By Topic

Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory

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)
Huawei Guo ; Sch. of Electron., Inf. & Electr. Eng., Shanghai Jiao Tong Univ. ; Wenkang Shi ; Yong Deng

This paper presents a new framework for sensor reliability evaluation in classification problems based on evidence theory (or the Dempster-Shafer theory of belief functions). The evaluation is treated as a two-stage training process. First, the authors assess the static reliability from a training set by comparing the sensor classification readings with the actual values of data, which are both represented by belief functions. Information content contained in the actual values of each target is extracted to determine its influence on the evaluation. Next, considering the ability of the sensor to understand a dynamic working environment, the dynamic reliability is evaluated by measuring the degree of consensus among a group of sensors. Finally, the authors discuss why and how to combine these two kinds of reliabilities. A significant improvement using the authors' method is observed in numerical simulations as compared with the recently proposed method

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:36 ,  Issue: 5 )