| Beschreibung |
Course content:
• State space models and sensor models
• Least Square Estimation
• Probability theory basics
• General Bayesian Filters
• Kalman Filter and variants
• Non-parametric filters (particle filters)
• Introduction to Data Association
Learning objectives:
Subject-specific competencies:
Students…
• Know the hierarchical structure of data fusion systems
• are able to derive the Bayesian filter equation
• can implement various types of Kalman filters
• can implement particle filters
• are able to evaluate the performance of stochastic filters
Methodological competencies:
• have gained an understanding of modern methods for information fusion
• know the sub modules for data association, track instantiation and termination
Interdisciplinary competencies:
• are aware of the cross connection between data acquisition and suitable fusion methods
• can apply data fusion methodology to interdisciplinary topics in robotics and automotive
Literature and other sources of information:
• Liggins, M., Hall, D. Llinas, J.: Handbook of Multisensor Data Fusion – Theory and Practice, 2nd. Ed. 2008, CRC Press, ISBN 978-1-4200-5308-1
• Koch, W.: Tracking and Sensor Data Fusion - Methodological Framework and Selected Applications, Springer 2014 ISBN 978-3-642-39271-9
• Gelb, A. Applied Optimal Estimation, The MIT Press 2001 ISBN 0-262-20027-9
• Anderson, B. Moore, J.: Optimal Filtering, Prentice Hall 1979 ISBN 0-13-638122-7
• Thrun, S., Burgard, W., Fox, D.:Probabilitic Robotics (Intelligent Robotics and Autonomous Agents), The MIT Press 2005, ISBN 978-3-642-39271-9 |
