88 research outputs found
Non-Contact Human Motion Sensing Using Radar Techniques
Human motion analysis has recently gained a lot of interest in the research community due to its widespread applications. A full understanding of normal motion from human limb joint trajectory tracking could be essential to develop and establish a scientific basis for correcting any abnormalities. Technology to analyze human motion has significantly advanced in the last few years. However, there is a need to develop a non-invasive, cost effective gait analysis system that can be functional indoors or outdoors 24/7 without hindering the normal daily activities for the subjects being monitored or invading their privacy. Out of the various methods for human gait analysis, radar technique is a non-invasive method, and can be carried out remotely. For one subject monitoring, single tone radars can be utilized for motion capturing of a single target, while ultra-wideband radars can be used for multi-subject tracking. But there are still some challenges that need to be overcome for utilizing radars for motion analysis, such as sophisticated signal processing requirements, sensitivity to noise, and hardware imperfections. The goal of this research is to overcome these challenges and realize a non-contact gait analysis system capable of extracting different organ trajectories (like the torso, hands and legs) from a complex human motion such as walking. The implemented system can be hugely beneficial for applications such as treating patients with joint problems, athlete performance analysis, motion classification, and so on
Remote Human Vital Sign Monitoring Using Multiple-Input Multiple-Output Radar at Millimeter-Wave Frequencies
Non-contact respiration rate (RR) and heart rate (HR) monitoring using millimeter-wave (mmWave) radars has gained lots of attention for medical, civilian, and military applications. These mmWave radars are small, light, and portable which can be deployed to various places. To increase the accuracy of RR and HR detection, distributed multi-input multi-output (MIMO) radar can be used to acquire non-redundant information of vital sign signals from different perspectives because each MIMO channel has different fields of view with respect to the subject under test (SUT). This dissertation investigates the use of a Frequency Modulated Continuous Wave (FMCW) radar operating at 77-81 GHz for this application. Vital sign signal is first reconstructed with Arctangent Demodulation (AD) method using phase change’s information collected by the radar due to chest wall displacement from respiration and heartbeat activities. Since the heartbeat signals can be corrupted and concealed by the third/fourth harmonics of the respiratory signals as well as random body motion (RBM) from the SUT, we have developed an automatic Heartbeat Template (HBT) extraction method based on Constellation Diagrams of the received signals. The extraction method will automatically spot and extract signals’ portions that carry good amount of heartbeat signals which are not corrupted by the RBM. The extracted HBT is then used as an adapted wavelet for Continuous Wavelet Transform (CWT) to reduce interferences from respiratory harmonics and RBM, as well as magnify the heartbeat signals. As the nature of RBM is unpredictable, the extracted HBT may not completely cancel the interferences from RBM. Therefore, to provide better HR detection’s accuracy, we have also developed a spectral-based HR selection method to gather frequency spectra of heartbeat signals from different MIMO channels. Based on this gathered spectral information, we can determine an accurate HR even if the heartbeat signals are significantly concealed by the RBM. To further improve the detection’s accuracy of RR and HR, two deep learning (DL) frameworks are also investigated. First, a Convolutional Neural Network (CNN) has been proposed to optimally select clean MIMO channels and eliminate MIMO channels with low SNR of heartbeat signals. After that, a Multi-layer Perceptron (MLP) neural network (NN) is utilized to reconstruct the heartbeat signals that will be used to assess and select the final HR with high confidence
Different Approaches of Numerical Analysis of Electromagnetic Phenomena in Shaded Pole Motor with Application of Finite Elements Method
In this paper is used Finite Element Method-FEM
for analysis of electromagnetic quantities of small micro motor –
single phase shaded pole motor-SPSPM. FEM is widely used
numerical method for solving nonlinear partial differential
equations with variable coefficients. For that purpose motor
model is developed with exact geometry and material’s
characteristics. Two different approaches are applied in FEM
analysis of electromagnetic phenomena inside the motor:
magneto-static where all electromagnetic quantities are analysed
in exact moment of time meaning frequency f=0 Hz and timeharmonic
magnetic approach where the magnetic field inside the
machine is time varying, meaning frequency f=50 Hz. Obtained
results are presented and compared with available analytical
result
Antenna Systems
This book offers an up-to-date and comprehensive review of modern antenna systems and their applications in the fields of contemporary wireless systems. It constitutes a useful resource of new material, including stochastic versus ray tracing wireless channel modeling for 5G and V2X applications and implantable devices. Chapters discuss modern metalens antennas in microwaves, terahertz, and optical domain. Moreover, the book presents new material on antenna arrays for 5G massive MIMO beamforming. Finally, it discusses new methods, devices, and technologies to enhance the performance of antenna systems
A Survey on Fundamental Limits of Integrated Sensing and Communication
