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AUTOMATICA
2008

Probabilistic performance of state estimation across a lossy network

9 years 2 months ago
Probabilistic performance of state estimation across a lossy network
We consider a discrete time state estimation problem over a packet-based network. In each discrete time step, a measurement packet is sent across a lossy network to an estimator unit consisting of a modified Kalman filter. Using the designed estimator algorithm, the importance of placing a measurement buffer at the sensor that allows transmission of the current and several previous measurements is shown. Previous pioneering work on Kalman filtering with intermittent observation losses is concerned with the asymptotic behavior of the expected value of the error covariance, i.e. E[Pk] < as k . We consider a different performance metric, namely a probabilistic statement of the error covariance Pr[Pk M] 1 - , meaning that with high probability the error covariance is bounded above at any instant in time. Provided the estimator error covariance has an upper bound whenever a measurement packet arrives, we show that for any finite M this statement will hold so long as the probability ...
Michael Epstein, Ling Shi, Abhishek Tiwari, Richar
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2008
Where AUTOMATICA
Authors Michael Epstein, Ling Shi, Abhishek Tiwari, Richard M. Murray
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