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

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{{cds110b-wi06}}
{{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__
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 ==
{| border=1 width=100%
<ol type=I>
|- valign=top
<li> Sensor fusion using Kalman filters
| width=50% |
<li> The extended Kalman filter
===== Monday =====
* Ducted fan example: {{cds110b-pdf|dfan_kf.m|dfan_kf.m}}, {{cds110b-pdf|pvtol.m|pvtol.m}}
<ol type="A">
<li> Parameter estimation using EKF
<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>
 
|
== Lecture Materials ==
===== Wednesday =====
* {{cds110b-pdfs|L6-1_sensor.pdf|Lecture presentation}} ({{cds110b-pdfs|L6-1_sensor.mp3|MP3}})
<ol type="A">
* {{cds110b-pdfs|kalman.pdf|Lecture Notes on Kalman Filters}}
<li>Application: Autonomous driving</li>
* Reading: Friedland, Chapter 11
* 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}}
* {{cds110b-wi07 pdfs|gro02_infofilter.pdf|Appendix}} from [http://www.grasp.upenn.edu/~bpg/ Ben Grochalsky's] thesis on information filter.
* [[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 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
  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