Covariance-based least-squares filtering algorithm under Markovian measurement delays
Autor: | Aurora Hermoso-Carazo, María J. García-Ligero, Josefa Linares-Pérez |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Applied Mathematics
Markov process Sampling (statistics) 010103 numerical & computational mathematics Covariance Recursive filtering algorithm 01 natural sciences Least squares Innovation approach Computer Science Applications 010101 applied mathematics symbols.namesake Computational Theory and Mathematics Covariance information Homogeneous symbols Markovian delays 0101 mathematics Algorithm Linear filter Least-squares estimation Mathematics |
Popis: | 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. This research is supported by Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2014-52291-P). |
Databáze: | OpenAIRE |
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