@misc{10481/67488,
year = {2020},
month = {1},
url = {http://hdl.handle.net/10481/67488},
abstract = {This paper addresses the least-squares linear filtering problem of signals
from measurements which may be randomly delayed by one or two sampling
times. The delays are modelled by a homogeneous discrete-time
Markov chain to capture the dependence between them. Assuming that
the evolution equation generating the signal is not available and that only
the first- and second-order moments of the processes involved in the observation
model are known, a recursive filtering algorithm is derived using an
innovation approach. Recursive formulas for the filtering error variances are
also obtained to measure the precision of the proposed estimators.},
organization = {This research is supported by Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional
FEDER (grant no. MTM2014-52291-P).},
publisher = {Taylor&Francis},
keywords = {Least-squares estimation},
keywords = {Covariance information},
keywords = {Innovation approach},
keywords = {Markovian delays},
keywords = {Recursive filtering algorithm},
title = {Covariance-based least-squares filtering algorithm under Markovian measurement delays},
doi = {https://doi.org/10.1080/00207160.2017.1422496},
author = {García-Ligero, María Jesús and Hermoso-Carazo, Aurora and Linares-Pérez, Josefa},
}