By Topic

Kalman filter based on adaptive quantized information

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)
Xianfeng Tang ; Inst. of Inf. & Control, Hangzhou Dianzi Univ., Hangzhou ; Quanbo Ge ; Chenglin Wen

When dealing with decentralized estimation problem of dynamic stochastic process in a sensor network, it is important to reduce the cost of communicating the local information due to bandwidth constraints. Thus, only quantized messages of the original information from local sensor are available. For a class of vector state-vector observation model, an adaptive quantization strategy and sequential filter technique are introduced to design fusion algorithms in this paper. According to different forms of original information, two suboptimal Kalman filters are presented based on quantized measurements (KFQM) and quantized innovations (KFQI) respectively. In contrast, the latter has better estimation accuracy under the same bandwidth constraints because of the less information loss while quantizing innovations. Computer simulations show the effectiveness of both methods.

Published in:

Communication Technology, 2008. ICCT 2008. 11th IEEE International Conference on

Date of Conference:

10-12 Nov. 2008