High-Resolution Through-the-Wall Radar Imaging Using Beamspace MUSIC

Yoon, Young-Ho; Amin, Moeness G.

doi:10.1109/tap.2008.923336

Cited by 107 publications

(55 citation statements)

References 19 publications

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“…Previously developed signal processing techniques that have also been applied to TTW imaging include the MUSIC algorithm [12], compressed sensing [13], image alignment procedures [14] and spatial filtering [15]. The focus of these techniques is often on the maximization the signal-to-noise ratio (SNR) of the desired target or removal of the significant primary wall reflection.…”

Section: Introductionmentioning

confidence: 99%

Through Wall Radar Classification of Human Micro-Doppler using Singular Value Decomposition Analysis

Ritchie

Ash

Chen

et al. 2016

Preprint

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Abstract:The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

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Section: Introductionmentioning

confidence: 99%

Through Wall Radar Classification of Human Micro-Doppler using Singular Value Decomposition Analysis

Ritchie

Ash

Chen

et al. 2016

Preprint

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“…Various reconstruction algorithms have been developed to process TWR measurement data. Several of these are based on beamforming or beam-space multiple signal classification (MUSIC) [5], while others cast the TWRI problem as an inverse scattering problem [6,7]. Among the reconstruction algorithms, a delayed-sum (DS) algorithm is widely used because of its algorithmic simplicity and robustness [8].…”

Section: Introductionmentioning

confidence: 99%

Target-to-Clutter Ratio Enhancement of Images in Through-the-Wall Radar Using a Radiation Pattern-Based Delayed-Sum Algorithm

Lim¹,

Nam²

2014

Journal of electromagnetic engineering and science

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In this paper, we compare the quality of images reconstructed by a conventional delayed-sum (DS) algorithm and radiation pattern-based DS algorithm. In order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in synthetic aperture radar (SAR) image assessment. The radiation pattern-based DS algorithm enhances the TCR of the image by focusing the target signals and preventing contamination of the radar scene. We first consider synthetic data obtained through GprMax2D/3D, a finite-difference time-domain (FDTD) forward solver. Experimental data of a 2-GHz bandwidth stepped-frequency signal are collected using a vector network analyzer (VNA) in an anechoic chamber setup. The radiation pattern-based DS algorithm shows a 6.7-dB higher TCR compared to the conventional DS algorithm. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ⓒ

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“…One of the primary objectives of through-the-wall radar imaging (TWRI) is to detect and recognize objects behind walls using microwave signals [1][2][3][4][5]. In TWRI, the backprojection method is typically employed for image formation.…”

Section: Introductionmentioning

confidence: 99%

Compressive sensing for through wall radar imaging of stationary scenes using arbitrary data measurements

Lagunas

Amin

Ahmad

et al. 2012

2012 11th International Conference on Information Science, Signal Processing and Their Applications (ISSPA)

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In this paper, we deal with removal of wall EM reflections prior to image reconstruction using step-frequency radars. The goal is to enable behind-the-wall target detection and localization from reduced data measurements. In the underlying problem, few frequency observations are available and they differ from one antenna position to another in a SAR imaging system. Because of using a different set of frequencies for different antennas, direct applications of wall clutter mitigation methods, such as subspace and spatial filtering, prove ineffective. To provide these methods with the response measured at the same set of frequencies, a compressive sensing approach is used to reconstruct the range profiles. We use prior knowledge of the wall standoff distance to speed up the convergence of the Orthogonal Matching Pursuit for sparse data reconstruction.

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High-Resolution Through-the-Wall Radar Imaging Using Beamspace MUSIC

Cited by 107 publications

References 19 publications

Through Wall Radar Classification of Human Micro-Doppler using Singular Value Decomposition Analysis

Through Wall Radar Classification of Human Micro-Doppler using Singular Value Decomposition Analysis

Target-to-Clutter Ratio Enhancement of Images in Through-the-Wall Radar Using a Radiation Pattern-Based Delayed-Sum Algorithm

Compressive sensing for through wall radar imaging of stationary scenes using arbitrary data measurements

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