Recursive fixed-point smoothing algorithm from covariances based on uncertain observations with correlation in the uncertainty

DOI10.1016/j.amc.2008.04.030zbMath1157.65314OpenAlexW2006582431WikidataQ59552454 ScholiaQ59552454MaRDI QIDQ2518716

Raquel Caballero-Águila, Seiichi Nakamori, Josefa Linares-Pérez, Aurora Hermoso-Carazo, José Domingo Jiménez-López

Publication date: 16 January 2009

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2008.04.030

zbMATH Keywords

numerical examplesinnovation processleast-squares estimationcovariance informationuncertain observationsdiscrete stochastic systemsfixed-point smoothing algorithmdiscrete-time signallinear recursive filtering

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric estimation (62G05)

Related Items (3)

Estimation for discrete-time systems with multiple packet dropouts using covariance information ⋮ Quadratic filtering algorithm based on covariances using correlated uncertain observations coming from different sensors ⋮ Recursive smoothing algorithms for the estimation of signals from uncertain observations via mixture approximations

Cites Work

• Recursive estimators of signals from measurements with stochastic delays using covariance information
• Design of recursive Wiener fixed-point smoothers based on innovations approach in linear discrete-time stochastic systems
• New design of estimators using covariance information with uncertain observations in linear discrete-time systems
• Optimal filtering with random sensor delay, multiple packet dropout and uncertain observations
• Kalman Filtering With Intermittent Observations
• New recursive estimators from correlated interrupted observations using covariance information

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