34 research outputs found
A NOVEL APPROACH TO ORBITAL DEBRIS MITIGATION
Since mankind launched the first satellite into orbit in 1957, we have been inadvertently, yet deliberately, creating an environment in space that may ultimately lead to the end of our space exploration. Space debris, more specifically, orbital debris is a growing problem that must be dealt with sooner, rather than later. Several ideas have been developed to address the complex problem of orbital debris mitigation.
This research will investigate the possibility of removing orbital debris from the Low Earth Orbit (LEO) regime by using a metaheuristic algorithm to maximize collection of debris resulting from the February 2009 on-orbit collision of Iridium 33 and Cosmos 2251. This treatment will concentrate on the Iridium debris field for analysis. This research is necessary today, more than ever, as we embark on the launch of thousands of LEO spacecraft, which could result in the realization of the Kessler Syndrome, “The certain risk of failure on launch or during operations due to an on-orbit collision with debris” (Kessler & Cour-Palais, 1978)
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
Design of Heuristic Algorithms for Hard Optimization
This open access book demonstrates all the steps required to design heuristic algorithms for difficult optimization. The classic problem of the travelling salesman is used as a common thread to illustrate all the techniques discussed. This problem is ideal for introducing readers to the subject because it is very intuitive and its solutions can be graphically represented. The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods. Each metaheuristic can then be presented in simplified form as a combination of these concepts. This approach avoids giving the impression that metaheuristics is a non-formal discipline, a kind of cloud sculpture. Moreover, it provides concrete applications of the travelling salesman problem, which illustrate in just a few lines of code how to design a new heuristic and remove all ambiguities left by a general framework. Two chapters reviewing the basics of combinatorial optimization and complexity theory make the book self-contained. As such, even readers with a very limited background in the field will be able to follow all the content
Cluster Heads Selection and Cooperative Nodes Selection for Cluster-based Internet of Things Networks
PhDClustering and cooperative transmission are the key enablers in power-constrained Internet
of Things (IoT) networks. The challenges for power-constrained devices in IoT networks
are to reduce the energy consumption and to guarantee the Quality of Service
(QoS) provision. In this thesis, optimal node selection algorithms based on clustering
and cooperative communication are proposed for different network scenarios, in particular:
• The QoS-aware energy efficient cluster heads (CHs) selection algorithm in one-hop
capillary networks. This algorithm selects the optimum set of CHs and construct
clusters accordingly based on the location and residual energy of devices.
• Cooperative nodes selection algorithms for cluster-based capillary networks. By
utilising the spacial diversity of cooperative communication, these algorithms select
the optimum set of cooperative nodes to assist the CHs for the long-haul transmission.
In addition, with the regard of evenly energy distribution in one-hop
cluster-based capillary networks, the CH selection is taken into consideration when
developing cooperative devices selection algorithms.
The performance of proposed selection algorithms are evaluated via comprehensive simulations.
Simulation results show that the proposed algorithms can achieve up to 20%
network lifetime longevity and up to 50% overall packet error rate (PER) decrement.
Furthermore, the simulation results also prove that the optimal tradeoff between energy
efficiency and QoS provision can be achieved in one-hop and multi-hop cluster-based
scenarios.Chinese Scholarship Counci
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
The Deep Space Network
Progress in flight project support, tracking and data acquisition (TDA) research and technology, network engineering, hardware and software implementation, and operations are reported
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available