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

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* Kalman filter derivation
 
* Kalman filter derivation
 
* Sensor fusion example revisited
 
* Sensor fusion example revisited
<li>Particle filters</li>
+
<li>Modern extensions of Kalman filtering</li>
 +
* Moving horizon estimation
 +
* Particle filters
 
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</ol>
 
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Revision as of 15:50, 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.

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
  2. Information filters
    • Problem setup
    • Kalman filter derivation
    • Sensor fusion example revisited
  3. Modern extensions of Kalman filtering
    • Moving horizon estimation
    • Particle filters

  • Lecture notes on sensor fusion
  • Lecture slides on information filters
  • HW #7 (due 5 Mar 08)

References and Further Reading

Frequently Asked Questions