Tim Baur
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Tim Baur has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Johannes Reuter
Institute of System Dynamics, HTWG Konstanz
2 shared publications - Patrick Hoher
Institute of System Dynamics, University of Applied Sciences Konstanz, D-78462 Konstanz, Germany
2 shared publications - Dennis Griesser1 shared publication
- Felix Govaers
FKIE, Fraunhofer-Institut für Kommunikation, Informationsverarbeitung und Ergonomie, Wachtberg, Germany
1 shared publication - Hannes Homburger
Institute of System Dynamics, HTWG Konstanz
1 shared publication - Moritz Diehl
Department of Microsystems Engineering (IMTEK) and Department of Mathematics, University of Freiburg, Freiburg, Germany
1 shared publication
Latest Publications
- Open Access
- 3 April 2025
- Published by Elsevier in Ocean Engineering
- Vol. 328
Solgenia—A test vessel toward energy-efficient autonomous water taxi applications
- 3 April 2025
- Published by Elsevier in Ocean Engineering
- Vol. 328
Open AccessSolgenia—A test vessel toward energy-efficient autonomous water taxi applications
- 23 December 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Signal Processing
- Vol. 70 (1053587X) , 228-239
Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Virtual Measurement Model
Random matrices are widely used to estimate the extent of an elliptically contoured object. Usually, it is assumed that the measurements follow a normal distribution, with its standard deviation being proportional to the object’s extent. However, the random matrix approach can filter the center of gravity and the covariance matrix of measurements independently of the measurement model. This work considers the whole chain from data acquisition to the linear Kalman Filter with extension estimation as a reference plant. The input is the (unknown) ground truth (position and extent). The output is the filtered center of gravity and the filtered covariance matrix of the measurement distribution. A virtual measurement model emulates the behavior of the reference plant. The input of the virtual measurement model is adapted using the proposed algorithm until the output parameters of the virtual measurement model match the result of the reference plant. After the adaptation, the input to the virtual measurement model is considered an estimation for position and extent. The main contribution of this paper is the reference model concept and an adaptation algorithm to optimize the input of the virtual measurement model.
- 23 December 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Signal Processing
- Vol. 70 (1053587X) , 228-239
Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Virtual Measurement Model
Random matrices are widely used to estimate the extent of an elliptically contoured object. Usually, it is assumed that the measurements follow a normal distribution, with its standard deviation being proportional to the object’s extent. However, the random matrix approach can filter the center of gravity and the covariance matrix of measurements independently of the measurement model. This work considers the whole chain from data acquisition to the linear Kalman Filter with extension estimation as a reference plant. The input is the (unknown) ground truth (position and extent). The output is the filtered center of gravity and the filtered covariance matrix of the measurement distribution. A virtual measurement model emulates the behavior of the reference plant. The input of the virtual measurement model is adapted using the proposed algorithm until the output parameters of the virtual measurement model match the result of the reference plant. After the adaptation, the input to the virtual measurement model is considered an estimation for position and extent. The main contribution of this paper is the reference model concept and an adaptation algorithm to optimize the input of the virtual measurement model.