  In this paper, we consider the peak-covariance stability of Kalman filtering
subject to packet losses. The length of consecutive packet losses is governed
by a time-homogeneous finite-state Markov chain. We establish a sufficient
condition for peak-covariance stability and show that this stability check can
be recast as a linear matrix inequality (LMI) feasibility problem. Comparing
with the literature, the stability condition given in this paper is invariant
with respect to similarity state transformations; moreover, our condition is
proved to be less conservative than the existing results. Numerical examples
are provided to demonstrate the effectiveness of our result.
