Covariance-based least-squares filtering algorithm under Markovian measurement delays García-Ligero, María Jesús Hermoso-Carazo, Aurora Linares-Pérez, Josefa Least-squares estimation Covariance information Innovation approach Markovian delays Recursive filtering algorithm 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. 2021-03-23T09:04:50Z 2021-03-23T09:04:50Z 2020-01 info:eu-repo/semantics/article J. García-Ligero, A. Hermoso-Carazo & J. Linares-Pérez (2020). Covariance-based least-squares filtering algorithm under Markovian measurement delays, International Journal of Computer Mathematics, 97 (1-2), 40-50. http://hdl.handle.net/10481/67488 https://doi.org/10.1080/00207160.2017.1422496 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/embargoedAccess Atribución-NoComercial-SinDerivadas 3.0 España Taylor&Francis