The integrated sensing and communication (ISAC), in which the sensing and communication share the same frequency band and hardware, has emerged as a key technology in future wireless systems due to two main reasons. First, many important application scenarios in fifth generation (5G) and beyond, such as autonomous vehicles, Wi-Fi sensing and extended reality, requires both high-performance sensing and wireless communications. Second, with millimeter wave and massive multiple-input multiple-output (MIMO) technologies widely employed in 5G and beyond, the future communication signals tend to have high-resolution in both time and angular domain, opening up the possibility for ISAC. As such, ISAC has attracted tremendous research interest and attentions in both academia and industry. Early works on ISAC have been focused on the design, analysis and optimization of practical ISAC technologies for various ISAC systems. While this line of works are necessary, it is equally important to study the fundamental limits of ISAC in order to understand the gap between the current state-of-the-art technologies and the performance limits, and provide useful insights and guidance for the development of better ISAC technologies that can approach the performance limits. In this paper, we aim to provide a comprehensive survey for the current research progress on the fundamental limits of ISAC. Particularly, we first propose a systematic classification method for both traditional radio sensing (such as radar sensing and wireless localization) and ISAC so that they can be naturally incorporated into a unified framework. Then we summarize the major performance metrics and bounds used in sensing, communications and ISAC, respectively. After that, we present the current research progresses on fundamental limits of each class of the traditional sensing and ISAC systems. Finally, the open problems and future research directions are discussed
Cognitive radar network design and applications
PhD ThesisIn recent years, several emerging technologies in modern radar system
design are attracting the attention of radar researchers and practitioners
alike, noteworthy among which are multiple-input multiple-output
(MIMO), ultra wideband (UWB) and joint communication-radar technologies.
This thesis, in particular focuses upon a cognitive approach
to design these modern radars. In the existing literature, these technologies
have been implemented on a traditional platform in which the
transmitter and receiver subsystems are discrete and do not exchange
vital radar scene information. Although such radar architectures benefit
from these mentioned technological advances, their performance remains
sub-optimal due to the lack of exchange of dynamic radar scene
information between the subsystems. Consequently, such systems are
not capable to adapt their operational parameters “on the fly”, which
is in accordance with the dynamic radar environment. This thesis explores
the research gap of evaluating cognitive mechanisms, which could
enable modern radars to adapt their operational parameters like waveform,
power and spectrum by continually learning about the radar scene
through constant interactions with the environment and exchanging this
information between the radar transmitter and receiver. The cognitive
feedback between the receiver and transmitter subsystems is the facilitator
of intelligence for this type of architecture.
In this thesis, the cognitive architecture is fused together with modern
radar systems like MIMO, UWB and joint communication-radar designs
to achieve significant performance improvement in terms of target parameter
extraction. Specifically, in the context of MIMO radar, a novel
cognitive waveform optimization approach has been developed which facilitates
enhanced target signature extraction. In terms of UWB radar
system design, a novel cognitive illumination and target tracking algorithm
for target parameter extraction in indoor scenarios has been developed.
A cognitive system architecture and waveform design algorithm
has been proposed for joint communication-radar systems. This thesis
also explores the development of cognitive dynamic systems that allows
the fusion of cognitive radar and cognitive radio paradigms for optimal
resources allocation in wireless networks. In summary, the thesis provides
a theoretical framework for implementing cognitive mechanisms in
modern radar system design. Through such a novel approach, intelligent
illumination strategies could be devised, which enable the adaptation of
radar operational modes in accordance with the target scene variations
in real time. This leads to the development of radar systems which are
better aware of their surroundings and are able to quickly adapt to the
target scene variations in real time.Newcastle University, Newcastle upon Tyne:
University of Greenwich
Architectures for embedded multimodal sensor data fusion systems in the robotics : and airport traffic suveillance ; domain
Smaller autonomous robots and embedded sensor data fusion systems often suffer from limited
computational and hardware resources. Many ‘Real Time’ algorithms for multi modal sensor data
fusion cannot be executed on such systems, at least not in real time and sometimes not at all, because
of the computational and energy resources needed, resulting from the architecture of the
computational hardware used in these systems. Alternative hardware architectures for generic
tracking algorithms could provide a solution to overcome some of these limitations. For tracking and
self localization sequential Bayesian filters, in particular particle filters, have been shown to be able to
handle a range of tracking problems that could not be solved with other algorithms. But particle filters
have some serious disadvantages when executed on serial computational architectures used in most
systems. The potential increase in performance for particle filters is huge as many of the computational
steps can be done concurrently. A generic hardware solution for particle filters can relieve the central
processing unit from the computational load associated with the tracking task.
