By Topic

Kalman Filtering Over a Packet-Dropping Network: A Probabilistic Perspective

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ling Shi ; Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Kowloon, China ; Epstein, M. ; Murray, R.M.

We consider the problem of state estimation of a discrete time process over a packet-dropping network. Previous 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 ?? M], i.e., the probability that Pk is bounded by a given M. We consider two scenarios in the paper. In the first scenario, when the sensor sends its measurement data to the remote estimator via a packet-dropping network, we derive lower and upper bounds on Pr[Pk ?? M]. In the second scenario, when the sensor preprocesses the measurement data and sends its local state estimate to the estimator, we show that the previously derived lower and upper bounds are equal to each other, hence we are able to provide a closed form expression for Pr[Pk ?? M]. We also recover the results in the literature when using Pr[Pk ?? M] as a metric for scalar systems. Examples are provided to illustrate the theory developed in the paper.

Published in:

Automatic Control, IEEE Transactions on  (Volume:55 ,  Issue: 3 )