Ultra-wideband Impulse Radar Research Articles

A Novel Method for the Detection of External Insulation Defects Based on the Ultrawideband Impulse Radar Technology

A Novel Method for the Detection of External Insulation Defects Based on the Ultrawideband Impulse Radar Technology

Real-Time Permittivity Extraction Based on Ultra-Wideband Impulse Radar and Genetic Algorithm for Tunnel Construction Operations

Real-Time Permittivity Extraction Based on Ultra-Wideband Impulse Radar and Genetic Algorithm for Tunnel Construction Operations

Variable Length Sequential Iterable Convolutional Recurrent Network for UWB-IR Vehicle Target Recognition

A variable length sequential iterable convolutional recurrent network (VS-ICRN) is proposed in this paper, aiming at improving the vehicle target recognition ability for the Ultra-Wideband Impulse Radar (UWB-IR). Firstly, the array imaging technology is introduced into the UWB-IR, and thus a range-angle imaging method for the array UWB-IR is put forward, to simulate the array UWB-IR vehicles image under different observation conditions. Secondly, in order to make full use of both the deep features in the single image and the deep associated features between the sequence images, a VS-ICRN model is proposed, which includes three sub-modules: the image feature extraction based on the iterable convolution, the variable length sequential image associated feature extraction and the target classification, respectively. Finally, the experiment on the simulation dataset and the MSATAR is carried out to validate the effectiveness of the proposed method. The experimental results on the simulation dataset show that when SNR=-10dB, the proposed method is superior in the recognition rate to the GoogLeNet and AlexNet methods with 16% and 19%, respectively. Meanwhile, the proposed VS-ICRN method only needs 1.38% parameters quantity to achieve a comparable recognition rate as GoogLeNet on MSTAR dataset.

Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network

Ultra-wideband (UWB) impulse radar is widely used for through-wall human detection due to its high-range resolution and high penetration capability. UWB impulse radar can detect human targets in non-line-of-sight (NLOS) conditions, mainly based on the chest motion caused by human respiration. The automatic detection and extraction of multiple stationary human targets is still a challenge. Missed alarms often exist if the detection method is based on the energy of the human target. This is mainly because factors such as the range of the target, the intensity of the respiratory movement, and the shadow effect will make a difference between the energy scattered by targets. Weak targets are easily masked by strong targets and thus cannot be detected. Therefore, in this paper, a multiple stationary human targets detection method based on convolutional neural network (CNN) in through-wall UWB impulse radar is proposed. After performing the signal-to-clutter-and-noise ratio (SCNR) enhancement method on the raw radar data, the range-slow-time matrix is fed into a six-layer CNN. Benefiting from the powerful feature extraction capability of CNN, the target point of interest (TPOI) can be extracted from the data matrix. The clustering algorithm is used to simplify the TPOIs to achieve accurate detection of multiple targets behind the wall. The effectiveness of the proposed method is verified by the experimental data.

Non-contact measurement of respiration and heart rates based on subspace methods and iterative notch filter using UWB impulse radar

This paper treats the problem of non-contact vital signs (VSs) estimation using ultra wideband (UWB) impulse radar. Specifically, we focus on the simultaneous estimation of respiration and heart rates, by adapting high resolution subspace methods (namely, root-MUSIC and ESPRIT) in order to obtain an accurate estimation of the chest velocity produced by VS motions. As a by product, we overcome the nuisance effect of the noise, the clutter, and the body motion. Then, we propose the use of an iterative notch filter (INF) to clean up the respiratory signal and its harmonics with the intention of estimating the heart rate in an accurate manner. The performance of the proposed method is analyzed in different scenarios, with simulated data, human VS activities, and mechanical vibrating structure using a PulsON 440 (P440) module. Finally, numerical simulations and experimental results reveal that the proposed method outperforms the state-of-the-art.

