Difference between revisions of "CDS 110b: Sensor Fusion"
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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. | ||
| + | |||
| + | * {{cds110b-wi08 pdfs placeholder|hw7.pdf|HW #7}} (due 5 Mar 08) | ||
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===== 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)
