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Proceedings Paper

Designing teams of unattended ground sensors using genetic algorithms
Author(s): Ayse Selen Yilmaz; Brian N. McQuay; Annie S. Wu; John C. Sciortino
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Paper Abstract

Improvements in sensor capabilities have driven the need for automated sensor allocation and management systems. Such systems provide a penalty-free test environment and valuable input to human operators by offering candidate solutions. These abilities lead, in turn, to savings in manpower and time. Determining an optimal team of cooperating sensors for military operations is a challenging task. There is a tradeoff between the desire to decrease the cost and the need to increase the sensing capabilities of a sensor suite. This work focuses on unattended ground sensor networks consisting of teams of small, inexpensive sensors. Given a possible configuration of enemy radar, our goal isto generate sensor suites that monitor as many enemy radar as possible while minimizing cost. In previous work, we have shown that genetic algorithms (GAs) can be used to evolve successful teams of sensors for this problem. This work extends our previous work in two ways: we use an improved simulator containing a more accurate model of radar and sensor capabilities for out fitness evaluations and we introduce two new genetic operators, insertion and deletion, that are expected to improve the GA's fine tuning abilities. Empirical results show that our GA approach produces near optimal results under a variety of enemy radar configurations using sensors with varying capabilities. Detection percentage remains stable regardless of changes in the enemy radar placements.

Paper Details

Date Published: 12 April 2004
PDF: 10 pages
Proc. SPIE 5421, Intelligent Computing: Theory and Applications II, (12 April 2004); doi: 10.1117/12.542612
Show Author Affiliations
Ayse Selen Yilmaz, Univ. of Central Florida (United States)
Brian N. McQuay, Univ. of Central Florida (United States)
Annie S. Wu, Univ. of Central Florida (United States)
John C. Sciortino, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 5421:
Intelligent Computing: Theory and Applications II
Kevin L. Priddy, Editor(s)

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