Ultra-wideband Sensor Research Articles

스마트공장용 로컬 5G의 효과적 도입을 위한 공동사용정책방안

In this study, interference simulations are performed between the local 5G service and ultra-wideband sensors in a smart factory environment. Moreover, the necessity of introducing various interference determination criteria, such as the duty cycle and application of interference avoidance technology, in addition to the existing interference probability is evaluated. This study also suggests improvements in both the technology and policy for an effective introduction of local 5G after carefully reviewing cognitive ratio, automatic frequency coordination, and spectrum-sharing policy. As a result, the proposed scheme will greatly benefit the policy makers.

Ultra-Wideband Positioning Sensor with Application to an Autonomous Ultraviolet-C Disinfection Vehicle.

Due to the COVID-19 virus being highly transmittable, frequently cleaning and disinfecting facilities is common guidance in public places. However, the more often the environment is cleaned, the higher the risk of cleaning staff getting infected. Therefore, strong demand for sanitizing areas in automatic modes is undoubtedly expected. In this paper, an autonomous disinfection vehicle with an Ultraviolet-C (UVC) lamp is designed and implemented using an ultra-wideband (UWB) positioning sensor. The UVC dose for 90% inactivation of the reproductive ability of COVID-19 is 41.7 J/m2, which a 40 W UVC lamp can achieve within a 1.6 m distance for an exposure time of 30 s. With this UVC lamp, the disinfection vehicle can effectively sterilize in various scenarios. In addition, the high-accuracy UWB positioning system, with the time difference of arrival (TDOA) algorithm, is also studied for autonomous vehicle navigation in indoor environments. The number of UWB tags that use a synchronization protocol between UWB anchors can be unlimited. Moreover, this proposed Gradient Descent (GD), which uses Taylor method, is a high-efficient algorithm for finding the optimal position for real-time computation due to its low error and short calculating time. The generalized traversal path planning procedure, with the edge searching method, is presented to improve the efficiency of autonomous navigation. The average error of the practical navigation demonstrated in the meeting room is 0.10 m. The scalability of the designed system to different application scenarios is also discussed and experimentally demonstrated. Hence, the usefulness of the proposed UWB sensor applied to UVC disinfection vehicles to prevent COVID-19 infection is verified by employing it to sterilize indoor environments without human operation.

UWB Based Relative Planar Localization with Enhanced Precision for Intelligent Vehicles

Along with the rapid development of advanced driving assistance systems for intelligent vehicles, essential functions such as forward collision warning and collaborative cruise control need to detect the relative positions of surrounding vehicles. This paper proposes a relative planar localization system based on the ultra-wideband (UWB) ranging technology. Three UWB modules are installed on the top of each vehicle. Because of the limited space on the vehicle roof compared with the ranging error, the traditional triangulation method leads to significant positioning errors. Therefore, an optimal localization algorithm combining homotopy and the Levenberg–Marquardt method is first proposed to enhance the precision. The triangular side lengths and directed area are introduced as constraints. Secondly, a UWB sensor error self-correction method is presented to further improve the ranging accuracy. Finally, we carry out simulations and experiments to show that the presented algorithm in this paper significantly improves the relative position and orientation precision of both the pure UWB localization system and the fusion system integrated with dead reckoning.

Multilayer Flexible Substrate Antenna Sensor for PT Measurement From Blood Plasma to Avoid Turbidity and Reagent Sensitivity Variations Through Regression Modelling

Passive antenna is applied in medical applications such as sensor, radiometer, rectenna, and implantable device for accurate measurement and analysis. Moreover, passive antennas can replace the optical sensors in the pathological instruments to overcome the drawbacks such as optical interference, offset voltage, time consumption and dark current. Furthermore, prothrombin time (PT) measurement through optical and mechanical instruments requires repeated testing for accurate results due to turbidity (lipemia) in blood Plasma and reagent sensitivity during coagulation. Plasma samples in instruments with more K vitamin influences the accuracy of PT measurements. In this paper, we propose a method to measure the PT from blood plasma through passive Ultra-Wideband sensor and overcomes the above problem. The proposed multilayer flexible substrate ultra-wide band (MFS-UWB) passive antenna absorbs the electromagnetic radiations from the concentration of plasma during clotting. The Concentration of plasma is proportional to electromagnetic energy density during clotting. The clotting duration time is acquired based on voltage. During clotting, observed electromagnetic emitted voltage is converted into frequency for measuring the clot concentration and the observed radiation from human blood clotting frequency ranges from 2GHz to 3GHz and voltage from 0.55mV to 0.85mV. The acquired blood clotting concentration voltage measures the prothrombin time using regression modelling and validated through laboratory values.

