Adaptive Polarization Contrast Techniques for Through-Wall Microwave Imaging Applications

Yemelyanov, Konstantin M.; Engheta, Nader; Hoorfar, Ahmad; McVay, J.

doi:10.1109/tgrs.2009.2015569

Cited by 48 publications

(19 citation statements)

References 24 publications

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“…In [10], the authors describe an adaptive polarization contrast technique, in which the principal components (PCs) of the background and targets are extracted using the statistics of the original polarization data.…”

Section: Multiple-polarization Datamentioning

confidence: 99%

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Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging

Yang

Bouzerdoum

Tivive

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper addresses the problem of Through-the-Wall Radar Imaging (TWRI) using the Multiple-Measurement Vector (MMV) compressive sensing model. TWR image formation is reformulated as a compressed sensing (CS) problem, seeking a sparse representation in the spatial domain. In traditional CS-based through-the-wall radar imaging (TWRI) methods, the measurement matrix is vectorized so that a single measurement vector (SMV) model is applied to generate a sparse solution, which represents a scene comprising pointlike targets. For multiple measurement TWRI problems, the SMV model may produce a sub-optimum sparse solution. On the other hand, the proposed MMV model for TWRI generates a more sparse scene by processing all the measurements simultaneously. To evaluate the effectiveness of the proposed method, it is applied to fuse multiple polarization data to form the radar image. Based on simulated data with different number of measurements and noise levels, the proposed MMV-based TWRI method produces better TWR images in terms of image quality and detection accuracy.

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Section: Multiple-polarization Datamentioning

confidence: 99%

“…As i /L = As (10) Another approach is to use all individual polarization data simultaneously. This is done by arranging the collected data from each polarization in a column vector of the measurement matrix Y = y 1 , y 2 , .…”

Section: Multiple-polarization Datamentioning

confidence: 99%

Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging

Yang

Bouzerdoum

Tivive

et al. 2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…For example, in through-wall imaging wall penetration loss is a major challenge for radar imagery [4,5]. Effective design, assessment and installation of a radio network in an indoor environment require an accurate characterisation of the radio propagation channel.…”

Section: Introductionmentioning

confidence: 99%

Transmitted fields of a directional antenna in proximity of a wall

Hosseini¹,

Dehmollaian

2015

IET Microwaves, Antennas & Propagation

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Transmission of a directional antenna in near proximity of a building wall modelled by a semi-finite dielectric slab above a perfectly conducting ground is investigated and an accurate and fast method to predict wall penetration loss (PL) once the source/observation points are in proximity of the wall is proposed. First, the antenna is replaced by an array of electric Hertzian dipoles with approximately the same half power beamwidth. Second, the dipole array radiation is calculated both numerically and asymptotically. They are based on computation of Sommerfeld integrals using Filon quadrature (FQ) technique and the steepest descent method, respectively. Third, PL of a typical x-polarised and y-polarised horn and a z-polarised wire antenna, simulated in FEKO using the method of moments (MoM) is compared with that of, respectively, the x-, y-and z-directed dipole arrays calculated using FQ technique. A good agreement between MoM and the dipole model results are obtained for relatively short stand-off distances for which estimation based on slab transmission coefficient is not an accurate approximation. The proposed dipole array model with FQ technique makes calculation at least 7.5 times faster than FEKO. Fourth, PL for different wall permittivity and conductivity is studied.

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“…5,16 The conventional beamforming methods have been shown to be effective for image-based indoor target detection and localization when using a large aperture array and large signal bandwidth. [17][18][19][20][21][22][23][24] However, a limitation of the traditional beamforming methods is that they require the full data volume to form a high-quality image; otherwise, the image quality deteriorates rapidly with a reduction of measurements. The question is then how to exploit the advantages of the traditional beamforming methods to obtain high-quality images from a reduced set of measurements.…”

Section: Introductionmentioning

confidence: 99%

Two-stage through-the-wall radar image formation using compressive sensing

Tang

Bouzerdoum

Phung

2013

J. Electron. Imaging

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Abstract. We introduce a robust image-formation approach for through-the-wall radar imaging (TWRI). The proposed approach consists of two stages involving compressive sensing (CS) followed by delay-and-sum (DS) beamforming. In the first stage, CS is used to reconstruct a complete set of measurements from a small subset collected with a reduced number of transceivers and frequencies. DS beamforming is then applied to form the image using the reconstructed measurements. To promote sparsity of the CS solution, an overcomplete Gabor dictionary is employed to sparsely represent the imaged scene. The new approach requires far fewer measurement samples than the conventional DS beamforming and CSbased TWRI methods to reconstruct a high-quality image of the scene. Experimental results based on simulated and real data demonstrate the effectiveness and robustness of the proposed two-stage image formation technique, especially when the measurement set is drastically reduced. 1 Introduction Through-the-wall radar imaging (TWRI) is an emerging technology with considerable research interest and important applications in surveillance and reconnaissance for both civilian and military missions. [1][2][3][4][5][6] To deliver high-resolution radar images in both range and crossrange, TWRI systems use wideband signals and large aperture arrays (physical or synthetic). This leads to prolonged data acquisition and high computational complexity because a large number of samples need to be processed. New approaches for TWRI are therefore needed to obtain high-quality images from fewer data samples at a faster speed. To this end, this paper proposes a new approach using compressive sensing (CS) for through-the-wall radar imaging. CS is used here to reconstruct a full measurement set, which is then employed for image formation using delay-and-sum (DS) beamforming.CS enables a sparse signal to be reconstructed using considerably fewer data samples than what is required by the Nyquist-Shannon theorem. [7][8][9] In through-the-wall radar

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Adaptive Polarization Contrast Techniques for Through-Wall Microwave Imaging Applications

Cited by 48 publications

References 24 publications

Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging

Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging

Transmitted fields of a directional antenna in proximity of a wall

Two-stage through-the-wall radar image formation using compressive sensing

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