Using Ultra-wideband Research Articles

Design a novel algorithm for enhancing UWB positioning accuracy in GPS denied environments

Accurate indoor positioning is the key to the development of the Internet of Things and intelligent devices. In view of GPS-denied indoor environments, we propose to build the indoor local positioning system by using ultra-wide band (UWB) system. In order to enhance the localization accuracy of UWB system, we propose a novel algorithm which integrates the Maximum Correntropy Criterion (MCC) and unscented Kalman filter (UKF) method to reconstruct the measurement distance by using the maximum entropy principle to reduce the influence of outliers and unknown process noise on the smooth effect. Subsequently, the least square (LS) method is implemented to attain the target node (TN) initial position coordinates, and the Taylor algorithm is then performed to further optimize the localization results of the LS method. Lastly, the experimental investigation is conducted to assess the effectiveness and applicability of the developed method via the UWB system in indoor scenarios. The experimental outcomes demonstrate that the developed MCCUKF-LS method can achieve the lowest root mean square error (RMSE), and enhance the positioning accuracy of the TN compared with the LS, KF-LS, and UKF-LS methods. The overall average RMSE of MCCUKF-LS method is reduced by 45.7% contracted with the LS algorithm. The average error of x-, y- and z-axis orientation for the LS method is reduced from 0.074 m, 0.067 m, 0.098 m to 0.036 m, 0.034 m, 0.044 m, and the achieved accuracy in the orientation of the three axes is increased by 51.4%, 49.3% and 55.1% respectively, which reveals that the designed fusion technique is capable of enhancing the positioning accuracy of the TN effectively, providing a new positioning methodology and reference for indoor positioning in GPS-denied environments.

Detection of malignant breast tissue using SAR observation with microwave imaging and convolutional neural network

The research paper introduces a deep learning approach for distinguishing between benign and malignant breast tissues using Ultra Wide Band (UWB) microwave imaging. A multi-resonant monopole microstrip patch antenna was crafted and placed on a female breast phantom model containing a tumor. The phantom’s dielectric properties were adjusted to mimic those of benign and malignant tissues. The Specific Absorption Rate (SAR) was analyzed by rotating the antenna at various angles throughout the phantom’s trajectory. Images capturing the SAR distribution over the breast phantom at different antenna resonances were obtained. The popular Convolutional Neural Network (CNN) based AlexNet model was employed for autonomous feature learning from SAR images. To address the challenge of overfitting with a limited dataset, the study utilized image augmentation and transfer learning methods. The proposed model achieved an impressive 98.59% accuracy in classifying benign and malignant breast phantom images, surpassing the performance of three other CNN models—VggNet16, GoogleNet, and ResNet. Notably, this microwave imaging system based on SAR images detected tumors as small as 1 mm with an accuracy of 97.08% outperforming existing malignancy screening techniques.

Coupled and radiated microwave sensors for vital signs and lung water level monitoring

This paper presents a novel approach for non-invasive monitoring of vital signs (heart rate (HR), respiration rate (RR)) and lung water levels using ultra-wideband (UWB) microwave sensors. The primary contribution of this study is the development and testing of two different sensor technologies to enhance the penetration and monitoring capabilities of electromagnetic waves within the human body. The first sensor technology employs a radiated UWB microwave that can be integrated into textiles, operating within a frequency range of 1.5 to 10 GHz, providing flexibility and comfort for wearable health monitoring. The second sensor technology involves using body-coupled UWB non-radiating sensors with a wide operational bandwidth from 1.3 to 10 GHz and a stable feeding structure, ensuring efficient reflection and transmission of waves for accurate monitoring. The shapes of flexible and textile UWB electromagnetic microwave sensor radiators and couplers, along with the measurement procedure, are tested in human clinical studies. The sensor-specific absorption rate (SAR) is evaluated at two distinct resonant frequencies on tissue masses of 1 g and 10 g, aligning with the IEEE C95.3 standard to ensure adherence to standard thresholds of 1.6 W/kg and 2 W/kg, respectively. Additionally, a denoising scheme utilizing deep learning techniques is proposed to filter Base Line Wander (BLW) noise attributable to misplaced electrodes, skin impedance, poor skin preparation, patient movement, and breathing within the frequency range of 0.05 to 3 Hz. The postprocessing outcome demonstrates the superiority of couplers over radiating sensors.

