By Topic

A comparison of a possibilistic and a probabilistic classifier in a multitarget tracking environment

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
$33 $33
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)
A. G. Huizing ; TNO-FEL, Netherlands ; A. Theil ; F. C. A. Groen

Two different approaches to target classification and the representation of the uncertainty in the classification are compared. First, a probabilistic (Bayes) classifier is described that minimizes the average cost of the classification. Then, a possibilistic classifier is presented that minimizes the maximum possible cost given the possibility distributions of the attributes. An evaluation of the performance of both classifiers shows the sensitivity to deviations from a priori knowledge that is employed. This sensitivity is particularly important in military scenarios where we deal with intelligent adversaries. Finally, the application of the probabilistic and possibilistic classifier to the plot-track association problem in a multitarget tracker is demonstrated with real radar data

Published in:

Radar 97 (Conf. Publ. No. 449)

Date of Conference:

14-16 Oct 1997