Experiments on wideband through-the-wall radar imaging (Invited Paper)

Dilsavor, Ronald L.; Ailes, William; Rush, Paul; Ahmad, Fauzia; Keichel, William; Titi, G.W.; Amin, Moeness G.

doi:10.1117/12.607742

Cited by 44 publications

(30 citation statements)

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“…The transceiver is placed at 69 different positions to synthesize an array aperture of length 1.2246m. The interested reader is referred to [29] for a detailed description of the experimental setup.…”

Section: Resultsmentioning

confidence: 99%

Multi-polarization through-the-wall radar imaging using joint Bayesian compressed sensing

Bouzerdoum

Tivive

Tang

2014

2014 19th International Conference on Digital Signal Processing

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This paper presents a new image formation method for multi-polarization through-the-wall radar imaging. The proposed method combines wall clutter mitigation and scene reconstruction in a unified framework using multitask Bayesian compressed sensing. First, the radar signals are jointly recovered using Bayesian compressed sensing in the wavelet domain. Then, a subspace projection method is employed to mitigate the front wall reflections. This is followed by principal component analysis, which is used to compress the remaining wavelet coefficients and remove noise. A linear model is developed which relates the compressed wavelet coefficients directly to the image of the scene. For scene reconstruction, multitask Bayesian compressed sensing is further applied to simultaneously form the images associated with all polarimetric channels. Experimental results based on real radar data demonstrate that the proposed method improves image quality by enhancing target reflections and attenuating background clutter. Abstract-This paper presents a new image formation method for multi-polarization through-the-wall radar imaging. The proposed method combines wall clutter mitigation and scene reconstruction in a unified framework using multitask Bayesian compressed sensing. First, the radar signals are jointly recovered using Bayesian compressed sensing in the wavelet domain. Then, a subspace projection method is employed to mitigate the front wall reflections. This is followed by principal component analysis, which is used to compress the remaining wavelet coefficients and remove noise. A linear model is developed which relates the compressed wavelet coefficients directly to the image of the scene. For scene reconstruction, multitask Bayesian compressed sensing is further applied to simultaneously form the images associated with all polarimetric channels. Experimental results based on real radar data demonstrate that the proposed method improves image quality by enhancing target reflections and attenuating background clutter. I. INTRODUCTIONThrough-the-wall radar imaging (TWRI) is emerging as a viable sensing technology supporting a range of civilian and military applications, such as search-and-rescue, law enforcement, and urban surveillance and reconnaissance. It can be used for various purposes, including determining the building layout, and locating and identifying stationary objects behind walls. In the past years, numerous studies have been conducted in modeling and imaging stationary and moving targets behind walls and inside enclosed building structures [1]- [4]. However, there is still a need for producing high quality images that can effectively discriminate the targets of interest from clutter without increasing the data acquisition time.Several TWRI studies have focused on the use of polarization of the electromagnetic (EM) waves for detecting targets or enhancing the discrimination of targets [5]-[11]. In [5], a method for through-the-wall detection of certain types of weapons was developed by analyzing the...

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Section: Resultsmentioning

confidence: 99%

Multi-polarization through-the-wall radar imaging using joint Bayesian compressed sensing

Bouzerdoum

Tivive

Tang

2014

2014 19th International Conference on Digital Signal Processing

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“…The nonhomogeneous wall has a thickness of 0.127 m and is placed at a standoff distance of 0.0127 m from the front face of the antennas. More detailed information about the experimental setup is provided in [14]. The scene to be imaged consists of nine targets: a sphere, three dihedrals, four trihedrals and a cylinder.…”

Section: Resultsmentioning

confidence: 99%

Efficient Sparse Imaging Reconstruction Algorithm for Through-the-Wall Radar

Qu¹,

Xing²,

Yang³

2017

PIER C

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Abstract-Sparse reconstruction technique can be used to provide high-resolution imaging result for through-the-wall radar (TWR) system. Since conventional sparse imaging reconstruction algorithms usually require a tremendous amount of computer memory and computational complexity, it is very difficult to apply in the practical large-scale TWR imaging applications. To solve the above problem, an efficient sparse imaging reconstruction algorithm is proposed in this paper. The proposed imaging method combines the spectral projection gradient L1-norm (SPGL1) algorithm with nonuniform fast Fourier transform (NUFFT) technique to achieve imaging reconstruction. Benefiting from the function handle operation of SPGL1 and computational efficiency of NUFFT, the proposed imaging algorithm can significantly reduce the memory requirement and computation complexity. The simulated and experimental results have shown that the proposed imaging method can significantly reduce the required computer memory and computational cost while providing the similar recovered image quality as the conventional sparse imaging method.

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“…7,3] GHz with a frequency step of 2.875 MHz. A 57-element array was synthesized by mounting a horn antenna, model ETS-Lindgren 3164-04, with an operational bandwidth of 0.7-6 GHz on a scanner and moving the scanner horizontally at an interspacing of 0.022 m. A room of size 7.62 × 7.62 m was padded with radio frequency-absorbing material for data collection; the interested reader is referred to [14] for more details about the room setting, the data acquisition, and the specification of the imaging system. The TWRI system was used to interrogate a scene populated with three targets placed at different locations behind a concrete wall of thickness 0.14 m and dielectric constant 7.6632.…”

Section: Experimental Results Using Real Datamentioning

confidence: 99%

Sparsity-Aware Human Motion Indication

Bouzerdoum

Yang

Tivive

2017

Compressive Sensing for Urban Radar

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Discrimination of targets can be improved significantly by analyzing the polarization of scattered electromagnetic waves. In radar imaging, the target image can be enhanced by combining measurements from different polarizations. In this chapter, we propose a joint image formation and fusion approach for multipolarization through-the-wall radar imaging, using compressive sensing (CS). The measurements from different polarization channels are processed jointly using the multiple measurement vector (MMV) model to produce several images of the scene, each corresponding to a polarization channel. Furthermore, the measurement vectors are fused together to form a composite measurement vector, which yields a composite image of the scene. The advantage of fusing the measurement vectors before image formation is that the measurement noise is reduced and the target information is enhanced, which leads to a more informative composite image. The MMV model enforces the same sparsity support for all formed images by reinforcing target information across channels and attenuating noise. Experimental results are presented using simulated and real data. Analysis and comparison of experimental results demonstrate the effectiveness of the proposed through-the-wall radar imaging approach, especially in the presence of high-measurement noise.

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Experiments on wideband through-the-wall radar imaging (Invited Paper)

Cited by 44 publications

References 0 publications

Multi-polarization through-the-wall radar imaging using joint Bayesian compressed sensing

Multi-polarization through-the-wall radar imaging using joint Bayesian compressed sensing

Efficient Sparse Imaging Reconstruction Algorithm for Through-the-Wall Radar

Sparsity-Aware Human Motion Indication

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