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Proceedings Paper

Using a multi-objective genetic algorithm for developing aerial sensor team search strategies
Author(s): Jeffrey P. Ridder; Jared A. Herweg; John C. Sciortino
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Paper Abstract

Finding certain associated signals in the modern electromagnetic environment can prove a difficult task due to signal characteristics and associated platform tactics as well as the systems used to find these signals. One approach to finding such signal sets is to employ multiple small unmanned aerial systems (UASs) equipped with RF sensors in a team to search an area. The search environment may be partially known, but with a significant level of uncertainty as to the locations and emissions behavior of the individual signals and their associated platforms. The team is likely to benefit from a combination of using uncertain a priori information for planning and online search algorithms for dynamic tasking of the team. Two search algorithms are examined for effectiveness: Archimedean spirals, in which the UASs comprising the team do not respond to the environment, and artificial potential fields, in which they use environmental perception and interactions to dynamically guide the search. A multi-objective genetic algorithm (MOGA) is used to explore the desirable characteristics of search algorithms for this problem using two performance objectives. The results indicate that the MOGA can successfully use uncertain a priori information to set the parameters of the search algorithms. Also, we find that artificial potential fields may result in good performance, but that each of the fields has a different contribution that may be appropriate only in certain states.

Paper Details

Date Published: 1 May 2008
PDF: 13 pages
Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 696408 (1 May 2008); doi: 10.1117/12.785362
Show Author Affiliations
Jeffrey P. Ridder, Innovating Systems, Inc. (United States)
Jared A. Herweg, Air Force Research Lab. (United States)
John C. Sciortino, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 6964:
Evolutionary and Bio-Inspired Computation: Theory and Applications II
Misty Blowers; Alex F. Sisti, Editor(s)

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