Difference between revisions of "CDS 110b: Sensor Fusion"
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−  {{cds110bwi08}}  +  {{cds110bwi08 lectureprev=Kalman Filtersnext=Robust Performance}} 
In this set of lectures we discuss discretetime random processes and the discretetime Kalman filter. We use the discretetime formulation to consider problems in (multirate) 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 discretetime random processes and the discretetime Kalman filter. We use the discretetime formulation to consider problems in (multirate) 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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<li>Application: Autonomous driving</li>  <li>Application: Autonomous driving</li>  
* Lowlevel sensor fusion in Alice (Gillula)  * Lowlevel sensor fusion in Alice (Gillula)  
−  * Sensor fusion for urban driving  +  * Sensor fusion for urban driving (DGC07) 
<li>Information filters</li>  <li>Information filters</li>  
* Problem setup  * Problem setup  
* Kalman filter derivation  * Kalman filter derivation  
* Sensor fusion example revisited  * Sensor fusion example revisited  
−  <li>  +  <li>Modern extensions of Kalman filtering</li> 
+  * Moving horizon estimation  
+  * Particle filters  
</ol>  </ol>  
}  }  
<p>  <p>  
−  * {{cds110bwi08 pdfs  +  * {{cds110bwi08 pdfsL81_fusion.pdfLecture notes on sensor fusion}} 
−  * {{cds110bwi08 pdfs  +  * {{cds110bwi08 pdfsL82_kfexts.pdfLecture slides on applications and extensions of Kalman filters}} 
−  * {{cds110bwi08 pdfs  +  * {{cds110bwi08 pdfshw7.pdfHW #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 discretetime random processes and the discretetime Kalman filter. We use the discretetime formulation to consider problems in (multirate) 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

Wednesday

 Lecture notes on sensor fusion
 Lecture slides on applications and extensions of Kalman filters
 HW #7 (due 5 Mar 08)
References and Further Reading
 R. M. Murray, OptimizationBased Control. Preprint, 2008: Chapter 5  Sensor Fusion
 Appendix from Ben Grochalsky's thesis on information filter.
 CDS 2702 (Networked Control Systems) page on Kalman Filtering  provides additional notes and lecture materials (including some nice references)