  Measurements of a scalar linear Gauss-Markov process are sent over a fading
channel. The fading channel is modeled as independent and identically
distributed random variables with known realization at the receiver. The
optimal estimator at the receiver is the Kalman filter. In contrast to the
classical Kalman filter theory, given a random channel, the Kalman gain and the
error covariance become random. Then the probability distribution function of
expected estimation error and its outage probability can be chosen for
estimation quality assessment. In this paper and in order to get the estimation
error outage, we provide means to characterize the stationary probability
density function of the random expected estimation error. Furthermore and for
the particular case of the i.i.d. Rayleigh fading channels, upper and lower
bounds for the outage probability are derived which provide insight and simpler
means for design purposes. We also show that the bounds are tight for the high
SNR regime, and that the outage probability decreases linearly with the inverse
of the average channel SNR.
