110 research outputs found
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
Advanced Sensing and Control for Connected and Automated Vehicles
Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs
Edge Artificial Intelligence for Real-Time Target Monitoring
The key enabling technology for the exponentially growing cellular communications sector is location-based services. The need for location-aware services has increased along with the number of wireless and mobile devices. Estimation problems, and particularly parameter estimation, have drawn a lot of interest because of its relevance and engineers' ongoing need for higher performance. As applications expanded, a lot of interest was generated in the accurate assessment of temporal and spatial properties.
In the thesis, two different approaches to subject monitoring are thoroughly addressed. For military applications, medical tracking, industrial workers, and providing location-based services to the mobile user community, which is always growing, this kind of activity is crucial.
In-depth consideration is given to the viability of applying the Angle of Arrival (AoA) and Receiver Signal Strength Indication (RSSI) localization algorithms in real-world situations. We presented two prospective systems, discussed them, and presented specific assessments and tests. These systems were put to the test in diverse contexts (e.g., indoor, outdoor, in water...). The findings showed the localization capability, but because of the low-cost antenna we employed, this method is only practical up to a distance of roughly 150 meters. Consequently, depending on the use-case, this method may or may not be advantageous. An estimation algorithm that enhances the performance of the AoA technique was implemented on an edge device.
Another approach was also considered. Radar sensors have shown to be durable in inclement weather and bad lighting conditions. Frequency Modulated Continuous Wave (FMCW) radars are the most frequently employed among the several sorts of radar technologies for these kinds of applications. Actually, this is because they are low-cost and can simultaneously provide range and Doppler data. In comparison to pulse and Ultra Wide Band (UWB) radar sensors, they also need a lower sample rate and a lower peak to average ratio. The system employs a cutting-edge surveillance method based on widely available FMCW radar technology. The data processing approach is built on an ad hoc-chain of different blocks that transforms data, extract features, and make a classification decision before cancelling clutters and leakage using a frame subtraction technique, applying DL algorithms to Range-Doppler (RD) maps, and adding a peak to cluster assignment step before tracking targets. In conclusion, the FMCW radar and DL technique for the RD maps performed well together for indoor use-cases. The aforementioned tests used an edge device and Infineon Technologies' Position2Go FMCW radar tool-set
Novel Approaches for Nondestructive Testing and Evaluation
Nondestructive testing and evaluation (NDT&E) is one of the most important techniques for determining the quality and safety of materials, components, devices, and structures. NDT&E technologies include ultrasonic testing (UT), magnetic particle testing (MT), magnetic flux leakage testing (MFLT), eddy current testing (ECT), radiation testing (RT), penetrant testing (PT), and visual testing (VT), and these are widely used throughout the modern industry. However, some NDT processes, such as those for cleaning specimens and removing paint, cause environmental pollution and must only be considered in limited environments (time, space, and sensor selection). Thus, NDT&E is classified as a typical 3D (dirty, dangerous, and difficult) job. In addition, NDT operators judge the presence of damage based on experience and subjective judgment, so in some cases, a flaw may not be detected during the test. Therefore, to obtain clearer test results, a means for the operator to determine flaws more easily should be provided. In addition, the test results should be organized systemically in order to identify the cause of the abnormality in the test specimen and to identify the progress of the damage quantitatively
Development of a Cost-Efficient Multi-Target Classification System Based on FMCW Radar for Security Gate Monitoring
Radar systems have a long history. Like many other great inventions, the origin of radar systems lies in warfare. Only in the last decade, radar systems have found widespread civil use in industrial measurement scenarios and automotive safety applications. Due to their resilience against harsh environments, they are used instead of or in addition to optical or ultrasonic systems. Radar sensors hold excellent capabilities to estimate distance and motion accurately, penetrate non-metallic objects, and remain unaffected by weather conditions. These capabilities make these devices extremely flexible in their applications. Electromagnetic waves centered at frequencies around 24 GHz offer high precision target measurements, compact antenna, and circuitry design, and lower atmospheric absorption than higher frequency-based systems.
This thesis studies non-cooperative automatic radar multi-target detection and classification. A prototype of a radar system with a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets passing through a road gate is presented. It allows identifying different types of targets, i.e., pedestrians, motorcycles, cars, and trucks. The developed system is based on a low-cost 24 GHz off-the-shelf FMCW radar, combined with an embedded Raspberry Pi PC for data acquisition and transmission to a remote processing PC, which takes care of detection and classification. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by the radar.
The developed method is based on an ad-hoc processing chain to accomplish the automatic target recognition task, which consists of blocks performing clutter and leakage removal with a frame subtraction technique, clustering with a DBSCAN approach, tracking algorithm based on the \u3b1-\u3b2 filter to follow the targets during traversal, features extraction, and finally classification of targets with a classification scheme based on support vector machines. The approach is validated in real experimental scenarios, showing its capabilities incorrectly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks). The approach has been validated with experimental data acquired in different scenarios, showing good identification capabilities
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Radio wave imaging using Ultra-Wide Band Spectrum Antennas for Near-Field Applications. Design, Development, and Measurements of Ultra-Wideband Antenna for Microwave Near-Field Imaging Applications by applying Optimisation Algorithms
The emergence of Ultra-wideband (UWB) technology application has yielded tremendous and vital impacts in the field of microwave wireless communications. These applications include military radar imaging, security screening, and tumour detection, especially for early detection of breast cancer. These indicators have stimulated and inspired many researchers to make the best use of this promising technology.
