CDS 110b: Sensor Fusion: Difference between revisions
From Murray Wiki
Jump to navigationJump to search
No edit summary |
No edit summary |
||
| Line 1: | Line 1: | ||
{{cds110b-wi08}} __NOTOC__ | {{cds110b-wi08}} __NOTOC__ | ||
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. | 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. | ||
* {{cds110b-wi08 pdfs placeholder|hw7.pdf|HW #7}} (due 5 Mar 08) | |||
{| border=1 width=100% | {| border=1 width=100% | ||
| Line 7: | Line 9: | ||
===== Monday ===== | ===== Monday ===== | ||
<ol type="A"> | <ol type="A"> | ||
<li> | <li>Discrete-time Kalman filter</li> | ||
* Discrete-time stochastic systems | |||
* Main theorem (following AM08) | |||
* Predictor-corrector form | |||
<li>Sensor fusion</li> | |||
* Problem setup {{to}} inverse covariance weighting | |||
* Example: TBD | |||
<li>Variations</li> | |||
* Multi-rate filtering and filtering with data loss | |||
</ol> | </ol> | ||
| | | | ||
===== Wednesday ===== | ===== Wednesday ===== | ||
<ol type="A"> | |||
<li>Information filters</li> | |||
* Problem setup | |||
* Kalman filter derivation | |||
<li>Examples</li> | |||
* Sensor fusion example revisited | |||
* Sensor fusion in Alice (Gillula + DGC07) | |||
|} | |} | ||
== References and Further Reading == | == References and Further Reading == | ||
Revision as of 15:37, 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.
- HW #7 (due 5 Mar 08)
Monday
|
Wednesday
|
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)
