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Estimation with Information Loss: Asymptotic Analysis and Error Bounds 
Abstract 
In this paper, we consider a discrete time … In this paper, we consider a discrete time state
estimation problem over a packetbased network. In each
discrete time step, the measurement is sent to a Kalman
filter with some probability that it is received or dropped.
Previous pioneering work on Kalman filtering with intermittent
observation losses shows that there exists a certain threshold of
the packet dropping rate below which the estimator is stable in
the expected sense. In their analysis, they assume that packets
are dropped independently between all time steps. However we
give a completely different point of view. On the one hand, it
is not required that the packets are dropped independently but
just that the information gain pi_g, defined to be the limit of the
ratio of the number of received packets n during N time steps
as N goes to infinity, exists. On the other hand, we show that
for any given pi_g, as long as pi_g > 0, the estimator is stable
almost surely, i.e. for any given epsilon > 0 the error covariance
matrix P{k is bounded by a finite matrix M, with probability
1 â epsilon. Given an error tolerance M, pi_g can in turn be found.
We also give explicit formula for the relationship between M
and epsilon. or the relationship between M
and epsilon. +


Authors  Ling Shi, Michael Epstein, Abhishek Tiwari and Richard M. Murray + 
ID  2005b + 
Source  2005 Conference on Decision and Control (CDC) + 
Tag  setm05cdc + 
Title  Estimation with Information Loss: Asymptotic Analysis and Error Bounds + 
Type  Conference Paper + 
Categories  Papers 
Modification date This property is a special property in this wiki.

15 May 2016 06:18:01 + 
URL This property is a special property in this wiki.

http://www.cds.caltech.edu/~murray/preprints/setm05cdc.pdf + 
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Estimation with Information Loss: Asymptotic Analysis and Error Bounds +  Title 
