Using a multi-objective genetic algorithm for developing aerial sensor team search strategies

Jeffrey P. Ridder; Jared A. Herweg; John C. Sciortino Jr.

doi:10.1117/12.785362

1 May 2008 Using a multi-objective genetic algorithm for developing aerial sensor team search strategies

Jeffrey P. Ridder, Jared A. Herweg, John C. Sciortino Jr.

Proceedings Volume 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II; 696408 (2008) https://doi.org/10.1117/12.785362
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

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.

Citation Download Citation

Jeffrey P. Ridder, Jared A. Herweg, and John C. Sciortino Jr. "Using a multi-objective genetic algorithm for developing aerial sensor team search strategies", Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 696408 (1 May 2008); https://doi.org/10.1117/12.785362

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
13 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Radar

Algorithm development

Genetic algorithms

Evolutionary algorithms

Sensors

Detection and tracking algorithms

Missiles

Show All Keywords

Keywords/Phrases

Search In:

Publication Years