Indiana University-Purdue University Indianapolis (IUPUI) / Finding the optimal placement of receiving assets among transmitting targets in
a three-dimensional (3D) space is a complex and dynamic problem that is solved in
this work. The placement of assets in R^6 to optimize the best coverage of transmitting
targets requires the placement in 3D-spatiality, center frequency assignment,
and antenna azimuth and elevation orientation, with respect to power coverage at
the receiver without overloading the feed-horn, maintaining suficient power sensitivity
levels, and maintaining terrain constraints. Further complexities result from
the human-user having necessary and time-constrained knowledge to real-world conditions
unknown to the problem space, such as enemy positions or special targets,
resulting in the requirement of the user to interact with the solution convergence
in some fashion. Particle Swarm Optimization (PSO) approaches this problem with
accurate and rapid approximation to the electronic warfare asset allocation problem
(EWAAP) with near-real-time solution convergence using a linear combination of
weighted components for tness comparison and particles representative of asset con-
gurations. Finally, optimizing the weights for the tness function requires the use
of unsupervised machine learning techniques to reduce the complexity of assigning a
tness function using a Meta-PSO. The result of this work implements a more realistic
asset allocation problem with directional antenna and complex terrain constraints
that is able to converge on a solution on average in 488.7167+-15.6580 ms and has a
standard deviation of 15.3901 for asset positions across solutions.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/15929 |
| Date | 05 1900 |
| Creators | Boler, William M. |
| Contributors | Christopher, Lauren, King, Brian, Salama, Paul |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Rights | Attribution 3.0 United States, http://creativecommons.org/licenses/by/3.0/us/ |
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