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Bobrovsky-Zakai Bound for Filtering, Prediction and Smoothing of Nonlinear Dynamic Systems
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
(Middle East Technical University, Turkey)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-3270-171X
2018 (English)In: 2018 21st International Conference on Information Fusion (FUSION), 2018, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, recursive Bobrovsky-Zakai bounds for filtering, prediction and smoothing of nonlinear dynamic systems are presented. The similarities and differences to an existing Bobrovsky-Zakai bound in the literature for the filtering case are highlighted. The tightness of the derived bounds are illustrated on a simple example where a linear system with non-Gaussian measurement likelihood is considered. The proposed bounds are also compared with the performance of some well known filters/predictors/smoothers and other Bayesian bounds.

Place, publisher, year, edition, pages
2018. p. 1-8
Keywords [en]
Bayesian lower bounds, Bobrovsky-Zakai bound, filtering, prediction, smoothing, nonlinear dynamic systems
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-151671OAI: oai:DiVA.org:liu-151671DiVA, id: diva2:1252033
Conference
2018 21st International Conference on Information Fusion (FUSION), Cambrdige, UK, 2018
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsAvailable from: 2018-09-29 Created: 2018-09-29 Last updated: 2018-10-22

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Fritsche, CarstenGustafsson, Fredrik

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Output format
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