Night-Time Monitoring System (eNightLog) for Elderly Wandering Behavior.

Wandering is a common behavioral disorder in the community-dwelling elderly. More than two-thirds of caregivers believe that wandering would cause falls. While physical restraint is a common measure to address wandering, it could trigger challenging behavior in approximately 80% of the elderly with dementia. This study aims to develop a virtual restraint using a night monitoring system (eNightLog) to provide a safe environment for the elderly and mitigate the caregiver burden. The eNightLog system consisted of remote sensors, including a near infra-red 3D time-of-flight sensor and ultrawideband sensors. An alarm system was controlled by customized software and algorithm based on the respiration rate and body posture of the elderly. The performance of the eNightLog system was evaluated in both single and double bed settings by comparing to that of a pressure mat and an infrared fence system, under simulated bed-exiting scenarios. The accuracy and precision for the three systems were 99.0%, 98.8%, 85.9% and 99.2%, 97.8%, 78.6%, respectively. With higher accuracy, precision, and a lower false alarm rate, eNightLog demonstrated its potential as an alternative to physical restraint to remedy the workload of the caregivers and the psychological impact of the elderly.

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person’s behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users’ privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5–2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human–robot interaction for various practical applications, such as mobile robots for eldercare.

A UWB Location Algorithm---Based on Adaptive Kalman Filter

In order to solve the problem of large fluctuations in UWB(Ultra Wide Band) positioning accuracy and solid-state deviations in the positioning results, which are in complex indoor environments, an adaptive Kalman filter method is introduced in the later stage of data processing. This method can better retain data information and obtain better filtering effects when the system noise is complex and measurement information is missing,.Thereby providing better positioning accuracy. The experiment uses AGV trolley equipped with UWB sensors to obtain measurement data, uses traditional Kalman filtering and adaptive Kalman filtering methods to compare the filtering effects of the algorithms. The experimental results show that when the measurement information is missing, compared with the traditional Kalman filter algorithm, the adaptive Kalman filter method is a real-time high-precision indoor positioning algorithm with higher positioning accuracy.

Resilient Indoor Localization System Based on UWB and Visual–Inertial Sensors for Complex Environments

In this article, a resilient tightly coupled ultra-wideband (UWB) visual–inertial indoor localization system (R-UVIS) is developed to obtain accurate and robust localization performance in complex scenes, even in the case when sensors fail. More specifically, three schemes are designed for the proposed system. First, we introduce the line and image patch features to improve the precision and robustness of the visual features. Besides, we propose accurate loop closure and relocalization methods based on multifeatures to improve the performance of the localization system. Second, we introduce the UWB sensor into the system to suppress the localization drifts in the complex scenes and provide a fixed reference frame. Third, we propose a resilient multisensor fusion method based on an optimization framework to fuse the UWB, visual and inertial measurements in a tightly coupled manner. This data fusion approach improves the robustness of the system, making the localization system seamlessly switch among different localization modes depending on the specific scenes. In addition, an initialization process along with three sensors is also designed for the whole system. We conduct extensive experiments on the public dataset and in real-world scenarios to evaluate the proposed R-UVIS. The experimental results show that the proposed R-UVIS can provide the accurate and robust localization results in a fixed coordinate system for the complicated indoor scenes, even in the case when visual tracking fails or the UWB anchors are unavailable.