A Method for Reducing Timing Jitter’s Impact in Through-Wall Human Detection by Ultra-Wideband Impulse Radar

Ultra-wideband (UWB) impulse radar is widely used for through-wall human respiration detection due to its high range resolution and high penetration capability. UWB impulse radar emits very narrow time pulses, which can directly obtain the impulse response of the target. However, the time interval between successive pulses emitted is not ideally fixed because of timing jitter. This results in the impulse response position of the same target not being fixed, but it is related to slow-time. The clutter scattered by the stationary target becomes non-stationary clutter, which affects the accurate extraction of the human respiration signal. In this paper, we propose a method for reducing timing jitter’s impact in through-wall human detection by UWB impulse radar. After the received signal is processed by the Fast Fourier transform (FFT) in slow-time, we model the range-frequency matrix in the frequency domain as a superposition of the low-rank representation of jitter-induced clutter data and the sparse representation of human respiratory data. By only extracting the sparse component, the impact of timing jitter in human respiration detection can be reduced. Both numerical simulated data and experimental data demonstrate that our proposed method can effectively remove non-stationary clutter induced by timing jitter and improve the accuracy of the human target signal extraction.

Fast and cost-effective method for non-contact respiration rate tracking using UWB impulse radar

Breathing rate monitoring for a long period provides a valuable indicator about human health and an early warning of possible disasters on the well-being. To gain a comfortable and adequate measurement without restricting the person's privacy, this study investigates a novel non-contact-based solution using ultra wide-band (UWB) impulse radar. We propose a fast and low-cost method for breathing events detection and respiration rate (RR) tracking. This novel approach consists of three main parts: vital signs (VS) patterns extraction based on the Generalized Goertzel Algorithm (GGA) avoiding the prior knowledge of the person's location and overcoming the effect of the clutter. RR tracking built on the complex adaptive notch filter (CANF) with a variable step size to obtain a robust estimation under the presence of sparse random body movement (SRBM) and fast tracking of the breath variability. Finally, we introduce a control chart of the breathing process using the double exponential moving average (DEMA) in order to detect the apnea events. The proposed method is evaluated using two sets of experiments, from mechanical vibrating structure to human subject. The results demonstrate that the proposed system is a promising and effective solution and provides wide usability for continuous monitoring applications. Moreover, it outperforms the state-of-the-art, in terms of computation cost and space complexity.

Investigation on the chipless RFID tag with a UWB pulse using a UWB IR-based reader

AbstractIn order to encrypt/encode data based on the magnitude level of the radar cross-section (RCS), we propose an approach with a precise estimation considering the resonant characteristics of a multipatch backscatter-based chipless radio frequency identification (RFID) dedicated for chipless tags depolarization. The working principle is based on the polarization mismatch between the tag and the reader antenna to control the magnitude of the backscatter, which allows a reliable detection in real environments. We introduce in this paper a new 4-bit chipless RFID tag with an enhanced RCS, based on a triangular patch antenna with multiple resonators. Additionally, we propose an ultra-wideband impulse radar (UWB-IR)-based reader that interrogates the chipless tag with a UWB pulse, and the received backscatter was studied in both time- and frequency-domains. The antenna was operating from 4.7 to 6.1 GHz, a band allocated for RFID systems. The obtained experimental measurement results in the environment of anechoic chamber were exceptionally relevant to validate the simulation results.

Multivariate singular spectral analysis for heartbeat extraction in remote sensing of uwb impulse radar

Remote sensing of the heartbeat signal is an important task in a smart home and smart hospital. Ultra-wideband impulse (UWB) radar is recently noticed as a promising and potential solution for the task. In this paper, we propose a method based on multivariate singular spectral analysis (MSSA) for efficient extraction of heartbeat signal in the mixing with respiration and noise signals. The proposed method takes into account the correlation of the harmonic components of the multivariate input signals; and, thus, it provides a solution for the extraction of the heartbeat signal in an efficient way. Experiments were performed with different people, different orientations, and various distances from 0.5m to 3.0m. The experiment results showed that the proposed MSSA method could improve heartbeat signals with an average of 40dB signal-to-noise ratio (SNR). And, it outperformed the CWT and SSA methods with averages of 32dB and 18dB SNR, respectively. The accuracy of measurement of the heart rates by the proposed MSSA method was 3.44 % (0.044Hz) of standard deviation error compared with a reference photoplethysmography (PPG).

