CDS 110b: Sensor Fusion: Difference between revisions
From Murray Wiki
Jump to navigationJump to search
Line 27: | Line 27: | ||
* Kalman filter derivation | * Kalman filter derivation | ||
* Sensor fusion example revisited | * Sensor fusion example revisited | ||
<li> | <li>Modern extensions of Kalman filtering</li> | ||
* Moving horizon estimation | |||
* Particle filters | |||
</ol> | </ol> | ||
|} | |} |
Revision as of 15:50, 24 February 2008
CDS 110b | Schedule | Project | Course Text |
In this set of lectures we discuss discrete-time random processes and the discrete-time Kalman filter. We use the discrete-time formulation to consider problems in (multi-rate) sensor fusion and sensor fusion in the presence of information/packet loss. We also introduce the information filter, which provides a particularly simple method for sensor fusion.
Monday
|
Wednesday
|
- Lecture notes on sensor fusion
- Lecture slides on information filters
- HW #7 (due 5 Mar 08)
References and Further Reading
- R. M. Murray, Optimization-Based Control. Preprint, 2008: Chapter 5 - Sensor Fusion
- Appendix from Ben Grochalsky's thesis on information filter.
- CDS 270-2 (Networked Control Systems) page on Kalman Filtering - provides additional notes and lecture materials (including some nice references)