Remote monitoring of indoor/outdoor movement in epidemiological situations utilising UWB transceivers

A global pandemic has reduced mobility, impacting the healthcare assessment, and diagnostics on co-morbid patients. Various challenges for location finding and indoor signalling issues give rise to the necessity for compact and low-cost ultra-wideband sensor technology. Lockdown has played a vital role during crisis in engaging with specific conditions like position estimation in areas under LOS and NLOS caused by diffraction and reflection of UWB signals across obstacles. UWB-based localisation system with edge/fog computing is used to analyse the indoor/outdoor usage of quarantined patients reducing the transmission, which becomes a threat for society and healthcare personals. The mathematical models to symbolise various patterns utilising UWB sensors with anchors and tags were used to determine the flow of all rapid and random movements within the specified clustered locations. The proposed system achieves high classification accuracy with SVM to classify the patterns and assist frontline medical workers in containing pandemics through human interaction.

Robust UWB‐Based Localization with Application to Automated Guided Vehicles

Herein, localization framework based on ultra wide band (UWB) signaling is described. The proposed framework tackles the issues of industrial indoor localization. In particular, experiments are performed using the PulsON 410 Ranging and Communication Modules (P410 RCMs) produced by Time Domain. The considered scenario consists of an industrial warehouse with laser guided vehicles (LGVs), manual forklifts, and people. In the considered localization setup, three RCMs are placed on an LGV and an additional RCM acts as a Target Node (TN), whose position needs to be estimated. As the laser guarantees perfect knowledge of the LGV position inside the building, the three RCMs on board can be considered as Anchor Nodes (ANs) with known positions. The TN is supposed to be placed on a manual forklift or in the belt of a person inside the warehouse. Two localization algorithms that rely on the distance estimates between pairs of UWB sensors positioned on the automated guided vehicles (AGV) and the UWB sensor that acts as TN are proposed. Extensive performance results based on an experimental campaign performed in an industrial warehouse is also shown, for different TN positions around the LGV.

Localization of a person moving with an unknown character of motion

ABSTRACT In the last decade, it has been shown that short-range ultra-wideband (UWB) sensors can provide promising solutions to the issue of people localization in many interesting applications. The applicability of particular localization methods utilizing this wireless technology depends on the form of the motion of the monitored person. Therefore, great effort was initially taken to localize people who always carry out the same type of movement, i.e. to localize either moving or only static persons. In real life, people do not move always by the same form of the motion, but the character of their movement is normally time-variable. Moreover, a person-monitoring system has usually no a priori information about the movement status of the person to be monitored. The issue of localization of persons moving both by an unknown and changing character of motion has not been satisfactorily addressed. Motivated by these findings, we will introduce a new concept of UWB sensor signal processing focused on localization of a person moving with an unknown and time-variable character of motion. This concept is based on the decomposition of possible motion activities of persons into a finite set of basic forms of movement defining the so-called types of persons, while a specific signal processing procedure is proposed. The corresponding form of a person's movement is first identified and then the person is detected and localized. The performance properties of this concept will be illustrated by its application for through-the-wall localization of a person moving with short and long stops.

Monitoring Activities of Daily Living Using UWB Radar Technology: A Contactless Approach

In recent years, the ultra-wideband (UWB) radar technology has shown great potential in monitoring activities of daily living (ADLs) for smart homes. In this paper, we investigate the significance of using non-wearable UWB sensors for developing non-intrusive, unobtrusive, and privacy-preserving monitoring of elderly ADLs. A controlled experiment was setup, implementing multiple non-wearable sensors in a smart home Lab setting. A total of nine (n = 9) participants were involved in conducting predefined scenarios of ADLs- cooking, eating, resting, sleeping and mobility. We employed the UWB sensing prototype and conventional implementation technologies, and the sensed data of both systems were stored, analysed and their performances were compared. The result shows that the performance of the non-wearable UWB technology is as good as that of the conventional ones. Furthermore, we provided a proof-of-concept solution for the real-time detection of abnormal behaviour based on excessive activity levels, and a model for automatic alerts to caregivers for timely medical assistance on-demand.

Research on Developing an Outdoor Location System Based on the Ultra-Wideband Technology.