Deep-Learning Methods for Hand-Gesture Recognition Using Ultra-Wideband Radar

Using deep-learning techniques for analyzing radar signatures has opened new possibilities in the field of smart-sensing, especially in the applications of hand-gesture recognition. In this paper, we present a framework, using deep-learning techniques, to classify hand-gesture signatures generated from an ultra-wideband (UWB) impulse radar. We extract the signals of 14 different hand-gestures and represent each signature as a 3-dimensional tensor consisting of range-Doppler frame sequence. These signatures are passed to a convolutional neural network (CNN) to extract the unique features of each gesture, and are then fed to a classifier. We compare 4 different classification architectures to predict the gesture class, namely; (i) fully connected neural network (FCNN), (ii) k-Nearest Neighbours (k-NN), (iii) support vector machine (SVM), (iv) long short term memory (LSTM) network. The shape of the range-Doppler-frame tensor and the parameters of the classifiers are optimized in order to maximize the classification accuracy. The classification results of the proposed architectures show a high level of accuracy above 96 % and a very low confusion probability even between similar gestures.

Detection of Multiple Humans Equidistant From IR-UWB SISO Radar Using Machine Learning

In order to locate humans unconscious under rubble, we generally use an ultrawideband impulse radar to capture the chest displacement caused by breathing. Sometimes, cases may arise when more than one subject falls in the field of view of the radar antenna and are located equidistant from it. At that time, it becomes impossible for the radar to count the actual number of people present. Thus, we created a scenario under laboratory conditions where we placed three subjects equidistant from the radar with no obstruction in between. Then, we applied supervised machine learning algorithms to classify and predict the number of subjects using variance and singular values as the features. We found that the ensemble bagged trees classifier gave the most accurate classification at 97.5 $\%$ .

Heart rate extraction based on eigenvalues using UWB impulse radar remote sensing

Ultra wideband impulse (UWB) radar for vital signs measurement applications is getting much attention because it is a contactless method. However, the heartbeat signal is usually be perturbed in strong noisy signals from stationary and non-stationary objects. In addition, the respiration signal, which has a much higher intensity than the heartbeat signal, makes it challenging to extract the heartbeat signal. Thus, this paper proposes an extraction method of heartbeat signal and frequency using eigenvalues. Eigenvalues of the vital signal were found to be strongly correlated with the fundamental and harmonic frequencies of respiration and heartbeat. Experimental results show that the proposed method could improve the signal-to-noise ratio of over 18 dB for different distances from 0.5 m to 3.0 m and different orientations of the person. Furthermore, the original signal of respiration and heartbeat can be reconstructed by the selection of the eigenvalues and the eigenvectors.

Advanced system‐level simulation paradigm for ultra‐wideband systems using SCERNE platform

In this article, a progressive system-level simulation framework is developed based on Simulation de Chaines d'Emission/Reception Nouvelle gEneration (SCERNE) platform to simulate an ultra-wideband (UWB) impulse radar transmitter and accurately predict its performance. With the purpose of demonstrating the usefulness of

Multiresonance Chipless RFID Sensor Tag for Metal Defect Characterization Using Principal Component Analysis

