EECI09: Distributed estimation and sensor fusion: Difference between revisions

From Murray Wiki
Jump to navigationJump to search
No edit summary
No edit summary
Line 2: Line 2:


{{righttoc}}
{{righttoc}}
One paragraph overview of the lecture
In this lecture, we introduce the basics of distributed estimation. We consider both static sensor fusion and distributed Kalman filtering. We discuss some existing algorithms, and point out some open problems.


==  Lecture Materials ==
==  Lecture Materials ==
* Lecture slides: {{eeci-sp09 pdf|Ln_topic.pdf|Title}}
* Lecture slides: [[Media:Lecture_dist_estimation.pdf|Lecture Summary]]
* Links to anything else that is handed out in the lecture


== Further Reading ==
== Further Reading ==
* <p>[http://www.cds.caltech.edu/~murray/cdspanel Control in an Information Rich World], R. M. Murray (ed). SIAM, 2003. This book provides a high level description of some of the research challenges and opportunities in the field of control. The executive summary (Section 1) and the application sections on "Information and Networks" and "Robotics and Intelligent Machines" (Section 3.2 and 3.3) are particularly relevant.</p>
* <p>"On Optimal Track-to-Track Fusion," K. C. Chang, R. K. Saha and Y. Bar-Shalom, IEEE Transactions on Aerospace and Electronic Systems, AES-33:1271-1276, 1997. This paper provides a good overview of the correlation introduced by common process noise in dynamic sensor fusion.</p>
* <p>Second paper</p>
* <p>"Architectures and Algorithms for Track Association and Fusion," IEEE Aerospace and Electronic Systems Magazine, 15:5-13, 2000. This paper gives a nice overview of existing results on the track fusion problem.</p>
* [http://engineering.dartmouth.edu/~olfati/papers/cdc07_dkf.pdf "Distributed Kalman Filtering for Sensor Networks,"] R. Olfati-Saber, Proc. of the 46th IEEE Conference on Decision and Control, Dec. 2007. This paper provides the Kalman filter based dynamic sensor fusion algorithm discussed in the lecture.</p>


==  Additional Information ==  
==  Additional Information ==  

Revision as of 16:07, 12 March 2009

Prev: Estimation over networks Course home Next: Distributed protocols

In this lecture, we introduce the basics of distributed estimation. We consider both static sensor fusion and distributed Kalman filtering. We discuss some existing algorithms, and point out some open problems.

Lecture Materials

Further Reading

  • "On Optimal Track-to-Track Fusion," K. C. Chang, R. K. Saha and Y. Bar-Shalom, IEEE Transactions on Aerospace and Electronic Systems, AES-33:1271-1276, 1997. This paper provides a good overview of the correlation introduced by common process noise in dynamic sensor fusion.

  • "Architectures and Algorithms for Track Association and Fusion," IEEE Aerospace and Electronic Systems Magazine, 15:5-13, 2000. This paper gives a nice overview of existing results on the track fusion problem.

  • "Distributed Kalman Filtering for Sensor Networks," R. Olfati-Saber, Proc. of the 46th IEEE Conference on Decision and Control, Dec. 2007. This paper provides the Kalman filter based dynamic sensor fusion algorithm discussed in the lecture.

Additional Information

Retrieved from "https://murray.cds.caltech.edu/index.php?title=EECI09:_Distributed_estimation_and_sensor_fusion&oldid=9185"