CDS 110b: Sensor Fusion: Difference between revisions
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{{cds110b-wi08}} | {{cds110b-wi08 lecture|prev=Kalman Filters|next=Robust Performance}} | ||
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 | {| border=1 width=100% | ||
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===== Monday ===== | |||
<ol type="A"> | |||
<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> | |||
| | |||
===== Wednesday ===== | |||
<ol type="A"> | |||
<li>Application: Autonomous driving</li> | |||
* Low-level sensor fusion in Alice (Gillula) | |||
* Sensor fusion for urban driving (DGC07) | |||
<li>Information filters</li> | |||
* Problem setup | |||
* Kalman filter derivation | |||
* Sensor fusion example revisited | |||
<li>Modern extensions of Kalman filtering</li> | |||
* Moving horizon estimation | |||
* Particle filters | |||
</ol> | |||
|} | |||
<p> | |||
* {{cds110b-wi08 pdfs|L8-1_fusion.pdf|Lecture notes on sensor fusion}} | |||
* {{cds110b-wi08 pdfs|L8-2_kfexts.pdf|Lecture slides on applications and extensions of Kalman filters}} | |||
* {{cds110b-wi08 pdfs|hw7.pdf|HW #7}} (due 5 Mar 08) | |||
</p> | |||
== References and Further Reading == | == References and Further Reading == | ||
* R. M. Murray, ''Optimization-Based Control''. Preprint, 2008: {{obc08 pdfs|stochastic_25Feb08.pdf|Chapter 5 - Sensor Fusion}} | * R. M. Murray, ''Optimization-Based Control''. Preprint, 2008: {{obc08 pdfs|stochastic_25Feb08.pdf|Chapter 5 - Sensor Fusion}} | ||
* {{cds110b-wi07 pdfs|gro02_infofilter.pdf|Appendix}} from [http://www.grasp.upenn.edu/~bpg/ Ben Grochalsky's] thesis on information filter. | * {{cds110b-wi07 pdfs|gro02_infofilter.pdf|Appendix}} from [http://www.grasp.upenn.edu/~bpg/ Ben Grochalsky's] thesis on information filter. | ||
Latest revision as of 03:23, 2 March 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 applications and extensions of Kalman 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)
