Distributed Estimation: Difference between revisions
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{{cds270-2 header}} <!-- Generates the header, including table of contents and link back to main page --> | |||
<!-- Enter a 1 paragraph description of the contents of the lecture. Make sure to include any key concepts, so that the wiki search feature will pick them up --> | |||
In this lecture, we will take a look at the fundamentals of | In this lecture, we will take a look at the fundamentals of | ||
distributed estimation. We will consider a random variable being | distributed estimation. We will consider a random variable being | ||
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variable. Towards the end, we will look at the problem of dynamic sensor fusion, i.e., distributing | 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. | a Kalman filter so that multiple sensors can estimate a dynamic random variable. | ||
== Lecture Materials == | |||
<!-- Include links to materials that you used in your lecture. At a minimum, this should include a link to your lecture presentation. You might also include links to MATLAB scripts or other source code that students would find useful --> | |||
<!-- Sample lecture link: * [[Media:L1-1_Intro.pdf|Lecture: Networked Control Systems: Course Overview]] --> | |||
* [[Media:Lecture2_Mostofi.pdf |Lecture: Optimum Receiver Design for Estimation over Wireless Links]] | |||
== Reading == | |||
* <p>[http://www.cds.caltech.edu/~yasi/papers/CDC_Draft.pdf Receiver Design Principles for Estimation over Fading Channels], Yasamin Mostofi and Richard Murray, Proceedings of Conference on Decision and Control (CDC), December 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> | |||
Revision as of 17:16, 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
Receiver Design Principles for Estimation over Fading Channels, Yasamin Mostofi and Richard Murray, Proceedings of Conference on Decision and Control (CDC), December 2005.
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.
