Distributed Estimation
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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].
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.
