Improved human respiration detection method via ultra‐wideband radar in through‐wall or other similar conditions

Wu, Shiyou; Tan, Kui‐Thong; Xia, Zhenghuan; Chen, Jie; Meng, Shengwei; Fang, Guangyou

doi:10.1049/iet-rsn.2015.0159

Cited by 49 publications

(22 citation statements)

References 25 publications

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“…In this subsection, three algorithms are employed as references, including the constant false alarm ratio (CFAR) algorithm [ 45 ], advanced method (AM) [ 46 ], and the multiple higher order cumulant (MHOC) method [ 47 ] to validate the performances of the developed method.…”

Section: Resultsmentioning

confidence: 99%

Experimental Study of Wireless Monitoring of Human Respiratory Movements Using UWB Impulse Radar Systems

Liang¹,

Wang²,

et al. 2018

Sensors

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This paper analyzes and discusses the capability of human being detection using impulse ultra-wideband (UWB) radar with an improved detection algorithm. The multiple automatic gain control (AGC) technique is employed to enhance the amplitudes of human respiratory signals. Two filters with seven values averaged are used to further improve the signal-to-noise ratio (SNR) of the human respiratory signals. The maximum slope and standard deviation are used for analyzing the characteristics of the received pulses, which can provide two distance estimates for human being detection. Most importantly, based on the two distance estimates, we can accurately judge whether there are human beings in the detection environments or not. The data size can be reduced based on the defined interested region, which can improve the operation efficiency of the radar system for human being detection. The developed algorithm provides excellent performance regarding human being detection, which is validated through comparison with several well-known algorithms.

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Section: Resultsmentioning

confidence: 99%

Experimental Study of Wireless Monitoring of Human Respiratory Movements Using UWB Impulse Radar Systems

Liang¹,

Wang²,

et al. 2018

Sensors

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“…The first category suggests either operating at very high frequencies (e.g., millimeter wave or terahertz) [22] which do not penetrate walls or using the SAR (synthetic aperture radar) algorithm [23]. The second category proposes to extract the specific features of the target such as vital signs [24][25][26] or movement characteristics [27][28][29] of the target. Micro-Doppler signatures [30] are commonly used to detect vital signs or to classify the target's specific activities such as running, walking, or even falling.…”

Section: Introductionmentioning

confidence: 99%

Through-Wall Radar Classification of Human Posture Using Convolutional Neural Networks

Kilic

Babaoğlu

Babalik

et al. 2019

International Journal of Antennas and Propagation

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Through-wall detection and classification are highly desirable for surveillance, security, and military applications in areas that cannot be sensed using conventional measures. In the domain of these applications, a key challenge is an ability not only to sense the presence of individuals behind the wall but also to classify their actions and postures. Researchers have applied ultrawideband (UWB) radars to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. As a form of UWB radar, stepped frequency continuous wave (SFCW) radars have been preferred due to their advantages. On the other hand, the success of classification with deep learning methods in different problems is remarkable. Since the radar signals contain valuable information about the objects behind the wall, the use of deep learning techniques for classification purposes will give a different direction to the research. This paper focuses on the classification of the human posture behind the wall using through-wall radar signals and a convolutional neural network (CNN). The SFCW radar is used to collect radar signals reflected from the human target behind the wall. These signals are employed to classify the presence of the human and the human posture whether he/she is standing or sitting by using CNN. The proposed approach achieves remarkable and successful results without the need for detailed preprocessing operations and long-term data used in the traditional approaches.

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“…UWB radar also finds application in through-the-wall detection of human subjects, imaging, detection of moving targets, etc. UWB radars for detection of vital signs of a human subject were discussed in [5][6][7][8][9][10]. Signal processing plays a very important role in vital sign detection using Doppler radar.…”

Section: Introductionmentioning

confidence: 99%

“…Signal processing plays a very important role in vital sign detection using Doppler radar. Various algorithms such as parameterized demodulation, singular value decomposition (SVD), adaptive cancellation of clutter, constant false alarm ratio (CFAR) and correlation were proposed to extract vital signs from the received noisy signal [7][8][9][10][11]. But these signal processing algorithms do not separate the vital signs of multiple subjects.…”

Section: Introductionmentioning

confidence: 99%

Through the Wall Respiration Rate Detection of Multiple Human Subjects Using Hilbert Vibrational Decomposition

Harikesh¹,

Basu²,

Abegaonkar³

et al. 2019

PIER M

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In this paper, through-the-wall detection of multiple human subjects is demonstrated using Doppler radar, with the help of Hilbert Vibrational Decomposition (HVD). The proposed Doppler radar can detect multiple human subjects behind a wall by respiration rate estimation. With different breathing conditions resembling real-life scenario, respiration signals of human subjects are extracted. The algorithm works in many real-life situations and detects the respiration rate of human subjects. It is expected to find use in the earthquakes, building collapses and security applications.

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Improved human respiration detection method via ultra‐wideband radar in through‐wall or other similar conditions

Cited by 49 publications

References 25 publications

Experimental Study of Wireless Monitoring of Human Respiratory Movements Using UWB Impulse Radar Systems

Experimental Study of Wireless Monitoring of Human Respiratory Movements Using UWB Impulse Radar Systems

Through-Wall Radar Classification of Human Posture Using Convolutional Neural Networks

Through the Wall Respiration Rate Detection of Multiple Human Subjects Using Hilbert Vibrational Decomposition

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