By Topic

A Bayesian approach to extended object tracking and tracking of loosely structured target groups

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)
Koch, W. ; FGAN-FKIE, Wachtberg, Germany ; Saul, R.

In algorithms for tracking and sensor data fusion the targets to be tracked are usually considered as point source objects; i.e., compared to the sensor resolution their extension is neglected. Due to the increasing resolution capabilities of modern sensors, however, this assumption is often not valid: different scattering centers of an object can cause distinct detections when passing the signal processing chain. Examples of extended targets are found in short-range applications (littoral surveillance, autonomous weapons, or robotics). As an extended target also a collectively moving, loosely structured group can be considered. This point of view is all the more appropriate, the smaller the mutual distances between the individual targets are due to the resulting data association and resolution conflicts any attempt of tracking the individual objects is no longer reasonable. With simulated sensor data produced by a partly resolvable aircraft formation the addressed phenomena are illustrated and a Bayesian solution to the resulting tracking problem is proposed. Ellipsoidal object extensions are modeled by random matrices and treated as additional state variables to be estimated or 'tracked'. We expect that the resulting tracking algorithms are relevant also for tracking large, collectively moving target swarms.

Published in:

Information Fusion, 2005 8th International Conference on  (Volume:1 )

Date of Conference:

25-28 July 2005