S. Nakamori (Japan), R. Caballero, A. Hermoso, and
J. Linares (Spain)
Estimation. Statistical modelling. Stochastic Systems.
Randomly delayed observations. Covariance information.
Least-squares linear prediction and filtering algorithms to
estimate a signal using randomly delayed measurements
contaminated by additive white noise are derived. The
delay is considered to be random and modelled by a
binary white noise with values zero or one; these values
indicate that the measurements arrive in time or they
are delayed by one sampling time. Recursive estimation
algorithms are obtained without requiring the state-space
model generating the signal, but just using covariance
information about the signal and additive noise in the
observations and the delay probabilities.