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

From MurrayWiki
Jump to: navigation, search
 
(2 intermediate revisions by the same user not shown)
Line 1: Line 1:
{{cds110b-wi08}} __NOTOC__
+
{{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.
  
Line 34: Line 34:
 
<p>
 
<p>
 
* {{cds110b-wi08 pdfs|L8-1_fusion.pdf|Lecture notes on sensor fusion}}
 
* {{cds110b-wi08 pdfs|L8-1_fusion.pdf|Lecture notes on sensor fusion}}
* {{cds110b-wi08 pdfs|L8-2_fusion.pdf|Lecture slides on information filters}}
+
* {{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)
 
* {{cds110b-wi08 pdfs|hw7.pdf|HW #7}} (due 5 Mar 08)
 
</p>
 
</p>

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