The general topic of this research are hardware-software architectures for multi modal sensor data
fusion in embedded systems in particular tracking, with the goal to develop a high performance
computational architecture for embedded applications in robotics and airport traffic surveillance
domain. The primary concern of the research is therefore: The integration of domain specific concept
support into hardware architectures for low level multi modal sensor data fusion, in particular
embedded systems for tracking with Bayesian filters; and a distributed hardware-software tracking
systems for airport traffic surveillance and control systems.
Runway Incursions are occurrences at an aerodrome involving the incorrect presence of an aircraft,
vehicle, or person on the protected area of a surface designated for the landing and take-off of aircraft.
The growing traffic volume kept runway incursions on the NTSB’s ‘Most Wanted’ list for safety
improvements for over a decade. Recent incidents show that problem is still existent. Technological
responses that have been deployed in significant numbers are ASDE-X and A-SMGCS. Although these
technical responses are a significant improvement and reduce the frequency of runway incursions,
some runway incursion scenarios are not optimally covered by these systems, detection of runway
incursion events is not as fast as desired, and they are too expensive for all but the biggest airports.
Local, short range sensors could be a solution to provide the necessary affordable surveillance accuracy
for runway incursion prevention. In this context the following objectives shall be reached. 1) Show the
feasibility of runway incursion prevention systems based on localized surveillance. 2) Develop a design
for a local runway incursion alerting system. 3) Realize a prototype of the system design using the
developed tracking hardware.Kleinere autonome Roboter und eingebettete Sensordatenfusionssysteme haben oft mit stark
begrenzter Rechenkapazität und eingeschränkten Hardwareressourcen zu kämpfen. Viele
Echtzeitalgorithmen für die Fusion von multimodalen Sensordaten können, bedingt durch den hohen
Bedarf an Rechenkapazität und Energie, auf solchen Systemen überhaupt nicht ausgeführt werden,
oder zu mindesten nicht in Echtzeit. Der hohe Bedarf an Energie und Rechenkapazität hat seine
Ursache darin, dass die Architektur der ausführenden Hardware und der ausgeführte Algorithmus
nicht aufeinander abgestimmt sind. Dies betrifft auch Algorithmen zu Spurverfolgung. Mit Hilfe von
alternativen Hardwarearchitekturen für die generische Ausführung solcher Algorithmen könnten sich
einige der typischerweise vorliegenden Einschränkungen überwinden lassen. Eine Reihe von Aufgaben,
die sich mit anderen Spurverfolgungsalgorithmen nicht lösen lassen, lassen sich mit dem Teilchenfilter,
einem Algorithmus aus der Familie der Bayesschen Filter lösen. Bei der Ausführung auf traditionellen
Architekturen haben Teilchenfilter gegenüber anderen Algorithmen einen signifikanten Nachteil,
allerdings ist hier ein großer Leistungszuwachs durch die nebenläufige Ausführung vieler
Rechenschritte möglich. Eine generische Hardwarearchitektur für Teilchenfilter könnte deshalb die
oben genannten Systeme stark entlasten.
Das allgemeine Thema dieses Forschungsvorhabens sind Hardware-Software-Architekturen für die
multimodale Sensordatenfusion auf eingebetteten Systemen - speziell für Aufgaben der
Spurverfolgung, mit dem Ziel eine leistungsfähige Architektur für die Berechnung entsprechender
Algorithmen auf eingebetteten Systemen zu entwickeln, die für Anwendungen in der Robotik und
Verkehrsüberwachung auf Flughäfen geeignet ist. Das Augenmerk des Forschungsvorhabens liegt
dabei auf der Integration von vom Einsatzgebiet abhängigen Konzepten in die Architektur von
Systemen zur Spurverfolgung mit Bayeschen Filtern, sowie auf verteilten Hardware-Software
Spurverfolgungssystemen zur Überwachung und Führung des Rollverkehrs auf Flughäfen.
