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Kalman filtering over a packet dropping network: A probabilistic approach

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3 Author(s)
Ling Shi ; Control & Dynamical Syst., California Inst. of Technol., Pasadena, CA ; Epstein, M. ; Murray, R.M.

We consider the problem of state estimation of a discrete time process over a packet dropping network. Previous pioneering work on Kalman filtering with intermittent observations is concerned with the asymptotic behavior of E[Pk], i.e., the expected value of the error covariance, for a given packet arrival rate. We consider a different performance metric, Pr[Pk les M], i.e., the probability that Pk is bounded by a given M, and we derive lower and upper bounds on Pr[Pk les M]. We are also able to recover the results in the literature when using Pr[Pk les M] as a metric for scalar systems. Examples are provided to illustrate the theory developed in the paper.

Published in:

Control, Automation, Robotics and Vision, 2008. ICARCV 2008. 10th International Conference on

Date of Conference:

17-20 Dec. 2008