Difference between revisions of "CDS 110b: Sensor Fusion"

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{{cds110b-wi08 lecture|prev=Kalman Filters|next=Robust Performance}}
In this lecture we show how the Kalman filter can be used for sensor fusion and explore some variations on the basic Kalman filter, including the extended Kalman filter. __NOTOC__
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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.
  
== Lecture Outline ==
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{| border=1 width=100%
<ol type=I>
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|- valign=top
<li> Sensor fusion using Kalman filters
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| width=50% |
<li> The extended Kalman filter
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===== Monday =====
* Ducted fan example: {{cds110b-pdfs|dfan_kf.m|dfan_kf.m}}, {{cds110b-pdfs|pvtol.m|pvtol.m}}
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<ol type="A">
<li> Parameter estimation using EKF
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<li>Discrete-time Kalman filter</li>
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* Discrete-time stochastic systems
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* Main theorem (following AM08)
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* Predictor-corrector form
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<li>Sensor fusion</li>
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* Problem setup {{to}} inverse covariance weighting
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* Example: TBD
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<li>Variations</li>
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* Multi-rate filtering and filtering with data loss
 
</ol>
 
</ol>
 
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|
== Lecture Materials ==
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===== Wednesday =====
* {{cds110b-pdfs|L6-1_sensor.pdf|Lecture presentation}} ({{cds110b-pdfs|L6-1_sensor.mp3|MP3}})
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<ol type="A">
* {{cds110b-pdfs|kalman.pdf|Lecture Notes on Kalman Filters}}
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<li>Application: Autonomous driving</li>
* Reading: Friedland, Chapter 11
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* Low-level sensor fusion in Alice (Gillula)
* {{cds110b-pdfs|hw5.pdf|HW #5}}, due 13 Feb (Mon)
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* Sensor fusion for urban driving (DGC07)
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<li>Information filters</li>
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* Problem setup
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* Kalman filter derivation
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* Sensor fusion example revisited
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<li>Modern extensions of Kalman filtering</li>
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* Moving horizon estimation
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* Particle filters
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</ol>
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|}
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<p>
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* {{cds110b-wi08 pdfs|L8-1_fusion.pdf|Lecture notes on sensor fusion}}
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* {{cds110b-wi08 pdfs|L8-2_kfexts.pdf|Lecture slides on applications and extensions of Kalman filters}}
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* {{cds110b-wi08 pdfs|hw7.pdf|HW #7}} (due 5 Mar 08)
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</p>
  
 
== References and Further Reading ==
 
== References and Further Reading ==
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* R. M. Murray, ''Optimization-Based Control''. Preprint, 2008: {{obc08 pdfs|stochastic_25Feb08.pdf|Chapter 5 - Sensor Fusion}}
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* {{cds110b-wi07 pdfs|gro02_infofilter.pdf|Appendix}} from [http://www.grasp.upenn.edu/~bpg/ Ben Grochalsky's] thesis on information filter.
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* [[NCS:_Kalman_Filtering|CDS 270-2 (Networked Control Systems) page on Kalman Filtering]] - provides additional notes and lecture materials (including some nice references)
  
 
== Frequently Asked Questions ==
 
== Frequently Asked Questions ==

Latest revision as of 03:23, 2 March 2008

CDS 110b Schedule Project

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
  1. Discrete-time Kalman filter
    • Discrete-time stochastic systems
    • Main theorem (following AM08)
    • Predictor-corrector form
  2. Sensor fusion
    • Problem setup → inverse covariance weighting
    • Example: TBD
  3. Variations
    • Multi-rate filtering and filtering with data loss
Wednesday
  1. Application: Autonomous driving
    • Low-level sensor fusion in Alice (Gillula)
    • Sensor fusion for urban driving (DGC07)
  2. Information filters
    • Problem setup
    • Kalman filter derivation
    • Sensor fusion example revisited
  3. Modern extensions of Kalman filtering
    • Moving horizon estimation
    • Particle filters

References and Further Reading

Frequently Asked Questions