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)
  
 
{| border=1 width=100%
 
{| border=1 width=100%
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===== Monday =====
 
===== Monday =====
 
<ol type="A">
 
<ol type="A">
<li>test</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)
 
|}
 
|}
 
* {{cds110b-wi08 pdfs placeholder|hw7.pdf|HW #7}} (due 5 Mar 08)
 
  
 
== 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
  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. Information filters
    • Problem setup
    • Kalman filter derivation
  2. Examples
    • Sensor fusion example revisited
    • Sensor fusion in Alice (Gillula + DGC07)

References and Further Reading

== Frequently Asked Questions ==