imaging of Terahertz Circular SAR with Sparse Linear Array

Interaction Multipath in Through-the-Wall Radar Imaging Based on Compressive Sensing

Towards real‐time through‐obstacle imaging based on compressed sensing for sparse objects

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