EECI09: Distributed estimation and sensor fusion: Difference between revisions

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{{eeci-sp09 header|prev=Estimation over networks|next=Information theoretic tools for networked control}}
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==  Lecture Materials ==
==  Lecture Materials ==
* Lecture slides: [[Media:Lecture_dist_estimation.pdf|Lecture Summary]]
* Lecture slides: [[Media:Lecture_dist_estimation.pdf|Lecture Summary]]
* [http://www.cds.caltech.edu/~murray/courses/eeci-sp08/L9_distributed.pdf Notes on distributed estimation], Vijay Gupta (2006)


== Further Reading ==
== Further Reading ==
* <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>K.-C. Chang, R. K. Saha and Y. Bar-Shalom, "On Optimal Track-to-Track Fusion,"  ''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>"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>
* <p>C.-Y. Chong, S. Mori, W. H. Barker and K.-C. Chang, "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>
* <p>R. Olfati-Saber, [http://engineering.dartmouth.edu/~olfati/papers/cdc07_dkf.pdf "Distributed Kalman Filtering for Sensor Networks"], 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>
* <p> Rao, Durrant-Whyte and Sheen, "A Fully Decentralized Multi-Sensor System For Tracking and Surveillance", ''International Journal of Robotics Research'', 1993.  This papers makes use of the information form of the Kalman filter to formulate a Kalman filter with no central fusion node but full connectivity.</p>


==  Additional Information ==  
==  Additional Information ==  
* [http://home.cwru.edu/ncs/ Networked Control Systems Repository] (M. Branicky and S. Phillipps)
* [[EECI08: Distributed Estimation and Control]] - 2008 lecture page
* [[EECI08: Introduction to Networked Control Systems|2008 lecture page]]
* [[CDS 270-4: Distributed Kalman Filtering]] - lecture notes for CDS 270, Spring 2008 (R. Murray)
* Additional links to external information

Latest revision as of 09:06, 18 March 2009

Prev: Cooperative control Course home Next: Information systems

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

  • K.-C. Chang, R. K. Saha and Y. Bar-Shalom, "On Optimal Track-to-Track Fusion," 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.

  • C.-Y. Chong, S. Mori, W. H. Barker and K.-C. Chang, "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.

  • R. Olfati-Saber, "Distributed Kalman Filtering for Sensor Networks", 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.

  • Rao, Durrant-Whyte and Sheen, "A Fully Decentralized Multi-Sensor System For Tracking and Surveillance", International Journal of Robotics Research, 1993. This papers makes use of the information form of the Kalman filter to formulate a Kalman filter with no central fusion node but full connectivity.

Additional Information