Polynomial fixed-point smoothing of uncertainly observed signals based on covariances

DOI10.1080/00207720701777777zbMath1283.93326OpenAlexW2059079232WikidataQ59552447 ScholiaQ59552447MaRDI QIDQ5402744

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

Publication date: 17 March 2014

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207720701777777

zbMATH Keywords

polynomial estimationleast-squares estimationcovariance informationuncertain observations

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Least squares and related methods for stochastic control systems (93E24) Information theory (general) (94A15)

Related Items (4)

Recursive smoothing algorithms for the estimation of signals from uncertain observations via mixture approximations ⋮ Derivation of linear estimation algorithms from measurements affected by multiplicative and additive noises ⋮ Nonlinear estimation applying an unscented transformation in systems with correlated uncertain observations ⋮ Least-squares polynomial estimation from observations featuring correlated random delays

Cites Work

• Recursive estimator for linear and nonlinear systems with uncertain observations
• Second-order polynomial estimators from uncertain observations using covariance information
• Polynomial filtering approach to reconstruction and noise reduction of nonuniformly sampled signals.
• New design of estimators using covariance information with uncertain observations in linear discrete-time systems
• Restoration of images corrupted by additive non-Gaussian noise
• Fixed-point smoothing with non-independent uncertainty using covariance information
• Polynomial Filtering for Linear Discrete Time Non-Gaussian Systems
• Polynomial estimation of the amplitude of a signal
• Optimal quadratic filtering of linear discrete-time non-Gaussian systems
• New recursive estimators from correlated interrupted observations using covariance information

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