Online stochastic convergence analysis of the Kalman filter

DOI10.1155/2013/240295zbMath1417.93330OpenAlexW2089609978WikidataQ58993558 ScholiaQ58993558MaRDI QIDQ2444208

Yu Gu, Matthew B. Rhudy

Publication date: 9 April 2014

Published in: International Journal of Stochastic Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2013/240295

zbMATH Keywords

linear Kalman filter

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Signal detection and filtering (aspects of stochastic processes) (60G35) Stochastic stability in control theory (93E15)

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Cites Work

• Unnamed Item
• Unnamed Item
• Comments on ``Performance evaluation of UKF-based nonlinear filtering
• A practical note on evaluating Kalman filter performance optimality and degradation
• The Kantorovich inequality for error analysis of the Kalman filter with unknown noise distributions
• Uncertainties for recursive estimators in nonlinear state-space models, with applications to epidemiology
• Performance evaluation of UKF-based nonlinear filtering
• Stochastic processes and filtering theory
• Convergence properties of the Riccati difference equation in optimal filtering of nonstabilizable systems
• Estimating time-varying parameters by the Kalman filter based algorithm: stability and convergence
• Coping with singular transition matrices in estimation and control stability theory†
• Observers for nonlinear stochastic systems
• Stochastic stability of the discrete-time extended Kalman filter
• Observer-estimators for discrete-time systems
• Optimal Estimation of Dynamic Systems
• Use of the Kalman Filter for Inference in State-Space Models With Unknown Noise Distributions
• THE DISTRIBUTION OF NONSTATIONARY AUTOREGRESSIVE PROCESSES UNDER GENERAL NOISE CONDITIONS
• Almost Sure Boundedness of Randomly Sampled Systems