One of the challenges in structural health monitoring (SHM) is that defects, such as metal cracks and corrosions, can occur with multiple material properties and multilayer structure, entangled with influences from the reader system and the environment. This paper presents a multiresonance chipless radio frequency identification (RFID) sensor tag and the chipless RFID sensor reader applying principal component analysis (PCA) for multiparameter estimation in metal defect characterization. The proposed sensor tag is composed of the crossed diagonal dipole patches and the L-shaped patches generating six resonance peaks within 2-6-GHz band. The frequency signature of the sensor tag is acquired and processed by a chipless RFID reader implemented using an ultra-wideband impulse radar (UWB-IR) transceiver module. We demonstrate that the multiresonance chipless RFID sensor tag in conjunction with PCA-based feature extraction and selection can be applied for the characterization of metal crack with different widths and orientations. Furthermore, the proposed technique can find a robust feature indicating corrosion stages under a coating layer regardless of the sample orientation. This paper paves the way for multiparameter defect sensing and monitoring using a chipless RFID sensor system.

Listen to your heart

listen to your heart

Monitoring heart activity using ultra‐wideband radar

This Letter focuses on extracting the heartbeat information using ultra-wideband impulse radar, and a new method that can extract not only the heart rate but also the timing of the heartbeat with high accuracy is proposed herein. As the first step towards achieving this goal, radar pulses are projected towards a human body, and the reflected pulses are accumulated along the time axis at specific time intervals. The accumulated pulses pass through a digital filter to reveal the heartbeat information and are converted to a greyscale image. A pattern that reflects the heartbeats is used as a template to find similar patterns in the entire image. After all the significant patterns are found, the timing information corresponding to the locations where the patterns appear is calculated, and this indicates the time when the heartbeats actually occur. From the experimental results, the authors confirmed that the proposed method could estimate the heart rate with high accuracy and extract the timing information of the heartbeats.

Person Identification Using Micro-Doppler Signatures of Human Motions and UWB Radar

As a typical task of passive biometrics, behavior-based person identification has been studied extensively in recent years. This letter proposes the use of the ultrawideband impulse radar for person identification based upon the micro-Doppler signatures of human motions. A new convolutional neural network (CNN) architecture is proposed for taking advantage of the hierarchical features. The experimental results show that, by utilizing the micro-Doppler signatures of the six selected human motions, the task of person identification can be accurately achieved. Both traditional algorithms and landmark CNN algorithms are chosen for comparison, and the proposed model performs better than the others. Especially when the motion of “running” is adopted to identify persons, the model achieves 95.21% accuracy on the identification of 15 people.

Remote sensing of vital signs using an ultra-wide-band radar

ABSTRACT Remote vital signs monitoring and tracking are of great importance for various applications including but not limited to baby monitoring, elderly care, human activity behind barriers, bio-medical and more. This paper deals with the extraction of micro-Doppler (uDoppler) signatures of a person’s respiration and heart-beat using a single transmitter (TX), single receiver (RX) Ultra Wide Band (UWB) impulse radar. The received spectrum contains the respiration and heart fundamental beat frequencies and their corresponding integer multiplicity harmonies as well as their mutual inter-modulations that appears due to the micro-Doppler frequency-modulation of the sinusoidal nature of the moving torso and heart, a fact that poses a difficulty in extracting the correct heart-beat fundamental harmony. Moreover, the commonly used Discrete Fourier Transform (DFT) is insufficient for differentiating close proximity spectral components, thus, an adaptive, real-time, novel, spectral estimation algorithm is proposed and experimental results are given.

Improved ranging method for life detection using ultra‐wide band impulse radar

This study focuses on range estimate for life detection based on the characteristics analysis of life signs by employing the ultra-wideband (UWB) impulse radar. Via analysing the standard deviation of life signs, the time of arrival (TOA) of the pulse from the radar receiver to detection target is estimated based on the calculated energy blocks. Based on the obtained TOA estimates, one area containing life information is determined, which is referred to as region of interest (ROI). Using the signals in ROI, the frequency of human breathing motion is measured more easily and accurately. Different experiments are carried out to test the performance of the new method in through-wall conditions. Results show perfect capability of detection and improving signal-to-noise ratio.

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

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.