Cart (Loading....) | Create Account
Close category search window
 

Bayesian Approach for Data Fusion 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

2 Author(s)
Wu, J.K. ; Inst. for Infocomm Res., Singapore ; Wong, Y.F.

We formulate the target tracking based on received signal strength in the sensor networks using Bayesian network representation. Data fusion among the same type of sensors in an active sensor neighborhood is referred to as cross-sensor fusion, conceptualized as "cooperative fusion". This data fusion is embedded in the likelihood function derivation. Fusion of signals collected by multiple types of sensors are referred to as cross-modality fusion. It is "complementary", and represented by the contribution of their likelihood functions to the state update. The tracking algorithm is implemented using particle filter. Very good experimental results are obtained using sensor data

Published in:

Information Fusion, 2006 9th International Conference on

Date of Conference:

10-13 July 2006

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.