Comparative Analysis of Filtering Techniques for AGV Indoor Localization with Ultra-Wideband Technology

This paper investigates the filtering techniques to enhance the accuracy of indoor localization for Autonomous Guided Vehicles (AGVs) using Ultra-Wideband (UWB) technology. A comprehensive comparative analysis of various filtering approaches, including the Kalman Filter (KF), Moving Average Filter (MA), Savitzky-Golay Filter (SG), Weighted Average Filter (WAF), and their combinations, are conducted. The primary focus of this paper is the integration of a Moving Average-Kalman Filter (MAKF) with an extended window size of 201. Experimental findings reveal significant performance differences among these filtering techniques. The most effective approach is the MAKF technique, achieving an accuracy of 85.13% and the lowest path deviation of 0.17 meters. Conversely, the MA exhibits the lowest accuracy at 68.83%. Notably, the WAF attains an accuracy of 72.46% but exhibits a significantly higher path deviation of 2.65 meters compared to 1.45 meters of the MA filtering technique. The proposed MAKF acknowledged for its ability to effectively reduce noise with real-time responsiveness, represents a significant advancement in AGV indoor localization techniques.

UWB-enabled Sensing for Fast and Effortless Blood Pressure Monitoring

Blood Pressure (BP) is a critical vital sign to assess cardiovascular health. However, existing cuff-based and wearable-based BP measurement methods require direct contact between the user's skin and the device, resulting in poor user experience and limited engagement for regular daily monitoring of BP. In this paper, we propose a contactless approach using Ultra-WideBand (UWB) signals for regular daily BP monitoring. To remove components of the received signals that are not related to the pulse waves, we propose two methods that utilize peak detection and principal component analysis to identify aliased and deformed parts. Furthermore, to extract BP-related features and improve the accuracy of BP prediction, particularly for hypertensive users, we construct a deep learning model that extracts features of pulse waves at different scales and identifies the different effects of features on BP. We build the corresponding BP monitoring system named RF-BP and conduct extensive experiments on both a public dataset and a self-built dataset. The experimental results show that RF-BP can accurately predict the BP of users and provide alerts for users with hypertension. Over the self-built dataset, the mean absolute error (MAE) and standard deviation (SD) for SBP are 6.5 mmHg and 6.1 mmHg, and the MAE and SD for DBP are 4.7 mmHg and 4.9 mmHg.

Microwave Heartprint: A novel non-contact human identification technology based on cardiac micro-motion detection using ultra wideband bio-radar

The existing one-time identity authentication technology cannot continuously guarantee the legitimacy of user identity during the whole human-computer interaction session, and often requires active cooperation of users, which seriously limits the availability. This study proposes a new non-contact identity recognition technology based on cardiac micro-motion detection using ultra wideband (UWB) bio-radar. After the multi-point micro-motion echoes in the range dimension of the human heart surface area were continuously detected by ultra wideband bio-radar, the two-dimensional principal component analysis (2D-PCA) was exploited to extract the compressed features of the two-dimensional image matrix, namely the distance channel-heart beat sampling point (DC-HBP) matrix, in each accurate segmented heart beat cycle for identity recognition. In the practical measurement experiment, based on the proposed multi-range-bin & 2D-PCA feature scheme along with two conventional reference feature schemes, three typical classifiers were selected as representatives to conduct the heart beat identification under two states of normal breathing and breath holding. The results showed that the multi-range-bin & 2D-PCA feature scheme proposed in this paper showed the best recognition effect. Compared with the optimal range-bin & overall heart beat feature scheme, our proposed scheme held an overall average recognition accuracy of 6.16% higher (normal respiration: 6.84%; breath holding: 5.48%). Compared with the multi-distance unit & whole heart beat feature scheme, the overall average accuracy increase was 27.42% (normal respiration: 28.63%; breath holding: 26.21%) for our proposed scheme. This study is expected to provide a new method of undisturbed, all-weather, non-contact and continuous identification for authentication.

