By Topic

Chernoff information-based optimization of sensor networks for distributed detection

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

2 Author(s)
Fabeck, G. ; Inst. for Theor. Inf. Technol., RWTH Aachen Univ., Aachen, Germany ; Mathar, R.

This paper addresses the scalable optimization of sensor networks for distributed detection applications. In the general case, the jointly optimum solution for the local sensor decision rules and the fusion rule is extremely difficult to obtain and does not scale with the number of sensors. In this paper, we consider optimization of distributed detection systems based on a local metric for sensor detection performance. Derived from the asymptotic error exponents in binary hypothesis testing, the Chernoff information emerges as an appropriate metric for sensor detection quality. By locally maximizing the Chernoff information at each sensor and thus decoupling the optimization problem, scalable solutions are obtained which are also robust with respect to the underlying prior probabilities. By considering the problem of detecting a deterministic signal in the presence of Gaussian noise, a detailed numerical study illustrates the feasibilty of the proposed approach.

Published in:

Signal Processing and Information Technology (ISSPIT), 2009 IEEE International Symposium on

Date of Conference:

14-17 Dec. 2009