EECI09: Distributed estimation and sensor fusion: Difference between revisions
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* <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> | * <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> | ||
* <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>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 == | ||
* [[EECI08: Distributed Estimation and Control | * [[EECI08: Distributed Estimation and Control]] - 2008 lecture page | ||
* [[CDS 270-4: Distributed Kalman Filtering]] - lecture notes for CDS 270, Spring 2008 (R. Murray) | * [[CDS 270-4: Distributed Kalman Filtering]] - lecture notes for CDS 270, Spring 2008 (R. Murray) | ||
Latest revision as of 09:06, 18 March 2009
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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 slides: Lecture Summary
- Notes on distributed estimation, Vijay Gupta (2006)
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
- EECI08: Distributed Estimation and Control - 2008 lecture page
- CDS 270-4: Distributed Kalman Filtering - lecture notes for CDS 270, Spring 2008 (R. Murray)