Untethered Ultra-Wideband-Based Real-Time Locating System for Road-Worker Safety.

In order to reduce the accident risk in road construction and maintenance, this paper proposes a novel solution for road-worker safety based on an untethered real-time locating system (RTLS). This system tracks the location of workers in real time using ultra-wideband (UWB) technology and indicates if they are in a predefined danger zone or not, where the predefined safe zone is delimited by safety cones. Unlike previous works that focus on road-worker safety by detecting vehicles that enter into the working zone, our proposal solves the problem of distracted workers leaving the safe zone. This paper presents a simple-to-deploy safety system. Our UWB anchors do not need any cables for powering, synchronisation, or data transfer. The anchors are placed inside safety cones, which are already available in construction sites. Finally, there is no need to manually measure the positions of anchors and introduce them to the system thanks to a novel self-positioning approach. Our proposal, apart from automatically estimating the anchors' positions, also defines the limits of safe and danger zones. These features notably reduce the deployment time of the proposed safety system. Moreover, measurements show that all the proposed simplifications are obtained with an accuracy of 97%.

Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar

Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results.

Target-following Robotic Platform Based on UWB Localization and Depth Camera

A mobile robot capable of following a specified target using Ultra-Wideband (UWB) technology and an RGB-D camera is presented in this paper. One of our primary objectives is to promote the use of UWB as a precise and fast real-time localization system (RTLS) in mobile robotics, specifically the target following applications using the approach of non-fixed anchors. We use the depth camera as a complementary sensor to the omnidirectional UWB, to increase the robot’s awareness of its surroundings and possibly also as a way of providing human-machine interaction. This paper also describes creating a simplified mathematical model for computing the control action and the method we have used to measure the system’s rotational dynamics for system identification purposes. Thereafter, we combined a PD controller and proportional virtual spring regulation and selected parameters of the compound regulator to slowly pursue a non-linear movement of the followed person. Our robotic platform is originally meant to carry a toolbox and equipment. However, it might be just as well used in various applications such as material handling in industrial and manufacturing settings, as an assistive technology for load handling in warehouses, construction sites, even carrying airline baggage or military equipment. Most countries implement regulations regarding lifting and carrying loads that are even more strict in case of repetitive or long-term use. Therefore, using a robotic platform in such cases might be convenient.

Building Geometry Survey by Using Ultra-Wideband (UWB) Wireless Technology and Algorithm-Based BIM Modelling

The efficiency and degree of maturity of the architectural survey determine the construction process in many ways. From the economic and technical point of view, it is significant. It can be found in almost all activities and, depending on the technology used, it provides different qualities and quantities of initial data and information to prepare and support, among other things, the design, demolition, and construction work processes. The objective of the research and development project and this article is to investigate and demonstrate the application of a novel solution that differs from the conventional building survey tools, methods, and procedures of the information. It breaks with traditional methods by implementing wireless (using Ultra-Wideband (UWB) technology) spatial positioning and algorithm-based post-processing methods, as well as Building Information Modelling (BIM).

Spoofing Evident and Spoofing Deterrent Localization Using Ultrawideband (UWB) Active–Passive Ranging

Spoofing Evident and Spoofing Deterrent Localization Using Ultrawideband (UWB) Active–Passive Ranging

