Covariance-based least-squares filtering algorithm under Markovian measurement delays

DOI10.1080/00207160.2017.1422496zbMath1486.94027OpenAlexW2782321086WikidataQ59551759 ScholiaQ59551759MaRDI QIDQ5030564

Josefa Linares-Pérez, Aurora Hermoso-Carazo, María Jesús García-Ligero

Publication date: 17 February 2022

Published in: International Journal of Computer Mathematics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10481/67488

zbMATH Keywords

least-squares estimationcovariance informationinnovation approachMarkovian delaysrecursive filtering algorithm

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10) Signal detection and filtering (aspects of stochastic processes) (60G35) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (2)

Fusion filtering for rectangular descriptor systems with stochastic bias and random observation delays under weighted try-once-discard protocol ⋮ Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains

Cites Work

• Dynamic output feedback control for nonlinear networked control systems with random packet dropout and random delay
• Minimum variance generalized state estimators for multiple sensors with different delay rates
• \(H_\infty \) filtering of network-based systems with random delay
• Linear optimal filtering for discrete-time systems with random jump delays
• Least-squares linear filtering using observations coming from multiple sensors with one- or two-step random delay
• Covariance-based estimation from multisensor delayed measurements with random parameter matrices and correlated noises
• Linear unbiased state estimation under randomly varying bounded sensor delay
• Optimal filtering for networked systems with Markovian communication delays
• Linear unbiased state estimation with random one-step sensor delay

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