Signal Estimation from Randomly Delayed Observations using Covariance Information

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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