By Topic

Learning sensor-detection policies

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Malhotra, R. ; WL-AACF, WPAFB, OH, USA ; Blasch, E.P. ; Johnson, J.D.

Tactical aircraft pilots frequently perform complex sequential support tasks to obtain accurate and timely integrated sensor information about the local environment. When workloads are heavy, offloading these sensor-support tasks to an automated sensor management system would enhance performance and situational awareness. Reinforcement learning, a family of machine learning techniques, offers a way to learn to conduct sensor-support tasks despite sensor complexities by mapping a situation to an action. This paper applies reinforcement learning to a simplified target-detection policy, compares simulated performances of the learned technique to that of an optimal and an uninformed detection policy, and draws conclusions for future research directions

Published in:

Aerospace and Electronics Conference, 1997. NAECON 1997., Proceedings of the IEEE 1997 National  (Volume:2 )

Date of Conference:

14-18 Jul 1997