Distributed Estimation: Difference between revisions
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== Reading == | == Reading == | ||
* <p>[http://www.cds.caltech.edu/ | * <p>Please refresh the material covered by Henrik a couple of weeks ago[http://www.cds.caltech.edu/%7Emurray/wiki/images/b/b3/Stateestim.pdf].</p> | ||
* <p>[http://www.cds.caltech.edu/~yasi/papers/ACC_Draft.pdf On Dropping Noisy Packets in Kalman Filtering over a Wireless Fading Channel], Yasamin Mostofi and Richard Murray, Proceedings of American Control Conference (ACC), June 2005.</p> | * <p>[http://www.cds.caltech.edu/~yasi/papers/ACC_Draft.pdf On Dropping Noisy Packets in Kalman Filtering over a Wireless Fading Channel], Yasamin Mostofi and Richard Murray, Proceedings of American Control Conference (ACC), June 2005.</p> | ||
* <p>[http://www.cds.caltech.edu/~yasi/papers/secon.pdf Effect of Time-Varying Fading Channels on the Control Performance of a Mobile Sensor Node], Yasamin Mostofi and Richard Murray, Proceedings of IEEE 1st International Conference on Sensor and Ad Hoc Communications and Networks (Secon), October 2004, Santa Clara, CA.</p> | * <p>[http://www.cds.caltech.edu/~yasi/papers/secon.pdf Effect of Time-Varying Fading Channels on the Control Performance of a Mobile Sensor Node], Yasamin Mostofi and Richard Murray, Proceedings of IEEE 1st International Conference on Sensor and Ad Hoc Communications and Networks (Secon), October 2004, Santa Clara, CA.</p> | ||
Revision as of 17:31, 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].
On Dropping Noisy Packets in Kalman Filtering over a Wireless Fading Channel, Yasamin Mostofi and Richard Murray, Proceedings of American Control Conference (ACC), June 2005.
Effect of Time-Varying Fading Channels on the Control Performance of a Mobile Sensor Node, Yasamin Mostofi and Richard Murray, Proceedings of IEEE 1st International Conference on Sensor and Ad Hoc Communications and Networks (Secon), October 2004, Santa Clara, CA.
