CDS 110b: Sensor Fusion: Difference between revisions
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{{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. | ||
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===== Monday ===== | ===== Monday ===== | ||
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* Sensor fusion example revisited | * Sensor fusion example revisited | ||
* Sensor fusion in Alice (Gillula + DGC07) | * Sensor fusion in Alice (Gillula + DGC07) | ||
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* {{cds110b-wi08 pdfs placeholder|hw7.pdf|HW #7}} (due 5 Mar 08) | |||
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Revision as of 15:38, 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.
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Monday
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Wednesday
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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)
