A Novel Prescreening Method for Land-Mine Detection in UWB SAR Based on Feature Point Matching

Liu, Jun; Jin, Tian; Liang, Fuyuan; Zhou, Zhimin

doi:10.1109/tgrs.2012.2220552

Cited by 14 publications

(11 citation statements)

References 48 publications

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“…The detected pixel is determined to be part of a human target when the amplitude exceeds the threshold, which can be defined as [31]:

{\begin{matrix} \bar{I} (i, j) \geq T_{C F A R} (i, j) belongs to vital sign \\ \bar{I} (i, j) < T_{C F A R} (i, j) does not belong to vital sign \end{matrix}

where

T_{C F A R}

denotes the threshold of CFAR.…”

Section: Automatic Detection Methodsmentioning

confidence: 99%

Detection of Multiple Stationary Humans Using UWB MIMO Radar

Liang

et al. 2016

Sensors

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Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls.

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“…The detected pixel is determined to be part of a human target when the amplitude exceeds the threshold, which can be defined as [31]:

{\begin{matrix} \bar{I} (i, j) \geq T_{C F A R} (i, j) belongs to vital sign \\ \bar{I} (i, j) < T_{C F A R} (i, j) does not belong to vital sign \end{matrix}

where

T_{C F A R}

denotes the threshold of CFAR.…”

Section: Automatic Detection Methodsmentioning

confidence: 99%

Detection of Multiple Stationary Humans Using UWB MIMO Radar

Liang

et al. 2016

Sensors

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“…Synthetic aperture Radar (SAR) is another powerful sensor for target detection, which is capable of obtaining a high-resolution image of the targets and their surrounding areas. With the development of SAR techniques including hardware, waveform design, and imaging algorithm, the resolution and imaging quality have been greatly improved in past decades [3][4][5][6]. The ultrawideband synthetic aperture radar (UWB SAR) has become an important means for detecting landmines.…”

Section: Introductionmentioning

confidence: 99%

Lightweight CFARNets for Landmine Detection in Ultrawideband SAR

Zhang,

Song,

Jin

2023

Remote Sensing

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The high-resolution image obtained by ultrawideband synthetic aperture radar (UWB SAR) includes rich features such as shape and scattering features, which can be utilized for landmine discrimination and detection. Due to the high performance and automatic feature learning ability, deep network-based detection methods have been widely employed in SAR target detection. However, existing deep networks do not consider the target characteristics in SAR images, and their structures are too complicated. Therefore, lightweight deep networks with efficient and interpretable blocks are essential. This work investigates how to utilize the SAR characteristics to design a lightweight deep network. The widely employed constant false alarm rates (CFAR) detector is used as a prototype and transformed into trainable multiple-feature network filters. Based on CFAR filters, we propose a new class of networks called CFARNets which can serve as an alternative to convolutional neural networks (CNNs). Furthermore, a two-stage detection method based on CFARNets is proposed. Compared to prevailing CNNs, the complexity and number of parameters of CFARNets are significantly reduced. The features extracted by CFARNets are interpretable as CFAR filters have definite physical significance. Experimental results show that the proposed CFARNets have comparable detection performance compared to other real-time state-of-the-art detectors but with faster inference speed.

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“…In recent years, UWB-SAR has played an important role in terms of battlefield observation based on its ultra-high range and azimuth resolution (Vu, Sjogren, & Pettersson, 2010. UWB-SAR, which works at the ultra high frequency/very high frequency (UHF/VHF) band, could penetrate foliage and ground to detect and identify concealed or hidden targets though high-resolution image (Lou, Jin, Liang, & Zhou, 2013). The Twin-Otter SAR of Sandia Laboratory using linear frequency modulation (LFM) signal works at Ka-, Ku-, X-and VHF/UHF band and uses changed bandwidth from 125 to 950 MHz, which could obtain a 2-10 m resolution image when the UWB-SAR works at stripmap mode or a 1-3 m resolution image when the working mode is spotlight (Walker et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

Deceptive jamming for countering UWB-SAR based on Doppler frequency phase template of false target

Tang

2015

International Journal of Electronics

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A false target deceptive jamming method for countering ultra-wideband synthetic aperture radar (UWB-SAR) is proposed in this paper, which is based on dechirp processing to intercepted UWB-SAR signal and inverse dechirp to jamming signal. The jammer quadrature down-converts and dechirps the intercepted UWB-SAR signal using a linear frequency modulation (LFM) signal oscillator, which could reduce the bandwidth and sample rate of analog-to-digital converter. Then, the jammer utilises the azimuth direction Doppler frequency phase between the false target and the jammer, and backward reflection coefficient template to modulate the phase of the intercepted UWB-SAR signal, and then delayed the modulated phase and also modulated the range direction Doppler frequency phase to the that. Finally, the jammer uses LFM signal oscillator to up-convert the narrowband jamming signal in order to recover the bandwidth of the signal. Parameter errors analysis and simulation results have shown that the detected parameters and motion characteristic errors reduce the resolution and offset the expected position of the false target, but it still could obtain an expected false target image. Theoretical analysis and simulation results indicated that the jamming signal proposed in this paper could produce a false target in the UWB-SAR image, which provide a feasible method for countering UWB-SAR in real time.

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A Novel Prescreening Method for Land-Mine Detection in UWB SAR Based on Feature Point Matching

Cited by 14 publications

References 48 publications

Detection of Multiple Stationary Humans Using UWB MIMO Radar

Detection of Multiple Stationary Humans Using UWB MIMO Radar

Lightweight CFARNets for Landmine Detection in Ultrawideband SAR

Deceptive jamming for countering UWB-SAR based on Doppler frequency phase template of false target

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