By Topic

Data Association for Multiple Sensor Types Using Fuzzy Logic

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)
Stubberud, S.C. ; ANZUS Inc., Poway, CA ; Kramer, K.A.

The concept of target tracking, a part of level 1 data fusion, is to combine measures from various sensors to form a coherent picture of the scene. A key component of the fusion problem is data association, the assignment of various measurements to existing target tracks. For the typical case in target association where both the target tracks and the measurements are described with Gaussian random variables, the standard association uses the chi2 metric, a weighted inner product of the residual formed by an estimated measurement and the true measurement. There are cases where the measurements are not well described as Gaussian random variables, including those from sensors that have uncertainties that are better approximated as uniform distributions or where the Gaussian distribution is corrupted by sensor blockage or target constraints. Based upon the proven concept of the chi2 metric, a straightforward fuzzy-logic-based association method is developed that can emulate this metric for Gaussian measurements but can be modified to address problems where the Gaussian assumption on the track and/or measurement is not appropriate

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:55 ,  Issue: 6 )