Speaker identification using Ultra‐Wideband measurement of voice

AbstractVoice identification is being increasingly adopted in various domains, including security infrastructures, intelligent home systems, and personalised digital assistants. Notably, it harbours significant promise in transforming healthcare, especially in electronic health record detecting and speech impairment monitoring such as aphasia. Current strategies such as acoustic models based on deep learning, voice bio‐metrics, and spectrogram analysis, have been identified with several drawbacks including vulnerability to altered voices, susceptibility to ambient noise, and the necessity for significant computational power. In response to these issues, the authors introduce a ground‐breaking method of voice identification using Ultra‐Wideband (UWB) technology. This method capitalises on the micro‐Doppler shifts associated with movements of the laryngeal prominence. The distinctive nature of these bio‐metric traits related to speech production provides superior resistance against common pitfalls of voice identification. The proposed model leverages the high‐resolution characteristics of UWB to register tiny variations in laryngeal movements produced during speech, thus forming a distinct voice profile for each speaker. Through rigorous testing, the proposed system demonstrated significant progress in voice identification, achieving close to 90% accuracy in controlled experimental settings. This breakthrough indicates that UWB‐enabled voice identification could have a profound effect on medical applications, providing potential improvements in diagnosing, monitoring, possibly treating speech disorders, and thereby shaping a future of enhanced and secured healthcare services.

Quad-Element MIMO Antenna System Using Half-Cut Miniaturized UWB Antenna for IoT-Based Smart Home Digital Entertainment Network

This work presents a miniaturized quad-element multi-input multi-output (MIMO) antenna with low mutual coupling, inherent isolation, and easy upgradability for an Internet of Things (IoT) based high-speed smart digital entertainment network using ultra-wideband (UWB) technology. Ultra Wideband (UWB) technology is anticipated to play a significant role in the future IoT based smart systems due to its ability to provide high precision location information, low power consumption, high data rates and secure communication. An elliptical quadrant-shaped radiator with a C-shaped protuberant ground plane is conceptualized as a quasi-monopole UWB antenna following the half-cut miniaturization approach with very compact dimensions of 18W11 mm2. The elliptical quadrant radiator in combination with C-shaped ground plane introduces multi-resonance characteristic in addition to providing better impedance matching with miniaturization and inherent high isolation for MIMO configuration. The single element antenna is then organized in an orthogonal arrangement with an edge-to-edge distance of 5 mm to develop a quad-element MIMO antenna system. The MIMO antenna structure realizes a wide bandwidth of 3.14-11.23 GHz (center frequency at 7.19 GHz), peak gain of 7.2 dBi, and radiation efficiency of above 60% with good isolation (>-20 dB) between the adjacent elements without employing any dedicated decoupling structure. The diversity performances of the proposed MIMO antenna are relatively good, with a very low envelope correlation coefficient (ECC) of less than 0.35 and diversity gain (DG) nearly at 10. Further, the total active reflection coefficient (TARC) <-10 dB and channel capacity loss (CCL) <0.4 bit/s/Hz, parameters are witnessed to be within acceptable limits. The simulated and measured results agree well, testifying that the proposed four-element MIMO antenna is suitable for UWB applications.

Unidirective Miniaturized Ultrawideband Antenna for Sensing Buried Objects in Handheld Ground-Penetrating Radar Systems: A design approach

Ground-penetrating radars (GPR) using ultrawideband (UWB) technology have attracted a lot of research interests worldwide for high-resolution sensing and material characterization. This research contribution, therefore, introduces a miniaturized lightweight UWB antenna with high-gain characteristics for applications in handheld GPR systems. The high-gain antenna is developed to function in the 3.13–11.74-GHz wide frequency range such that it can allow high-resolution sensing of buried targets, such as landmines, metal pipes, and other structures. An elliptical quadrant-shaped UWB monopole radiator with a protuberant ground plane is conceptualized to constitute a highly miniaturized antenna of size 18 × 11 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The standalone UWB antenna realizes an impedance bandwidth of 3.07–11.67 GHz with a peak gain of 2.9 dBi. Next, a single-side copper-coated dielectric substrate as a reflector is placed beneath the antenna to attain a significant gain improvement. The proposed design with a reflector provides an average gain improvement of about 7.1 dB without influencing the original bandwidth. For experimental validation, a prototype is fabricated and characterized. The measured performance reveals close agreement with the simulation with an operating bandwidth of 3.19–11.83 GHz and a maximum gain of approximately 10 dBi at 4.02 GHz.