Ultra-wideband (UWB) technology is one of the most promising wireless communication technologies. Examples of UWB applications include, among others, radiocommunication devices and location systems, due to their operating range, ability to work in outdoor environments, and resistance to multipath effects. This article focuses on the use of UWB technology in constructing a guide localization system for an unmanned ground vehicle (UGV), which is one of the stages of implementing a “follow me” system. This article describes the complete process of UWB signal processing from its acquisition, methods of filtering, and obtained results, to determining the location of the guide. This article examines the possibility of using modified versions of localization algorithms for determining the guide’s location, including trilateration, methods of nonlinear programming, and a geometric algorithm proposed by us. The innovation of this study consists in the implementation of an algorithm that changes the selection of equations (mathematical model) for determining location based on the number of available measurements from UWB sensors.

UWB Sensor Characterization for Radar Sensing and Imaging in Superluminal Propagation Regions

This article investigates the pulse propagation in the near field of a transmitter and reflector from the perspective of radar sensing and imaging. The peaks of the radiated and reflected pulses were found to have traveled at superluminal speeds in the near field of radar. The conceptual explanation and experimental demonstration of the unique properties of the radiated and reflected pulses and their impact on near field radar imaging are presented in this article. To confirm the superluminal propagation, in this study, we considered several ultrawideband (UWB) sensors and scatterers. The physics behind the superluminal propagation and pulse shape change in the radar near field is demonstrated using the Huygens principle, and a method to improve the imaging of radar in the near field while using synthetic aperture techniques is also proposed.

Singular Spectrum Analysis-Based Algorithm for Vitality Monitoring Using M-Sequence UWB Sensor

Recently, noninvasive vital signs detection is widely used in through the wall (TTW) human detection and healthcare monitoring. In indoors and TTW environments, low signal to noise ratio make the detection of the weak vital signs difficult, and causes errors in the estimation of heartbeat, respiration rate. In this paper, different techniques are applied to the signal collected using a M-sequence ultra wideband sensor from the human subject, in order to suppress the clutter, detect the target range and estimate the heartbeat and respiration rate. First, moving average method is used to remove the stationary clutter. Next, Singular Value Decomposition (SVD) is applied to cancel the non-stationary clutter. Then, variance analysis is applied to estimate the range; when range has been detected, one dimensional Singular Spectrum Analysis (SSA) is applied to the range bin containing the subject data. It is observed that SSA completely succeeds to decompose the signal into heartbeat and respiration signal and a collection of unwanted signals as clutter, noise, and harmonics. Finally, Fast Fourier transform (FFT) is used for the estimation of the heartbeat and respiration rate. Comparison results show that the proposed algorithm outperforms a reference SVD based algorithms for heartbeat and respiration rate estimation.

Accurate indoor positioning with ultra-wide band sensors

Ultra-wide band is one of the emerging indoor positioning technologies. In the application phase, accuracy and interference are important criteria of indoor positioning systems. Not only the method used in positioning, but also the algorithms used in improving the accuracy is a key factor. In this paper, we tried to eliminate the effects of off-set and noise in the data of the ultra-wide band sensor-based indoor positioning system. For this purpose, optimization algorithms and filters have been applied to the raw data, and the accuracy has been improved. A test bed with the dimensions of 7.35 m × 5.41 m and 50 cm × 50 cm grids has been selected, and a total of 27,000 measurements have been collected from 180 test points. The average positioning error of this test bed is calculated as 16.34 cm. Then, several combinations of algorithms are applied to raw data. The combination of Big Bang-Big Crunch algorithm for optimization, and then the Kalman Filter have yielded the most accurate results. Briefly, the average positioning error has been reduced from 16.34 cm to 7.43 cm.

