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.
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