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Recursive estimators of signals from measurements with stochastic delays using covariance information. (English) Zbl 1062.94520
Summary: Least-squares linear one-stage prediction, filtering and fixed-point smoothing algorithms for signal estimation using measurements with stochastic delays 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 the additive noise in the observations as well as the delay probabilities.

94A12Signal theory (characterization, reconstruction, filtering, etc.)
93E03General theory of stochastic systems
93E10Estimation and detection in stochastic control
93E24Least squares and related methods in stochastic control
Full Text: DOI
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