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Kalman Filtering Over A Packet Dropping Network: A Probabilistic Approach

Shi, Ling and Epstein, Michael and Murray, Richard M. (2008) Kalman Filtering Over A Packet Dropping Network: A Probabilistic Approach. In: 10th Annual Conference on Control, Automation, Robotics, and Vision, 2008. IEEE , Piscataway, NJ, pp. 41-46. ISBN 978-1-4244-2286-9.

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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[P_k], i.e., the expected value of the error covariance, for a given packet arrival rate. We consider a different performance metric, Pr[P_k ≤ M], i.e., the probability that P_k is bounded by a given M, and we derive lower and upper bounds on Pr[P_k ≤ M]. We are also able to recover the results in the literature when using Pr[P_k ≤ M] as a metric for scalar systems. Examples are provided to illustrate the theory developed in the paper.

Item Type:Book Section
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Murray, Richard M.0000-0002-5785-7481
Additional Information:© 2008 IEEE. Issue Date: 17-20 Dec. 2008; Date of Current Version: 27 February 2009.
Subject Keywords:Networked estimation; Kalman filtering; Packet-dropping network
Record Number:CaltechAUTHORS:20100726-104209965
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Official Citation:Ling Shi; Epstein, M.; Murray, R.M.; , "Kalman filtering over a packet dropping network: A probabilistic approach," Control, Automation, Robotics and Vision, 2008. ICARCV 2008. 10th International Conference on , vol., no., pp.41-46, 17-20 Dec. 2008 doi: 10.1109/ICARCV.2008.4795489 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:19183
Deposited On:27 Jul 2010 23:38
Last Modified:08 Nov 2021 23:50

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