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The accuracy of moving horizon estimation (MHE) suffers significantly in the presence of measurement outliers.
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Computation of arrival cost for moving horizon estimation via unscented Kalman filtering
Cheryl C Qu and Juergen Hahn · 2009
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Rohit Kumar, David Castanón, Erhan Ermis, and Venkatesh Saligrama · 2010
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H-infinity filtering for a class of nonlinear discrete-time systems based on unscented transform
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Peter J Huber · 2011
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A particle filtering scheme for processing time series corrupted by outliers
Cristina S Maiz, Elisa M Molanes-Lopez, Joaquín Miguez, and Petar M Djuric · 2012
Doubly robust Bayesian inference for non-stationary streaming data with β \beta -divergences
Jeremias Knoblauch, Jack E Jewson, and Theodoros Damoulas · 2018
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A robust generalized-maximum likelihood unscented Kalman filter for power system dynamic state estimation
Junbo Zhao and Lamine Mili · 2018
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Douglas A Allan and James B Rawlings · 2019
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Ayman Boustati, Omer Deniz Akyildiz, Theodoros Damoulas, and Adam Johansen · 2020
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Bayesian Filtering and Smoothing
Simo Särkkä · 2013
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Moving-horizon estimation for discrete-time linear systems with measurements subject to outliers
Angelo Alessandri and Moath Awawdeh · 2014
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Moving-horizon estimation with guaranteed robustness for discrete-time linear systems and measurements subject to outliers
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Proximity moving horizon estimation for discrete-time nonlinear systems
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Robust stability of full information estimation
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Particle filters: A hands-on tutorial
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Anytime proximity moving horizon estimation: Stability and regret for nonlinear systems
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Generic stability implication from full information estimation to moving-horizon estimation
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A robust variational autoencoder using beta divergence
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Generalized moving horizon estimation for nonlinear systems with robustness to measurement outliers
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