UWB Radar for Human Being Detection

Yarovoy, Alexander; Ligthart, L.P.; Matuzas, J.; Levitas, B.

doi:10.1109/maes.2006.284354

Cited by 111 publications

(29 citation statements)

References 5 publications

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“…For fairness, we also set the sampling rate in the two systems to the same value, )* ,+7 = )* +,+-. Hence, the number of input nodes in the MISO-IMG-LiDAL ANN, 23 ,+7 , is…”

Section: Neural Network For Lidal Systemsmentioning

confidence: 99%

“…where is the number of output neurons ( = $ 15 = )/01 for the MISO-IMG-LiDAL and MIMO-LiDAL systems respectively), 23 is the number of the input neurons (given by 23 ,+7 or 23 +,+for the MISO-IMG-LiDAL and MIMO-LiDAL systems respectively) and is an arbitrary pruning factor (here ∈ 1,2. . 23 [44]). The training process uses the following steps:…”

Section: A the Neural Network Training Processmentioning

confidence: 99%

“…Ultra-wideband (UWB) RADAR systems with a transmitted signal bandwidth greater than 500 MHz have been introduced to detect, localise and track humans in the indoor environment [20], [22], [23]. The UWB carrier signal with a typical frequency range of 3.1GHz to 5.3 GHz can penetrate walls, furniture and human body [24].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network for LiDAL Systems

et al. 2019

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In this paper, we introduce an intelligent light detection and localization (LiDAL) system that uses artificial neural networks (ANN). The LiDAL systems of interest are MIMO LiDAL and MISO IMG LiDAL systems. A trained ANN with the LiDAL system of interest is used to distinguish a human (target) from the background obstacles (furniture) in a realistic indoor environment. In the LiDAL systems, the received reflected signals in the time domain have different patterns corresponding to the number of targets and their locations in an indoor environment. The indoor environment with background obstacles (furniture) appears as a set of patterns in the time domain when the transmitted optical signals are reflected from objects in LiDAL systems. Hence, a trained neural network that has the ability to classify and recognize the received signal patterns can distinguish the targets from the background obstacles in a realistic environment. The LiDAL systems with ANN are evaluated in a realistic indoor environment through computer simulation.

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“…For fairness, we also set the sampling rate in the two systems to the same value, )* ,+7 = )* +,+-. Hence, the number of input nodes in the MISO-IMG-LiDAL ANN, 23 ,+7 , is…”

Section: Neural Network For Lidal Systemsmentioning

confidence: 99%

Section: A the Neural Network Training Processmentioning

confidence: 99%

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Artificial Neural Network for LiDAL Systems

et al. 2019

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“…Typically, most UWB radars transmit via large bandwidth over short pulse periods, usually on the order of a nanosecond, or even a picosecond; we generally refer to this type of UWB signalling as impulse radio UWB (IR-UWB) radar [6,7,8]. This has gained popularity in social and military applications in through-wall imaging, ground penetrating radar, detection of moving targets, and so on owing to its high penetrability and high range resolution [9].…”

Section: Introductionmentioning

confidence: 99%

Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

Jin

2016

Sensors

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The radar sensor described realizes healthcare monitoring capable of detecting subject chest-wall movement caused by cardiopulmonary activities and wirelessly estimating the respiration and heartbeat rates of the subject without attaching any devices to the body. Conventional single-tone Doppler radar can only capture Doppler signatures because of a lack of bandwidth information with noncontact sensors. In contrast, we take full advantage of impulse radio ultra-wideband (IR-UWB) radar to achieve low power consumption and convenient portability, with a flexible detection range and desirable accuracy. A noise reduction method based on improved ensemble empirical mode decomposition (EEMD) and a vital sign separation method based on the continuous-wavelet transform (CWT) are proposed jointly to improve the signal-to-noise ratio (SNR) in order to acquire accurate respiration and heartbeat rates. Experimental results illustrate that respiration and heartbeat signals can be extracted accurately under different conditions. This noncontact healthcare sensor system proves the commercial feasibility and considerable accessibility of using compact IR-UWB radar for emerging biomedical applications.

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“…And the breathing and heart rate distortions have been gained within 8-cm-thick wall obstacles [9]. In [10] and [11], A.G. Yarovoy has investigated the detection and positioning of human in complex environment with UWB radar. And it has been shown that the range to a person varies within 0.6 cm because of breathing, and a novel motion or breathing detector has been presented on the basis of the measurements of radar return spatial variations.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in big data from UWB radars

Wang

Zhou

Zhang

et al. 2013

Security Comm Networks

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The definitions of big data and anomaly detection are presented. The theory of ultra-wideband radar and the throughwall detection of a human model based on ultra-wideband radar are briefly introduced. The target criterion with wavelet packet transform is deduced, and the procedure for the through-wall human detection with statistical process control is constructed. The radar echo signals are collected at stationary and moving statuses of a human being for three types of walls. The experimental results demonstrate the effective of through-wall target detection based on the proposed algorithm.

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UWB Radar for Human Being Detection

Cited by 111 publications

References 5 publications

Artificial Neural Network for LiDAL Systems

Artificial Neural Network for LiDAL Systems

Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

Anomaly detection in big data from UWB radars

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