Robust locally optimal filters: Kalman and Bayesian estimation theory

DOI10.1016/0020-0255(96)00022-9zbMath0884.93052OpenAlexW1993149094MaRDI QIDQ1373380

Ludwik Kurz, Mehmet Ertuğrul Çelebi

Publication date: 17 December 1997

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0020-0255(96)00022-9

zbMATH Keywords

robustnessMonte Carlo simulationsKalman filteringpiecewise linear approximationMAP iterative proceduresnon-Gaussian observation noise

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10)

Cites Work

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• Robust filtering algorithms
• Adaptive maximum likelihood estimators of a location parameter
• An adaptive robustizing approach to Kalman filtering
• Recursive Bayesian estimation using Gaussian sums
• Dynamic Generalized Linear Models and Bayesian Forecasting
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations
• Approximate non-Gaussian filtering with linear state and observation relations
• Optimal linear recursive estimation with uncertain system parameters
• Robust bayesian estimation for the linear model and robustifying the Kalman filter
• Bivariate m-interval classifiers with application to edge detection
• Robust Estimation of a Location Parameter
• A General Qualitative Definition of Robustness
• On Estimation of a Probability Density Function and Mode
• Robust Statistics

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