Concealed Dangerous Object Detection Based on a 77GHz Radar

Zhang, Zhaoyu; Di, Xin; Xu, Yong; Tian, Junfang

doi:10.1109/iwem.2018.8536660

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…The preprocessing was conducted using MATLAB R2020b on a computer with Intel i7-7700K CPU to detect potential targets, cropped the range-azimuth-elevation cubes from the generated 3D radar imaging. During preprocessing, the used hyper-parameters are summarized here: probability of false alarm in CFAR (1 × 10 −4 ), Range FFT points (256), Velocity FFT points (64), Angle FFT points for azimuth (86), Angle FFT points for elevation (16), clustering threshold ([ε r , ε v , ε a ] = [10,8,8]), cropped cube size along range, azimuth and elevation (24,24,10), and cube amplitude normalization value (1 × 10 5 ). The cropped and normalized cubes are stored at the local PC for following training and testing usage.…”

Section: Preprocessingmentioning

confidence: 99%

“…(email: xygao@uw.edu, huiliu@uw.edu, sroy@uw.edu, gxing@uw.edu, afaa97@uw.edu) Y. Luo is with the Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, 98195, USA. (email: yluo6@uw.edu) ultrasound [7], mmWave [8], Terahertz [9], infrared [10], fusion of visual RGB image and infrared [11], X-ray [12], etc.…”

Section: Introductionmentioning

confidence: 99%

“…For mmWave systems, there are two different approaches of imaging developed: passive sensing [14], [20], [21] and active sensing [8], [18], [22]- [24]. Passive mmWave imaging is done with natural illumination or with an incoherent noise source illuminating, which is good for detecting concealed weapons worn under clothes but takes a long scanning time (a few minutes) [20].…”

Section: Introductionmentioning

confidence: 99%

“…The feasibility of identifying a potential shooter carrying a concealed weapon (rifle) has been investigated in [8], [24] with 77 GHz mmWave FMCW radar. This anomaly detection is based on the micro-Doppler and range-Doppler signature analysis via deep learning.…”

Section: Introductionmentioning

confidence: 99%

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Learning to Detect Open Carry and Concealed Object with 77GHz Radar

Gao,

Liu,

Roy

et al. 2021

Preprint

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Detecting harmful carried objects plays a key role in intelligent surveillance systems and has widespread applications, for example, in airport security. In this paper, we focus on the relatively unexplored area of using low-cost 77 GHz mmWave radar for the carried objects detection problem. The proposed system is capable of real-time detecting three classes of objectslaptop, phone, and knife -under open carry and concealed cases where objects are hidden with clothes or bags. This capability is achieved by initial signal processing for localization and generating range-azimuth-elevation image cubes, followed by a deep learning-based prediction network and a multi-shot postprocessing module for detecting objects. Extensive experiments for validating the system performance on detecting open carry and concealed objects have been presented with a self-built radar-camera testbed and dataset. Additionally, the influence of different input, factors, and parameters on system performance is analyzed, providing an intuitive understanding of the system. This system would be the very first baseline for other future works aiming to detect carried objects using 77 GHz radar.

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Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning to Detect Open Carry and Concealed Object with 77GHz Radar

Gao,

Liu,

Roy

et al. 2021

Preprint

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show abstract

“…At the early stage, the application of 77 GHz band is mainly driven by the advanced driver assistance system (ADAS) [6]- [7]. Thanks to the development of highly integrated silicon germanium (SiGe) single-chip radar transceiver [8]- [9], numerous applications come true, for instance, perimeter intrusion detection, airport runway foreign object debris detection, life signs monitoring, intelligent traffic system, and so on [10]- [15]. In all the applications, range resolution and angular resolution are two important indexes of radar systems.…”

Section: Introductionmentioning

confidence: 99%

An E-Band Beam Sharpening Antenna Based on Monopulse Comparator

Teng

Wang

2021

IEEE Access

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An E-band antenna module and corresponding beam sharpening method is proposed to achieve high azimuth resolution for front-looking real aperture radar application. To achieve high angular resolution with a small antenna aperture size, a solution involving real beam and super-resolution is investigated. A monopulse antenna is developed to produce two coprime channels, which are essential for the subsequent beam sharpening in digital domain. A dual-aperture horn antenna and a monopulse comparator that make up the monopulse antenna are designed respectively, and the experimental prototypes are fabricated and tested. Experimental results show the antenna module can operate in the 75 to 80 GHz band, more than 20 dBi gain and 12 half power azimuth beamwidth can be maintained in the sum-patterns, and the null-depths are lower than -20 dB in the difference-patterns. Based on the measured sum and difference patterns, an azimuth super-resolution processing based on two-channel deconvolution is implemented. The simulation results show the sharpening ratio can achieve 12 dB with 0 dB signal-noiseratio and truncated half power beamwidth. Theoretically, the azimuth super-resolution of 0.8° can be achieved with 20 mm aperture size. The corresponding achievements are valuable in the follow-up E-band civil front-looking imaging system.INDEX TERMS E-band antenna, monopulse comparator, high angular resolution, super-resolution.

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Feature Extraction and Adaption in Micro-Doppler Image Based Concealed Dangerous Object Detection by Millimeter Wave Sensors

Zhang

et al. 2019

2019 IEEE Asia-Pacific Microwave Conference (APMC)

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Concealed Dangerous Object Detection Based on a 77GHz Radar

Cited by 5 publications

References 3 publications

Learning to Detect Open Carry and Concealed Object with 77GHz Radar

Learning to Detect Open Carry and Concealed Object with 77GHz Radar

An E-Band Beam Sharpening Antenna Based on Monopulse Comparator

Feature Extraction and Adaption in Micro-Doppler Image Based Concealed Dangerous Object Detection by Millimeter Wave Sensors

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