Portable UWB RADAR Sensing System for Transforming Subtle Chest Movement into Actionable Micro-Doppler Signatures to Extract Respiratory Rate Exploiting ResNet Algorithm

Contactless or non-invasive technology for the monitoring of anomalies in an inconspicuous and distant environment has immense significance in health-related applications, in particular COVID-19 symptoms detection, diagnosis, and monitoring. Contactless methods are crucial specifically during the COVID-19 epidemic as they require the least amount of involvement from infected individuals as well as healthcare personnel. According to recent medical research studies regarding coronavirus, individuals infected with novel COVID-19-Delta variant undergo elevated respiratory rates due to extensive infection in the lungs. This appalling situation demands constant real-time monitoring of respiratory patterns, which can help in avoiding any pernicious circumstances. In this paper, an Ultra-Wideband RADAR sensor “XeThru X4M200” is exploited to capture vital respiratory patterns. In the low and high frequency band, X4M200 operates within the 6.0-8.5 GHz and 7.25-10.20 GHz band, respectively. The experimentation is conducted on six distinct individuals to replicate a realistic scenario of irregular respiratory rates. The data is obtained in the form of spectrograms by carrying out normal (eupnea) and abnormal (tachypnea) respiratory. The collected spectrogram data is trained, validated, and tested using a cutting-edge deep learning technique called Residual Neural Network or ResNet. The trained ResNet model’s performance is assessed using the confusion matrix, precision, recall, F1-score, and classification accuracy. The unordinary skip connection process of the deep ResNet algorithm significantly reduces the underfitting and overfitting problem, resulting in a classification accuracy rate of up to 90%

Multi Detector Fusion of Dynamic TOA Estimation using Kalman Filter

In this paper, we propose fusion of dynamic TOA (time of arrival) from multiple non-coherent detectors like energy detectors operating at sub-Nyquist rate through Kalman filtering. We also show that by using multiple of these energy detectors, we can achieve the performance of a digital matched filter implementation in the AWGN (additive white Gaussian noise) setting. We derive analytical expression for number of energy detectors needed to achieve the matched filter performance. We demonstrate in simulation the validity of our analytical approach. Results indicate that number of energy detectors needed will be high at low SNRs and converge to a constant number as the SNR increases. We also study the performance of the strategy proposed using IEEE 802.15.4a CM1 channel model and show in simulation that two sub-Nyquist detectors are sufficient to match the performance of digital matched filter

Multiple Person Localization Based on Their Vital Sign Detection Using UWB Sensor

In the past period, great efforts have been made to develop methods for through an obstacle detection of human vital signs such as breathing or heart beating. For that purpose, ultra-wideband (UWB) radars operating in the frequency band DC-5 GHz can be used as a proper tool. The basic principle of respiratory motion detection consists in the identification of radar signal components possessing a significant power in the frequency band 0.2–0.7 Hz (frequency band of human respiratory rate) corresponding to a constant bistatic range between the target and radar. To tackle the task of detecting respiratory motion, a variety of methods have been developed. However, the problem of person localization based on his or her respiratory motion detection has not been studied deeply. In order to fill this gap, an approach for multiple person localization based on the detection of their respiratory motion will be introduced in this chapter

A Unified Multi-Functional Dynamic Spectrum Access Framework: Tutorial, Theory and Multi-GHz Wideband Testbed

Dynamic spectrum access is a must-have ingredient for future sensors that are ideally cognitive. The goal of this paper is a tutorial treatment of wideband cognitive radio and radar—a convergence of (1) algorithms survey, (2) hardware platforms survey, (3) challenges for multi-function (radar/communications) multi-GHz front end, (4) compressed sensing for multi-GHz waveforms—revolutionary A/D, (5) machine learning for cognitive radio/radar, (6) quickest detection, and (7) overlay/underlay cognitive radio waveforms. One focus of this paper is to address the multi-GHz front end, which is the challenge for the next-generation cognitive sensors. The unifying theme of this paper is to spell out the convergence for cognitive radio, radar, and anti-jamming. Moore’s law drives the system functions into digital parts. From a system viewpoint, this paper gives the first comprehensive treatment for the functions and the challenges of this multi-function (wideband) system. This paper brings together the inter-disciplinary knowledge

FMCW Signals for Radar Imaging and Channel Sounding

A linear / stepped frequency modulated continuous wave (FMCW) signal has for a long time been used in radar and channel sounding. A novel FMCW waveform known as “Gated FMCW” signal is proposed in this thesis for the suppression of strong undesired signals in microwave radar applications, such as: through-the-wall, ground penetrating, and medical imaging radar. In these applications the crosstalk signal between antennas and the reflections form the early interface (wall, ground surface, or skin respectively) are much stronger in magnitude compared to the backscattered signal from the target. Consequently, if not suppressed they overshadow the target’s return making detection a difficult task. Moreover, these strong unwanted reflections limit the radar’s dynamic range and might saturate or block the receiver causing the reflection from actual targets (especially targets with low radar cross section) to appear as noise. The effectiveness of the proposed waveform as a suppression technique was investigated in various radar scenarios, through numerical simulations and experiments. Comparisons of the radar images obtained for the radar system operating with the standard linear FMCW signal and with the proposed Gated FMCW waveform are also made. In addition to the radar work the application of FMCW signals to radio propagation measurements and channel characterisation in the 60 GHz and 2-6 GHz frequency bands in indoor and outdoor environments is described. The data are used to predict the bit error rate performance of the in-house built measurement based channel simulator and the results are compared with the theoretical multipath channel simulator available in Matlab

Ultra-Wideband Radar Based Human Motion Analysis

This thesis proposes and investigates two techniques in ultra-wideband (UWB) radar based human motion analysis. The first one is accurate human body landmark detection using UWB radars. The detection is achieved by moving target indication (MTI) and constant false alarm rate detection (CFAR). A new CFAR detection technique is proposed, namely the out-of-band (OB) CFAR detection. In the field experiment, two RF reflective markers are attached to the wrist and elbow of one human arm for reflecting radar signals. It is demonstrated that detection of two markers are feasible and successfully achieved. And our results suggests the OB-CFAR performs better than conventional CFAR in landmark detection. The second technique aims to study on the human motion classification through the exploitation of video and radar data, respectively. Motion history image (MHI) and Hu moment method are applied to extract temporal features from video clips. Principal component analysis (PCA) is used to obtain radar detection [signatures]. We use k-means clusters to quantize the observation feature vectors. Hidden Markov models (HMMs) are trained with the features extracted from both video and radar data to discern the motion types. Experiment results indicate that the proposed approach can provide improved performance in distinguishing fall motions from other motions such as sitting