By Topic

Distributed source number estimation for multiple target detection in sensor networks

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)
Xiaoling Wang ; Dept. of Electr. & Comput. Eng., Tennessee Univ., Knoxville, TN, USA ; Hairong Qi ; Hongtao Du

Multiple target detection in sensor networks is a challenging problem since the signal captured by individual sensor node is normally a linear/nonlinear weighted mixture of the source signals. Independent component analysis (ICA) has been widely used to solve the source estimation problem but most of the algorithms assume the number of sources is fixed and equals to the number of observations which generally is not the case in sensor networks. Even though several methods are put forward for the source number estimation, the centralized scheme hinders their application in sensor networks due to the extremely constrained resource and scalability issues. In this paper, a distributed source number estimation framework is developed, where the local estimation is generated within each cluster and a fusion algorithm is performed to combine the local results. We derive a posterior probability fusion method based on Bayes theorem and compare it with the Dempster rule of combination. Experimental results show that using the distributed framework, the confidence of source number estimation is improved over the centralized approach while at the same time, the network traffic can be significantly reduced and resources can be conserved.

Published in:

Statistical Signal Processing, 2003 IEEE Workshop on

Date of Conference:

28 Sept.-1 Oct. 2003