Loosely-Coupled Ultra-wideband-Aided Scale Correction for Monocular Visual Odometry

In this paper, we propose a method to address the problem of scale uncertainty in monocular visual odometry (VO), which includes scale ambiguity and scale drift, using distance measurements from a single ultra-wideband (UWB) anchor. A variant of Levenberg–Marquardt (LM) nonlinear least squares regression method is proposed to rectify unscaled position data from monocular odometry with 1D point-to-point distance measurements. As a loosely-coupled approach, our method is flexible in that each input block can be replaced with one’s preferred choices for monocular odometry/SLAM algorithm and UWB sensor. Furthermore, we do not require the location of the UWB anchor as prior knowledge and will estimate both scale and anchor location simultaneously. However, it is noted that a good initial guess for anchor position can result in more accurate scale estimation. The performance of our method is compared with state-of-the-art on both public datasets and real-life experiments.

A benchmarking measurement campaign in GNSS-denied/challenged indoor/outdoor and transitional environments

Abstract Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. This paper reports about a sequence of extensive experiments, conducted at The Ohio State University (OSU) as part of the joint effort of the FIG/IAG WG on Multi-sensor Systems. Their overall aim is to assess the feasibility of achieving GNSS-like performance for ubiquitous positioning in terms of autonomous, global, preferably infrastructure-free positioning of portable platforms at affordable cost efficiency. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were used whereby cooperative positioning (CP) is the major focus to achieve a joint navigation solution. The GPSVan of The Ohio State University was used as the main reference vehicle and for pedestrians, a specially designed helmet was developed. The employed/tested positioning techniques are based on using sensor data from GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), vison-based positioning with cameras and Light Detection and Ranging (LiDAR) as well as inertial sensors. The experimental and initial results include the preliminary data processing, UWB sensor calibration and Wi-Fi indoor positioning with room-level granularity and platform trajectory determination. The results demonstrate that CP techniques are extremely useful for positioning of platforms navigating in swarms or networks. A significant performance improvement in terms of positioning accuracy and reliability is achieved. Using UWB, decimeter-level positioning accuracy is achievable under typical conditions, such as normal walls, average complexity buildings, etc. Using Wi-Fi fingerprinting, success rates of approximately 97 % were obtained for correctly detecting the room-level location of the user.

Towards Industrial Ultra-Wideband Networks: Experiments for Machine Vibration Monitoring

We discuss the potential of ultra-wideband (UWB) technology for realizing use cases in industry 4.0 and demonstrate the implementation of an UWB sensor network with off-the-shelf transceivers for machine vibration monitoring. We propose a flexible medium access layer tailored for industrial sensing and show how the choice of its parameters is affected by the propagation environment and hardware constraints. Our real-world experiments reveal that links up to 20 meters can be achieved in industrial environments with an average packet loss rate in the order of 10 -6 for typical links. A comparison with a commercial vibration monitoring system based on IEEE 802.15.4 spread spectrum shows that UWB attains a twenty-times higher throughput for similar link ranges.

Real-time Visitor’s Behavior Analysis System via Ultra-Wide Band Radar

초광대역 레이더는 수 GHz대의 주파수 대역을 사용하는 고속무선통신 기술로 실내 무선 측위기술로 각광받고 있다. 한편, 박물관 및 전시회 등의 기관에서는 방문객 실시간 위치뿐만 아니라, 관람자의 이동경로 및 체류시간 정보 또한 관람 행태 분석을 위해 필요로 한다. 본 논문에서는 초광대역 레이더를 이용한 실시간 위치 추적 기술을 통해 박물관 방문객의 관람 행태를 분석하는 시스템을 구축 사례를 소개한다. 개발 시스템을 국립중앙박물관 신라관에 설치하여, 22개의 상용 초광대역 레이더로부터 실시간 측위 정보 수신하고, 이를통해 관람자 조건별 이동경로및 전시 대상별 체류시간을분석할 수 있는 환경을구축하였다.The Ultra-Wide Band sensor is widely used as a wireless indoor localization technology with frequency bands in the GHz range. Meanwhile, in museums, not only the real-time location of visitors but also information on visit route and duration time is required for patrons behavior analysis. In this paper, the analysis system based Ultra-Wide Band radar for visitor s viewing behavior is introduced and experimented in the real environment. We built the system in National Museum of Korea, and its 22 Ultra-Wide Band radar sensors receive the real-time location of their visitors: this analyzes the visit route and visit time for patrons.