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DOI

  • Inna Ivashko
The ultimate goal of any sensing system is to build situation awareness. Existing
solutions for a single radar node that have to assure extended areas of coverage with
high resolution measurements (in range, cross-range, and Doppler) are physically
cumbersome (large antenna size) and typically require large operational resources (high
transmit power, wide bandwidth and long integration time).
Combining data from multiple spatially separated nodes located at several locations
offers a possibility to use radars with low-cost omnidirectional antennas to cover wide
areas and overcome operational limitations such as sector blockage due to landscape or
high-rise buildings. Thus, performance of the complete system becomes dependent not
only on the parameters of a single radar node, but on the number of nodes and their
location (system topology) as well. A proper selection of both node-related (transmit
power, operational frequency and bandwidth, integration time, etc.) and system-related
(node location, node cooperation) resources is an important design task, which forms
the major focus of this thesis.
The first part of this dissertation is dedicated to the development of the radar
network performance assessment tool,while the second part provides the framework for
radar network topology optimization. The potential accuracy of the target parameters
estimation has been used for radar network performance assessment. The developed
tool incorporates parameters of a single radar node as well as system parameters
(positions of the nodes and their cooperation), evaluated using Cramér-Rao lower
bound. Using the tools developed, performance of different types of radar networks have
been studied and compared in this thesis. For the radar network topology optimization
several convex and greedy algorithms have been used, making the optimization
approach versatile. Validation and performance comparison of the optimization
algorithms have been performed in this thesis.
The results obtained in this research can be used to evaluate the potential
performance of radar networks for different applications and provide a solution to key
problems of their topology design.
Original languageEnglish
Supervisors/Advisors
Award date13 Dec 2016
Print ISBNs978-94-6186-751-3
DOIs
Publication statusPublished - 2016

    Research areas

  • radar networks, convex optimization, greedy optimization, Cramér-Rao lower bound, frame potential

ID: 9219893