scholarly journals Through-Wall UWB Radar Based on Sparse Deconvolution with Arctangent Regularization for Locating Human Subjects

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2488
Author(s):  
Artit Rittiplang ◽  
Pattarapong Phasukkit
Keyword(s):  
Time Series ◽  
Human Subjects ◽  
Convolution Product ◽  
Iterative Technique ◽  
Concrete Wall ◽  
Uwb Radar ◽  
Deconvolution Problem ◽  
Reflected Signal ◽  
Reliable Solution ◽  
Human Position

A common problem in through-wall radar is reflected signals much attenuated by wall and environmental noise. The reflected signal is a convolution product of a wavelet and an unknown object time series. This paper aims to extract the object time series from a noisy receiving signal of through-wall ultrawideband (UWB) radar by sparse deconvolution based on arctangent regularization. Arctangent regularization is one of the suitably nonconvex regularizations that can provide a reliable solution and more accuracy, compared with convex regularizations. An iterative technique for this deconvolution problem is derived by the majorization–minimization (MM) approach so that the problem can be solved efficiently. In the various experiments, sparse deconvolution with the arctangent regularization can identify human positions from the noisy received signals of through- wall UWB radar. Although the proposed method is an odd concept, the interest of this paper is in applying sparse deconvolution, based on arctangent regularization with an S-band UWB radar, to provide a more accurate detection of a human position behind a concrete wall.

Through Wall Stationary Human Target Detection and Localization Using OFDM-UWB Radar

Frequenz ◽  
2016 ◽  
Vol 70 (5-6) ◽  
Author(s):  
Zohra Slimane ◽  
Abdelmalek Abdelhafid
Keyword(s):  
Target Detection ◽  
Wide Band ◽  
Human Being ◽  
Concrete Wall ◽  
Uwb Radar ◽  
Spectrum Density ◽  
Background Clutter ◽  
Detection And Localization ◽  
Minimum System ◽  
System Operating

AbstractThis paper focuses on through wall stationary human target detection and localization using an OFDM based Ultra-Wide Band radar (OFDM-UWB). Our investigations relate to a monostatic UWB radar operating in the band [1.99–3] GHz at central frequency 2.5 GHz and emitting a power of –22 dBm, meeting FCC UWB spectrum density requirements. The detection of a human being is possible due to respiratory movements of the chest. Using the short-term Fourier transform, along with the optimal filtering and an averaging technique for background clutter suppression, interesting information could be extracted from the recorded waveforms about the presence and position of a human being behind a 20-cm-thick concrete wall. The results of the experimental simulations under Matlab/simulink are then presented. A maximum range of 4 m was found to be possible with a minimum system operating SNR of 5 dB.

Model of human breathing reflected signal received by PN-UWB radar

2014 ◽  
Author(s):  
Mohamed Mabrouk ◽  
Sreeraman Rajan ◽  
Miodrag Bolic ◽  
Izmail Batkin ◽  
Hilmi R. Dajani ◽  
...  
Keyword(s):  
Uwb Radar ◽  
Reflected Signal

Multifractal analysis of central (electrocardiography) and peripheral (laser Doppler flowmetry) cardiovascular time series from healthy human subjects

2009 ◽  
Vol 30 (7) ◽  
pp. 617-629 ◽  
Author(s):  
Anne Humeau ◽  
Benjamin Buard ◽  
François Chapeau-Blondeau ◽  
David Rousseau ◽  
Guillaume Mahe ◽  
...  
Keyword(s):  
Time Series ◽  
Laser Doppler Flowmetry ◽  
Multifractal Analysis ◽  
Human Subjects ◽  
Laser Doppler ◽  
Healthy Human ◽  
Doppler Flowmetry ◽  
Healthy Human Subjects

scholarly journals Multifractal correlation using wavelet transform modulus maxima

2021 ◽  
Author(s):  
Asif Hasan Sharif
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Wavelet Transform ◽  
Human Subjects ◽  
New Method ◽  
Multiple Time ◽  
Healthy Human ◽  
Wavelet Transform Modulus Maxima ◽  
Theoretical Predictions ◽  
Modulus Maxima

The wavelet transform modulus maxima method (WTMM) for a single time series is generalized to multiple time series. The new method, which is called the joint WTMM analysis in this work, allows analyses of multifractal correlation between simultaneously measured data. Dependent, partly dependent and independent binomial cascades are used to test the joint WTMM formulism and the degree of correlation assessed qualitatively is found to agree well with the theoretical predictions. Finally, the technique is applied to simultaneously measured surface scalp potential and heart rate data taken from two healthy human subjects. Via this new method, it is shown that there is multifractal correlation between the fractal dynamics in the cortex and the autonomic regulation of the heart rate.

scholarly journals Predicting human decision making in psychological tasks with recurrent neural networks

2021 ◽  
Author(s):  
Baihan Lin ◽  
Djallel Bouneffouf ◽  
Guillermo Cecchi
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Prisoner's Dilemma ◽  
Iowa Gambling Task ◽  
Human Subjects ◽  
Prisoner’S Dilemma ◽  
Gambling Task ◽  
Iterated Prisoner's Dilemma ◽  
Human Decision ◽  
Iterated Prisoner’S Dilemma

Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions and theory of mind, i.e. what others are thinking. This makes predicting human decision making challenging to be treated agnostically to the underlying psychological mechanisms. We propose to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by the human subjects at each step of their decision making, the first application of such methods in this research domain. In this study, we collate the human data from 8 published literature of the Iterated Prisoner's Dilemma comprising 168,386 individual decisions and postprocess them into 8,257 behavioral trajectories of 9 actions each for both players. Similarly, we collate 617 trajectories of 95 actions from 10 different published studies of Iowa Gambling Task experiments with healthy human subjects. We train our prediction networks on the behavioral data from these published psychological experiments of human decision making, and demonstrate a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as the Iowa Gambling Task and multi-agent scenarios such as the Iterated Prisoner's Dilemma. In the prediction, we observe that the weights of the top performers tends to have a wider distribution, and a bigger bias in the LSTM networks, which suggests possible interpretations for the distribution of strategies adopted by each group.

scholarly journals IR-UWB Pulse Generation Using FPGA Scheme for through Obstacle Human Detection

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3750 ◽  
Author(s):  
Lalida Tantiparimongkol ◽  
Pattarapong Phasukkit
Keyword(s):  
Human Subjects ◽  
Obstacle Detection ◽  
Radar System ◽  
Pulse Generation ◽  
Human Detection ◽  
Ultra Wideband ◽  
Range Estimation ◽  
Clay Bricks ◽  
Uwb Radar ◽  
Wide Range

This research proposes a scheme of field programmable gate array (FPGA) to generate an impulse-radio ultra-wideband (IR-UWB) pulse. The FPGA scheme consists of three parts: digital clock manager, four-delay-paths stratagem, and edge combiner. The IR-UWB radar system is designed to detect human subjects from their respiration underneath the rubble in the aftermath of an earthquake and to locate the human subjects based on range estimation. The proposed IR-UWB radar system is experimented with human subjects lying underneath layers of stacked clay bricks in supine and prone position. The results reveal that the IR-UWB radar system achieves a pulse duration of 540 ps with a bandwidth of 2.073 GHz (fractional bandwidth of 1.797). In addition, the IR-UWB technology can detect human subjects underneath the rubble from respiration and identify the location of human subjects by range estimation. The novelty of this research lies in the use of the FPGA scheme to achieve an IR-UWB pulse with a 2.073 GHz (117 MHz–2.19 GHz) bandwidth, thereby rendering the technology suitable for a wide range of applications, in addition to through-obstacle detection.

scholarly journals 1-Tx/5-Rx Through-Wall UWB Switched-Antenna-Array Radar for Detecting Stationary Humans

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6828 ◽  
Author(s):  
Artit Rittiplang ◽  
Pattarapong Phasukkit
Keyword(s):  
Antenna Array ◽  
Solid Wall ◽  
Human Subjects ◽  
Ultra Wideband ◽  
Projection Algorithm ◽  
Concrete Wall ◽  
Band Frequency ◽  
Narrow Angle ◽  
Solid Objects ◽  
Human Respiration

This research proposes a through-wall S-band ultra-wideband (UWB) switched-antenna-array radar scheme for detection of stationary human subjects from respiration. The proposed antenna-array radar consists of one transmitting (Tx) and five receiving antennas (Rx). The Tx and Rx antennas are of Vivaldi type with high antenna gain (10 dBi) and narrow-angle directivity. The S-band frequency (2–4 GHz) is capable of penetrating non-metal solid objects and detecting human respiration behind a solid wall. Under the proposed radar scheme, the reflected signals are algorithmically preprocessed and filtered to remove unwanted signals, and 3D signal array is converted into 2D array using statistical variance. The images are reconstructed using back-projection algorithm prior to Sinc-filtered refinement. To validate the detection performance of the through-wall UWB radar scheme, simulations are carried out and experiments performed with single and multiple real stationary human subjects and a mannequin behind the concrete wall. Although the proposed method is an odd concept, the interest of this paper is applying the 1-Tx/5-Rx UWB switched-antenna array radar with the proposed method that is capable of distinguishing between the human subjects and the mannequin behind the concrete wall.

scholarly journals Multifractal correlation using wavelet transform modulus maxima

2021 ◽  
Author(s):  
Asif Hasan Sharif
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Wavelet Transform ◽  
Human Subjects ◽  
New Method ◽  
Multiple Time ◽  
Healthy Human ◽  
Wavelet Transform Modulus Maxima ◽  
Theoretical Predictions ◽  
Modulus Maxima

The wavelet transform modulus maxima method (WTMM) for a single time series is generalized to multiple time series. The new method, which is called the joint WTMM analysis in this work, allows analyses of multifractal correlation between simultaneously measured data. Dependent, partly dependent and independent binomial cascades are used to test the joint WTMM formulism and the degree of correlation assessed qualitatively is found to agree well with the theoretical predictions. Finally, the technique is applied to simultaneously measured surface scalp potential and heart rate data taken from two healthy human subjects. Via this new method, it is shown that there is multifractal correlation between the fractal dynamics in the cortex and the autonomic regulation of the heart rate.

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