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}}
{{eeci-sp09 header|prev=Estimation over networks|next=Review of information theory and communications}}


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Revision as of 16:16, 12 March 2009

Prev: Estimation over networks Course home Next: Review of information theory and communications

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

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