Distributed Estimation: Difference between revisions
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* <p>Consensus algorithms will be covered in detail in the class next week. We will also touch upon one such algorithm in passing. For more details, you can read this paper.[http://www.cds.caltech.edu/~murray/papers/2003f_om04-tac.html].</p> | * <p>Consensus algorithms will be covered in detail in the class next week. We will also touch upon one such algorithm in passing. For more details, you can read this paper.[http://www.cds.caltech.edu/~murray/papers/2003f_om04-tac.html].</p> | ||
* <p>Additional references are mentioned in the lecture notes. Most of them are available using IEEE Xplore[http://ieeexplore.ieee.org/Xplore/dynhome.jsp]. If you are unable to obtain any, please send [http://www.cds.caltech.edu/~vijay] | * <p>Additional references are mentioned in the lecture notes. Most of them are available using IEEE Xplore[http://ieeexplore.ieee.org/Xplore/dynhome.jsp]. If you are unable to obtain any, please send [[http://www.cds.caltech.edu/~vijay|me]] a mail.</p> | ||
Revision as of 17:40, 5 May 2006
| Prev: Optimum Receiver Design for Estimation over Wireless Links | Course Home | Next: Intro to Distributed Control |
In this lecture, we will take a look at the fundamentals of
distributed estimation. We will consider a random variable being
observed by mutiple sensors. Under the assumptions of Gaussian noises
and linear measurements, we will derive the weighted covariance
combination of estimators. We will then touch upon the issues of
distributed static sensor fusion and estimation of a dynamic random
variable. Towards the end, we will look at the problem of dynamic sensor fusion, i.e., distributing
a Kalman filter so that multiple sensors can estimate a dynamic random variable.
Lecture Materials
Reading
Please refresh the material covered by Henrik a couple of weeks ago[1].
Consensus algorithms will be covered in detail in the class next week. We will also touch upon one such algorithm in passing. For more details, you can read this paper.[2].
