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
Bayesian fusion of empirical distributions based on local density reconstruction
TL;DR: A generalized multiplication procedure that mutually reweights appropriate points of one density by local density values of the other density, which is symmetric in the sense that it uses points from both densities.
Journal ArticleDOI
Predictive tracking with improved motion models for optical belt sorting
Florian Pfaff,Christoph Pieper,Georg Maier,Benjamin Noack,Robin Gruna,Harald Kruggel-Emden,Uwe D. Hanebeck,Siegmar Wirtz,Viktor Scherer,Thomas Längle,Jürgen Beyerer +10 more
TL;DR: Analysis of simulation-based ground truth data of the motion of different bulk materials and derive models specifically tailored to the generation of accurate predictions for particles traveling on a conveyor belt shows that the constant velocity model and constant acceleration model can be outperformed by utilizing the similarities in the motion behavior of particles of the same type.
Posted Content
Truncated Moment Problem for Dirac Mixture Densities with Entropy Regularization.
TL;DR: An algorithm is proposed for efficiently calculating Dirac mixture densities maintaining these moments while providing a homogeneous coverage of the state space.
Proceedings ArticleDOI
Shape Estimation and Tracking using Spherical Double Fourier Series for Three-Dimensional Range Sensors
TL;DR: In this article, a measurement model based on spherical double Fourier series (DFS) for estimating the 3D shape of a target concurrently with its kinematic state is introduced.
Book ChapterDOI
Dirac Mixture Approximation for Nonlinear Stochastic Filtering
TL;DR: This work presents a filter for estimating the state of nonlinear dynamic systems based on optimal recursive approximation the state densities by means of Dirac mixture functions in order to allow for a closed form solution of the prediction and filter step.