UWB technology challenges such as antenna design, the problem of imaging reconstruction techniques, challenges of severe signal attenuation and dispersion in high loss material. Others are lengthy computational time demand and large computer memory requirements are prevalent constraints that need to be tackled especially in a large scale and complex computational electromagnetic analysis. In this regard, it is necessary to find out recently developed optimisation techniques that can provide solutions to these problems.
In this thesis, designing, optimisation, development, measurement, and analysis of UWB antennas for near-field microwave imaging applications are considered. This technology emulates the same concept of surface penetrating radar operating in various forms of the UWB spectrum. The initial design of UWB monopole antennas, including T-slots, rectangular slots, and hexagonal slots on a circular radiating patch, was explicitly implemented for medical imaging applications to cover the UWB frequency ranging from 3.1 GHz to 10.6 GHz.
Based on this concept, a new bow-tie and Vivaldi UWB antennas were designed for a through-the-wall imaging application. The new antennas were designed to cover a spectrum on a lower frequency ranging from 1 GHz - 4 GHz to ease the high wall losses that will be encountered when using a higher frequency range and to guarantee deeper penetration of the electromagnetic wave. Finally, both simulated and calculated results of the designed, optimised antennas indicate excellent agreement with improved performance in terms of return loss, gain, radiation pattern, and fidelity over the entire UWB frequency. These breakthroughs provided reduced computational time and computer memory requirement for useful, efficient, reliable, and compact sensors for imaging applications, including security and breast cancer detection, thereby saving more lives.Tertiary Education Trust Fund (TET Fund)
Supported by the Nigerian Defence Academy (NDA
An FPGA-based 77 GHzs RADAR signal processing system for automotive collision avoidance
An FPGA implementable Verilog HDL based signal processing algorithm has been developed to detect the range and velocity of target vehicles using a MEMS based 77 GHz LFMCW long range automotive radar. The algorithm generates a tuning voltage to control a GaAs based VCO to produce a triangular chirp signal, controls the operation of MEMS components, and finally processes the IF signal to determine the range and veolicty of the detected targets. The Verilog HDL code has been developed targeting the Xilinx Virtex-5 SX50T FPGA. The developed algorithm enables the MEMS radar to detect 24 targets in an optimum timespan of 6.42 ms in the range of 0.4 to 200 m with a range resolution of 0.19 m and a maximum range error 0.25 m. A maximum relative velocity of ±300 km/h can be determined with a velocity resolution in HDL of 0.95 m/s and a maximum velocity error of 0.83 m/s with a sweep duration of 1 ms
Characterization of multi-wall carbon nanotubes and their applications
PhDCarbon nanotubes (CNT) and their applications is a field which has attract a lot of interest in the past two decades. Since the first invention of CNTs in 1991, and in view of utilising nanoantennas, the focus in many laboratories around the world has shifted to trying to lengthen nanotubes longer from nanometers to few centimeters. Eventually this could lead to CNTs’ use in sub-millimeter, millimiter wave and microwave antenna applications.
In this thesis, fundamental properties of carbon nanotube films are investigated, and some applications such as the use of CNTs as absorbers or CNT doped liquid crystals are considered. The concept of frequency tunable patch antennas is also presented. Simulation and measurement results of the liquid crystal based antenna show that frequency tuning is possible, through the use of a liquid crystal cell as a substrate. Additionally, greater tuning can be achieved using liquid crystals with higher dielectric anisotropy at microwave frequencies. This can be achieved by using CNT doped liquid crystals.
As mentioned, microwave and terahertz measurements of vertically aligned carbon nanotube arrays placed on the top surface of a rectangular silicon substrate are presented. The S-parameters are calculated allowing the extraction of the complex permittivity, permeability and conductivity of the samples. Theoretical models are being introduced delineating the behaviour of the multi-walled nanotube (MWNT) samples. The material properties of this film provide useful data for potential microwave and terahertz applications such as absorbers.
Finally, finite-difference time-domain (FDTD) modelling of CNTs is introduced, verifying the measurements that have been performed, confirming that CNT arrays can be highly absorptive. A novel estimation of the permittivity and permeability of an individual carbon nanotube is presented and a periodic structure is simulated, under periodic boundary conditions, consisting of solid anisotropic cylinders. In addition, the optical properties of vertically aligned carbon nanotube (VACNT) arrays, when the periodicity is both within the sub-wavelength and wavelength
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regime are calculated. The effect of geometrical parameters of the tube such as length, diameter and inter-tube distance between two consecutive tubes are also examined
Actuators for Intelligent Electric Vehicles
This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs
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