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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.

MSC:
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
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References:
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