CDS 110b: Sensor Fusion

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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)
width-50%
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 ==