Copyright © 2010 R. Caballero-Águila et al. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The least-squares linear estimation problem using covariance
information is addressed in discrete-time linear stochastic
systems with bounded random observation delays which can lead to
bounded packet dropouts. A recursive algorithm, including the
computation of predictor, filter, and fixed-point smoother, is
obtained by an innovation approach. The random delays are
modeled by introducing some Bernoulli random variables with known
distributions in the system description. The derivation of the
proposed estimation algorithm does not require full knowledge of
the state-space model generating the signal to be estimated, but
only the delay probabilities and the covariance functions of the
processes involved in the observation equation.