Covariance-based least-squares filtering algorithm under Markovian measurement delays
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Least-squares estimationCovariance informationInnovation approachMarkovian delaysRecursive filtering algorithm
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.
SponsorshipThis research is supported by Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2014-52291-P).
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.