Through-the-Wall Surveillance With Millimeter-Wave LFMCW Radars

Gonzalez-Partida, J.-T.; Almorox-González, P.; Burgos-García, M.; Dorta-Naranjo, B. Pablo; Alonso, J.I.

doi:10.1109/tgrs.2008.2007738

Cited by 32 publications

(6 citation statements)

References 23 publications

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“…SVD removes ground surface reflection based on common singular vectors [15]. This method applies to received data in a line scan as described in the following:

X_{m} = [x_{m, 1}, x_{m, 2}, \dots, x_{m, N}]

where X m can be represented as follows [21]:

X_{m} = \sum_{k = 1}^{L} σ_{k} u_{k} v_{k}^{H}

…”

Section: Clutter Cancellationmentioning

confidence: 99%

See 1 more Smart Citation

Robust Wiener filter‐based time gating method for detection of shallowly buried objects

Gharamohammadi

Behnia

Shokouhmand

et al. 2021

IET Signal Processing

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A robust method for ultra-wideband (UWB) imaging of buried shallow objects based on time gating, Wiener filtering, as well as constant false alarm rate (CFAR) is proposed. Moreover, it is demonstrated that Wiener filtering can be used as a clutter removal tool in UWB signal applications. Basically, the problem with time gating method is that the length of the timing window for unknown targets cannot be determined accurately in advance. In fact, it is a blind methodology and some targets can be missed due to a lack of preknowledge about their depth. Imprecise window length selection leads to missing some parts of the target signals along with the clutter, which in turn increases the missed detection rate. Herein, an algorithm to tackle this problem is proposed by using a Wiener filter along with CFAR as a primary detector of the target positions employing average similarity function imaging. The time gating method is then built on top of the information achieved for the window length selection from the primary detection. The combination of the two steps provides better detection of shallowly buried objects with less missed detection of targets, besides having fewer artefacts in comparison to other methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…SVD removes ground surface reflection based on common singular vectors [15]. This method applies to received data in a line scan as described in the following:

X_{m} = [x_{m, 1}, x_{m, 2}, \dots, x_{m, N}]

where X m can be represented as follows [21]:

X_{m} = \sum_{k = 1}^{L} σ_{k} u_{k} v_{k}^{H}

…”

Section: Clutter Cancellationmentioning

confidence: 99%

“…Ultra-wideband (UWB) imaging is a versatile imaging method which is used in different fields of anomaly detection such as BOI [13][14][15][16][17][18], breast cancer imaging [19,20] and through wall imaging [21]. To accurately detect shallowly buried objects, proper cancellation of the reflected signals from the ground surface is needed.…”

Section: Introductionmentioning

confidence: 99%

Robust Wiener filter‐based time gating method for detection of shallowly buried objects

Gharamohammadi

Behnia

Shokouhmand

et al. 2021

IET Signal Processing

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“…In general, the existing algorithms for data argumentation in through-the-wall radar imaging (TWRI) can be divided into five categories: signal domain transformation methods, statistical signal processing methods, compressed sensing (CS) methods, traditional machine learning methods and deep learning methods [3]. Among them, the signal domain transformation methods, including fast Fourier transform [4]- [7], shorttime Fourier transform [8], [9], wavelet transform [10], [11], and their improvements, [12]- [14] can achieve high peak signal-to-noise ratio (PSNR) and robustness with the lowest computational cost in specific conditions, which achieves the classifiers' accuracy up to 80% [6]- [8]. The statistical signal processing methods, including signal estimation algorithm [15]- [18] and signal detection algorithm [19]- [22], are applied with single-transmitter single-receiver (SISO), multitransmitter multiple-receiver (MIMO) and array through-thewall radar systems, which improve the recognition accuracy to more than 80% [15].…”

Section: Introductionmentioning

confidence: 99%

Attention Mechanism and Sparse Low-Rank Modeling Based Neural Network for Data Augmentation of Through-the-Wall Radar Human Motion Recognition

Gao¹,

Lan²,

Yang³

et al. 2022

Preprint

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In order to better address the non-adaptability of image recognition algorithms on through-the-wall radar human motion data and the low signal-to-noise ratio (SNR) caused by microwave penetration through walls, an attention mechanism and sparse low-rank modeling based neural network is proposed in this paper. The method combines information from physical background of moving target and vision characteristics of imaging to achieve effective suppression of wall clutter and noise as well as enhancement of motion feature. The super resolution of human motion feature is achieved by sparsely encoded learning iterative shrinkage thresholding (LISTA) module. The location of human behind the wall is obtained by an improved coordinate attention mechanism, which automatically calibrates the regions associated with human motion characteristics. Chunked output super-resolution information after attention mechanism and LISTA module is finally weighted and aggregated by the parallel adaptive weight module. Experiments demonstrate that a better SNR is achieved for image feature extraction, while the accuracy and the convergence speed of the existing classification algorithms is effectively raised.

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“…Since mmWs are non-ionizing and have no known side effects on health at moderate power levels, they can be used in public places [4]. mmWs have been preferred in various applications including security [5], medical [6], through-the-wall detection [7] and non-destructive testing [8]. Because of the terrorist attacks, 3D imaging systems in civilian fields such as airports have gained importance in recent years and the demand for these systems has increased dramatically [1].…”

Section: Introductionmentioning

confidence: 99%

Conical Differential Range Based Back-Projection Algorithm for Concealed Object Detection with Three-Dimensional mmW Imaging

Duysak¹,

Yiğit²,

Seyfi³

2022

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Millimeter wave(mmW) imaging has spread to a wide range of applications in the last quarter. One of the most important research areas of mmW is three-dimensional (3D) imaging systems. In this study, conical differential range-based back-projection (BP) algorithm is proposed for three-dimensional mmW imaging. In the algorithm, the differential range is created using points inside a conical volume, thus the number of interpolation points is considerably reduced. The performance of the algorithm is demonstrated by simulation and experimental studies. Cylindrical scanning is carried out by means of the experimental setup. Experiments are carried out at frequencies of 26.5-40 GHz. The traditional BP algorithm (BPA) and the proposed algorithm are used to reconstruct the images. With the proposed method, it is observed that ISLR for the point target increased by about 5 dB compared to the traditional method. Moreover, the computational complexity is reduced by up to 10 times, depending on the imaging area. Thanks to the proposed method, the image of the concealed weapon under the cloth in an experimental study is more clearly focused compared to the traditional method. Therefore, it can provide images that give more accurate results for applications such as automatic target detection methods.

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Through-the-Wall Surveillance With Millimeter-Wave LFMCW Radars

Cited by 32 publications

References 23 publications

Robust Wiener filter‐based time gating method for detection of shallowly buried objects

Robust Wiener filter‐based time gating method for detection of shallowly buried objects

Attention Mechanism and Sparse Low-Rank Modeling Based Neural Network for Data Augmentation of Through-the-Wall Radar Human Motion Recognition

Conical Differential Range Based Back-Projection Algorithm for Concealed Object Detection with Three-Dimensional mmW Imaging

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