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Published December 2008 | Published
Book Section - Chapter Open

Kalman Filtering Over A Packet Dropping Network: A Probabilistic Approach

Abstract

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.

Additional Information

© 2008 IEEE. Issue Date: 17-20 Dec. 2008; Date of Current Version: 27 February 2009.

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Published - Shi2008p80952008_10Th_International_Conference_On_Control_Automation_Robotics___Vision_Icarv_2008_Vols_1-4.pdf

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Shi2008p80952008_10Th_International_Conference_On_Control_Automation_Robotics___Vision_Icarv_2008_Vols_1-4.pdf

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Created:
August 22, 2023
Modified:
October 20, 2023