Extended Kalman/UFIR Filters for UWB-Based Indoor Robot Localization Under Time-Varying Colored Measurement Noise

In indoor robot localization by using Ultra-Wide Band (UWB), the extended Kalman filter (EKF)-based algorithms suffer from the Colored Measurement Noise (CMN) that degrades the localization accuracy and causes the divergence. To overcome this issue, we develop a hybrid colored EKF and colored extended unbiased Finite Impulse Response (EFIR) filter (cEKF/EFIR filter) employing measurement differences. We also develop this algorithm using a filter bank on merged averaging horizons to be adaptive to time-varying CMN and call it the aEKF/EFIR filter. Experimental testing is provided in UWB-based indoor mobile robot localization environments. It is shown that the end-to-end cEKF/EFIR and aEKF/EFIR filtering algorithms have better performances than the EKF, EFIR filter, and their modifications for CMN.

Ultra-wide band antipodal Vivaldi antenna design using target detection algorithm for detection application

This work presents a technique for detecting targets between two walls using ultra-wide band (UWB) modified antipodal Vivaldi antenna (MAVA). The detection system works on principle of time domain reflectrometry (TDR) using through wall imaging (TWI). This technique utilized a vector network analyzer (VNA) to produced short and small pluses to irradiate through an antenna array system onto the wall under study. The purposed detection system operated in UWB frequency spectrum (3.1 GHz to 10.6 GHz). Furthermore, an algorithm for hidden target detection has been developed. The results of the simulation of the designed antenna revealed a significant level of penetration, demonstrating a smart advancement in detecting and imaging system, to locate hidden metallic targets with good accuracy. A signal processing technique have been employed to improve the resolution of the target image. Using computer simulation technology (CST) software, the development and optimization process of an antenna is carried out, and the parametric performance of return loss, directivity and radiation pattern is evaluated.

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar.

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE-K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD-WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO-SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

Real-time remote measurement of distance using ultra-wideband (UWB) sensors

Distance measurement is significant for ensuring the safety and serviceability of engineering structures. Recently, ultra-wideband (UWB) sensors have offered an alternative real-time remote sensing solution to measure distances. UWB sensors exhibit small size, low cost, low energy consumption, and high robustness to weather conditions, but their ranging accuracy is still limited. This paper presents two machine learning approaches to achieve high accuracy and high frequency, simultaneously. The first approach integrates a convolutional neural network, a long short-term memory module, and a regression module. The second approach integrates two random forest models. These two approaches were implemented into measurement from UWB sensors deployed on a highway bridge and outperformed the state-of-the-art approaches in terms of measurement accuracy and output frequency. The configurations and key parameters of the two approaches were evaluated and improved. This research enhances the capability of measuring distances and deformations for structural health monitoring.

Meta-surface (frequency selective surface) loaded high gain directional antenna systems for ultra-wideband applications

&lt;span lang="EN-US"&gt;In this work, a meta-surface (frequency selective surface) loaded high gain directional antenna system is presented. The antenna system is developed using ultra-wideband (UWB) antenna element and meta-surface reflector. The UWB antenna element is designed and simulated without meta-surface reflector. The UWB antenna element has poor impedance bandwidth and directivity. A meta-surface is created using unit cell and equal in the size of the antenna substrate. The meta-surface is placed over the UWB antenna element at optimized height (H=30 mm). The impedance bandwidth, directivity and gain of the proposed antenna are improved by the meta-surface reflector. The proposed antenna is fabricated and experimentally validated by the comparison of the simulated and measured results. The antenna has 3 to 6 GHz wide impedance bandwidth, more than 5 dBi gain and maximum 4.6 dBi directivity at 3.5 GHz frequency. Performance of the proposed antenna is also compared with existing carried out work. Comparatively, the proposed antenna with high directivity is most suitable for IEEE 802.15.4a UWB wireless sensor network (WSN) security application.&lt;/span&gt;

UWB-Based Early Breast Cancer Existence Prediction Using Artificial Intelligence for Large Data Set

Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use.