Authors:
S. Nakamori
1
;
A. Hermoso-Carazo
2
;
J. Linares-Pérez
2
and
M. I. Sánchez-Rodríguez
2
Affiliations:
1
Faculty of Education, Japan
;
2
Universidad de Granada, Japan
Keyword(s):
Signal estimation, randomly delayed observations, coloured noise, covariance information.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Signal Reconstruction
;
Time Series and System Modeling
Abstract:
A recursive algorithm for the least-squares linear one-stage prediction and filtering problems of discrete-time signals using randomly delayed measurements perturbed by additive white plus coloured noises are presented. It is assumed that the autocovariance function of the signal and the coloured noise are expressed in a semidegenerate kernel form and the delay is modelled by a sequence of independent Bernoulli random variables, which indicate if the measurements arrive in time or are delayed by one sampling time. The estimators are obtained by an innovation approach and do not use the state-space model of the signal, but only the covariance information about the signal and the observation noises and the delay probabilities.