New design of estimators using covariance information with uncertain observations in linear discrete-time systems

DOI10.1016/S0096-3003(02)00060-7zbMath1016.62110DBLPjournals/amc/NakamoriCHL03aOpenAlexW2165733002WikidataQ59552633 ScholiaQ59552633MaRDI QIDQ1856001

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

Publication date: 28 January 2003

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

Full work available at URL: https://doi.org/10.1016/s0096-3003(02)00060-7

zbMATH Keywords

stochastic processesfilteringfactorizationlinear discrete-time systemsWiener-Hopf equationrecursive estimationcovariance information

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (12)

RLS Wiener estimators from observations with multiple and random delays in linear discrete-time stochastic systems ⋮ Quadratic estimation from uncertain observations with white plus coloured noises using covariance information ⋮ Design of RLS Wiener estimators from randomly delayed observations in linear discrete-time stochastic systems ⋮ Second-order polynomial estimators from uncertain observations using covariance information ⋮ Polynomial fixed-point smoothing of uncertainly observed signals based on covariances ⋮ New recursive estimators from correlated interrupted observations using covariance information ⋮ Recursive estimators of signals from measurements with stochastic delays using covariance information ⋮ Least-squares \(\nu\) th-order polynomial estimation of signals from observations affected by non-independent uncertainty ⋮ Recursive fixed-point smoothing algorithm from covariances based on uncertain observations with correlation in the uncertainty ⋮ Adaptive Kalman estimation in target tracking mixed with random one-step delays, stochastic-bias measurements, and missing measurements ⋮ Chandrasekhar-type filter for a wide-sense stationary signal from uncertain observations using covariance information ⋮ Fixed-interval smoothing problem from uncertain observations with correlated signal and noise

Cites Work

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• Estimation technique using covariance information with uncertain observations in linear discrete-time systems
• New design of linear least-squares fixed-point smoother using covariance information in continuous systems
• A view of three decades of linear filtering theory
• Optimal recursive estimation with uncertain observation

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