U
Uwe D. Hanebeck
Researcher at Karlsruhe Institute of Technology
Publications - 575
Citations - 9054
Uwe D. Hanebeck is an academic researcher from Karlsruhe Institute of Technology. The author has contributed to research in topics: Kalman filter & Gaussian. The author has an hindex of 39, co-authored 549 publications receiving 7977 citations. Previous affiliations of Uwe D. Hanebeck include Technische Universität München & IAR Systems.
Papers
More filters
Proceedings ArticleDOI
Template Matching using Fast Normalized Cross Correlation
Kai Briechle,Uwe D. Hanebeck +1 more
TL;DR: Depending on the approximation, the algorithm can by far outperform Fourier-transform based implementations of the normalized cross correlation algorithm and it is especially suited to problems, where many different templates are to be found in the same image f.
Proceedings ArticleDOI
On entropy approximation for Gaussian mixture random vectors
TL;DR: This paper deals with a novel entropy approximation method for Gaussian mixture random vectors, which is based on a component-wise Taylor-series expansion of the logarithm of aGaussian mixture and on a splitting method of Gaussia mixture components.
Proceedings ArticleDOI
WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors
TL;DR: A pedestrian tracking framework based on particle filters is proposed, which extends the typical WLAN-based indoor positioning systems by integrating low-cost MEMS accelerometer and map information.
Proceedings Article
Shape tracking of extended objects and group targets with star-convex RHMs
Marcus Baum,Uwe D. Hanebeck +1 more
TL;DR: In this paper, a star-convex RHM is introduced for tracking star- Convex shape approximations of targets and Bayesian inference is performed by means of a Gaussian-assumed state estimator allowing for an efficient recursive closed-form measurement update.
Proceedings ArticleDOI
Random Hypersurface Models for extended object tracking
Marcus Baum,Uwe D. Hanebeck +1 more
TL;DR: In this paper, the authors introduce the concept of Random Hypersurface Models for extended targets, which assumes that each measurement source is an element of a randomly generated hypersurface.