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2022-10-07T03:42:35+00:00
Estimation with Information Loss: Asymptotic Analysis and Error Bounds
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In this paper, we consider a discrete time state
estimation problem over a packet-based 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 รข 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.
Ling Shi, Michael Epstein, Abhishek Tiwari and Richard M. Murray
2005b
2005 Conference on Decision and Control (CDC)
setm05-cdc
Conference Paper
2016-05-15T06:18:01Z
2457523.7625116
Estimation with Information Loss: Asymptotic Analysis and Error Bounds
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