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

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{{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.
  
 
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{| border=1 width=100%
 
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===== Monday =====
 
===== Monday =====
 
<ol type="A">
 
<ol type="A">
<li>test</li>
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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>
 
|
 
|
 
===== Wednesday =====
 
===== Wednesday =====
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<ol type="A">
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<li>Application: Autonomous driving</li>
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* Low-level sensor fusion in Alice (Gillula)
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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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<p>
* {{cds110b-wi08 pdfs placeholder|hw7.pdf|HW #7}} (due 5 Mar 08)
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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 ==

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