By Topic

Random Weighting Method for Multisensor Data Fusion

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)
Shesheng Gao ; Sch. of Automatics, Northwestern Polytech. Univ., Xi''an, China ; Yongmin Zhong ; Wei Li

This paper presents a new data fusion method by adopting random weighting estimation for optimal weighted fusion of multisensor observation data. This method adjusts in real time the weights of individual sensors according to variations in estimated sensor variances to obtain optimal weight distribution. Theories of random weighting estimation are established for optimal data fusion through optimal weighting distribution. Algorithms of random weighting estimation are developed to calculate sensor variances for determination of optimal random weighting factors. The fusion result in least mean square error is achieved directly from multisensor observation data, without requirement of any prior knowledge on unknown parameters. The mean square error estimated by the proposed method is not only smaller than from each individual sensor, but also smaller than by the mean of multisensor observation data.

Published in:

Sensors Journal, IEEE  (Volume:11 ,  Issue: 9 )