New recursive estimators from correlated interrupted observations using covariance information

DOI10.1080/00207720500219914zbMath1122.93078OpenAlexW2065016524WikidataQ59552558 ScholiaQ59552558MaRDI QIDQ5712076

Aurora Hermoso-Carazo, Seiichi Nakamori, Raquel Caballero-Águila, Josefa Linares-Pérez

Publication date: 22 December 2005

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

Full work available at URL: https://doi.org/10.1080/00207720500219914

zbMATH Keywords

least-squares estimatorcovariance informationuncertain observationsquadratic estimation

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10) Least squares and related methods for stochastic control systems (93E24) Signal detection and filtering (aspects of stochastic processes) (60G35) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (5)

Linear and quadratic estimation using uncertain observations from multiple sensors with correlated uncertainty ⋮ Polynomial fixed-point smoothing of uncertainly observed signals based on covariances ⋮ Optimal linear filter design for systems with correlation in the measurement matrices and noises: recursive algorithm and applications ⋮ Derivation of linear estimation algorithms from measurements affected by multiplicative and additive noises ⋮ Recursive fixed-point smoothing algorithm from covariances based on uncertain observations with correlation in the uncertainty

Cites Work

• 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
• Optimal linear estimation with uncertain observations (Corresp.)
• Polynomial filtering of discrete-time stochastic linear systems with multiplicative state noise
• Optimal recursive estimation with uncertain observation

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