A simple strategy for moving target imaging via an experimental UWB through-wall radar

Wu, Shiyou; Tan, Kui‐Thong; Xu, Yanyun; Chen, Jie; Meng, Shengwei; Fang, Guangyou

doi:10.1109/icgpr.2012.6255002

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…due to its high-range resolution and high penetration capability [1][2][3][4][5][6][7]. Different waveforms such as short pulse signals, pseudo-random coded signals, frequency-modulated continuous wave (FMCW) signals, and stepped frequency continuous wave signals are used in UWB radar systems [8,9]. UWB impulse radar, as a form of UWB signaling, radiates ultra-narrow pulses that are inherently broadband, which can detect the micromotion of a human target.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Vital Signals for UWB Through-Wall Radar Using Low-Rank and Block-Sparse Matrix Decomposition

Liang,

Ye,

Song

et al. 2024

Remote Sensing

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Ultra-wideband (UWB) vital detection radar plays an important role in post-disaster search and rescue, but the vital signal acquired in practice is often submerged in noise. In this paper, an advanced signal processing algorithm based on low-rank block-sparse representation is proposed to enhance the vital signal in life detection radar applications. The preprocessed echo signal can be decomposed into low-rank and block-sparse parts. The alternate direction method (ADM) is employed to obtain the block-sparse part containing the desired vital signal. We solve the subproblems involved in the ADM method using the Douglas/Peaceman–Rachford (DR) monotone operator splitting method. The projection method is applied to accelerate the calculation. Simulation and experimental results show that the proposed algorithm outperforms existing methods in terms of output signal-to-noise ratio (SNR).

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Section: Introductionmentioning

confidence: 99%

Enhancement of Vital Signals for UWB Through-Wall Radar Using Low-Rank and Block-Sparse Matrix Decomposition

Liang,

Ye,

Song

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…UWB radar is widely used in ground-penetrating radar (GPR), through-wall imaging, post-earthquake rescue, and so on, due to its high range resolution and high penetration capability [1][2][3][4][5][6][7]. The frequently benefitted waveforms in UWB radar literature are short impulses [8][9][10][11], pseudo-random coded signals [12][13][14][15], frequencymodulated continuous wave (FMCW) signals [16][17][18][19][20], and stepped-frequency continuous wave (SFCW) signals [21][22][23][24]. UWB impulse radar has been increasingly adopted by researchers for human target detection due to its simple structure, low-cost, and low power consumption [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network

et al. 2022

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Ultra-wideband (UWB) impulse radar is widely used for through-wall human detection due to its high-range resolution and high penetration capability. UWB impulse radar can detect human targets in non-line-of-sight (NLOS) conditions, mainly based on the chest motion caused by human respiration. The automatic detection and extraction of multiple stationary human targets is still a challenge. Missed alarms often exist if the detection method is based on the energy of the human target. This is mainly because factors such as the range of the target, the intensity of the respiratory movement, and the shadow effect will make a difference between the energy scattered by targets. Weak targets are easily masked by strong targets and thus cannot be detected. Therefore, in this paper, a multiple stationary human targets detection method based on convolutional neural network (CNN) in through-wall UWB impulse radar is proposed. After performing the signal-to-clutter-and-noise ratio (SCNR) enhancement method on the raw radar data, the range-slow-time matrix is fed into a six-layer CNN. Benefiting from the powerful feature extraction capability of CNN, the target point of interest (TPOI) can be extracted from the data matrix. The clustering algorithm is used to simplify the TPOIs to achieve accurate detection of multiple targets behind the wall. The effectiveness of the proposed method is verified by the experimental data.

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“…Spatial filtering approach considers an array of receivers parallel to the wall and assumes that wall reflections are similar at every receiver along the array, whereas the contribution of echoes from the targets changes. Signals related to the wall can be separated from those related to the actual targets by applying a spatial frequency transform to convert the data into the new spatial frequency domain; a filter is then applied to the data to remove wall 3 reflections [16]. An alternative method isolates wall reflections from the received signal and extracts the required parameters to compute analytically the response of the wall, which is then subtracted from the actual measured data [20].…”

mentioning

confidence: 99%

“…This subtraction can be performed either on raw data (coherent subtraction) [4][5], or on images after the application of beam-forming algorithms (non-coherent subtraction) [9]. The stationary background to be subtracted from the data can be estimated with more refined methods, for instance averaging the previous received signals [2], or with suitable weights to cope with targets that stop for a while during the measurement [3].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Frequency-Modulated Interrupted Continuous Wave as Wall Removal Technique in Through-the-Wall Imaging

Fioranelli

Salous

Raimundo

2014

IEEE Trans. Geosci. Remote Sensing

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A simple strategy for moving target imaging via an experimental UWB through-wall radar

Cited by 6 publications

References 17 publications

Enhancement of Vital Signals for UWB Through-Wall Radar Using Low-Rank and Block-Sparse Matrix Decomposition

Enhancement of Vital Signals for UWB Through-Wall Radar Using Low-Rank and Block-Sparse Matrix Decomposition

Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network

Frequency-Modulated Interrupted Continuous Wave as Wall Removal Technique in Through-the-Wall Imaging

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