Eine „Runway Incursion“ (RI) ist ein Vorfall auf einem Flugplatz, bei dem ein Fahrzeug oder eine Person
sich unerlaubt in einem Abschnitt der Start- bzw. Landebahn befindet, der einem Verkehrsteilnehmer
zur Benutzung zugewiesen wurde. Der wachsende Flugverkehr hat dafür gesorgt, das RIs seit über
einem Jahrzehnt auf der „Most Wanted“-Liste des NTSB für Verbesserungen der Sicherheit stehen.
Jüngere Vorfälle zeigen, dass das Problem noch nicht behoben ist. Technologische Maßnahmen die in
nennenswerter Zahl eingesetzt wurden sind das ASDE-X und das A-SMGCS. Obwohl diese Maßnahmen
eine deutliche Verbesserung darstellen und die Zahl der RIs deutlich reduzieren, gibt es einige RISituationen
die von diesen Systemen nicht optimal abgedeckt werden. Außerdem detektieren sie RIs
ist nicht so schnell wie erwünscht und sind - außer für die größten Flughäfen - zu teuer. Lokale Sensoren
mit kurzer Reichweite könnten eine Lösung sein um die für die zuverlässige Erkennung von RIs
notwendige Präzision bei der Überwachung des Rollverkehrs zu erreichen. Vor diesem Hintergrund
sollen die folgenden Ziele erreicht werden. 1) Die Machbarkeit eines Runway Incursion
Vermeidungssystems, das auf lokalen Sensoren basiert, zeigen. 2) Einen umsetzbaren Entwurf für ein
solches System entwickeln. 3) Einen Prototypen des Systems realisieren, das die oben gennannte
Hardware zur Spurverfolgung einsetzt
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Wireless indoor localisation within the 5G internet of radio light
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNumerous applications can be enhanced by accurate and efficient indoor localisation using wireless
sensor networks, however trade-offs often exist between these two parameters. In this thesis, realworld
and simulation data is used to examine the hybrid millimeter wave and Visible Light
Communications (VLC) architecture of the 5G Internet of Radio Light (IoRL) Horizon 2020 project.
Consequently, relevant localisation challenges within Visible Light Positioning (VLP) and asynchronous
sampling networks are identified, and more accurate and efficient solutions are developed.
Currently, VLP relies strongly on the assumed Lambertian properties of light sources.
However, in practice, not all lights are Lambertian. To support the widespread deployment of VLC
technology in numerous environments, measurements from non-Lambertian sources are analysed to
provide new insights into the limitations of existing VLP techniques. Subsequently, a novel VLP
calibration technique is proposed, and results indicate a 59% accuracy improvement against existing
methods. This solution enables high accuracy centimetre level VLP to be achieved with non-
Lambertian sources.
Asynchronous sampling of range-based measurements is known to impact localisation
performance negatively. Various Asynchronous Sampling Localisation Techniques (ASLT) exist to
mitigate these effects. While effective at improving positioning performance, the exact suitability of
such solutions is not evident due to their additional processes, subsequent complexity, and increased
costs. As such, extensive simulations are conducted to study the effectiveness of ASLT under variable
sampling latencies, sensor measurement noise, and target trajectories. Findings highlight the
computational demand of existing ASLT and motivate the development of a novel solution. The
proposed Kalman Extrapolated Least Squares (KELS) method achieves optimal localisation
performance with a significant energy reduction of over 50% when compared to current leading ASLT.
The work in this thesis demonstrates both the capability for high performance VLP from non-
Lambertian sources as well as the potential for energy efficient localisation for sequentially sampled
range measurements.Horizon 202
A fuzzy logic approach to localisation in wireless local area networks
This thesis examines the use and value of fuzzy sets, fuzzy logic and fuzzy inference in wireless positioning systems and solutions. Various fuzzy-related techniques and methodologies are reviewed and investigated, including a comprehensive review of fuzzy-based positioning and localisation systems. The thesis is aimed at the development of a novel positioning technique which enhances well-known multi-nearest-neighbour (kNN) and fingerprinting algorithms with received signal strength (RSS) measurements. A fuzzy inference system is put forward for the generation of weightings for selected nearest-neighbours and the elimination of outliers. In this study, Monte Carlo simulations of a proposed multivariable fuzzy localisation (MVFL) system showed a significant improvement in the root mean square error (RMSE) in position estimation, compared with well-known localisation algorithms. The simulation outcomes were confirmed empirically in laboratory tests under various scenarios. The proposed technique uses available indoor wireless local area network (WLAN) infrastructure and requires no additional hardware or modification to the network, nor any active user participation. The thesis aims to benefit practitioners and academic researchers of system positioning
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