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Alternative em Algorithms for Nonlinear State-Space Models
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0001-6630-243X
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-2718-0262
2018 (English)In: 2018 21st International Conference on Information Fusion, FUSION 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 1260-1267Conference paper, Published paper (Refereed)
Abstract [en]

The expectation-maximization algorithm is a commonly employed tool for system identification. However, for a large set of state-space models, the maximization step cannot be solved analytically. In these situations, a natural remedy is to make use of the expectation-maximization gradient algorithm, i.e., to replace the maximization step by a single iteration of Newton's method. We propose alternative expectation-maximization algorithms that replace the maximization step with a single iteration of some other well-known optimization method. These algorithms parallel the expectation-maximization gradient algorithm while relaxing the assumption of a concave objective function. The benefit of the proposed expectation-maximization algorithms is demonstrated with examples based on standard observation models in tracking and localization. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 1260-1267
Keywords [en]
Expectation-maximization, Levenberg-Marquardt, system identification, the Gauss-Newton method, trust region, Identification (control systems), Image segmentation, Information fusion, Newton-Raphson method, Positron emission tomography, Religious buildings, Signal receivers, State space methods, Concave objective functions, Expectation - maximizations, Expectation-maximization algorithms, Gauss-Newton methods, Nonlinear state space models, State - space models, Maximum principle
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-236699DOI: 10.23919/ICIF.2018.8455234Scopus ID: 2-s2.0-85054096276ISBN: 9780996452762 (print)OAI: oai:DiVA.org:kth-236699DiVA, id: diva2:1262457
Conference
21st International Conference on Information Fusion, FUSION 2018, 10 July 2018 through 13 July 2018
Funder
Swedish Foundation for Strategic Research
Note

Conference code: 139346; Export Date: 22 October 2018; Conference Paper; Funding details: SSF, Stiftelsen för Strategisk Forskning; Funding details: SSF, Sjögren’s Syndrome Foundation; Funding text: This research is financially supported by the Swedish Foundation for Strategic Research (SSF) via the project ASSEMBLE. QC 20181112

Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2018-11-12Bibliographically approved

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Jaldén, JoakimHändel, Peter

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Citation style
  • apa
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  • Other locale
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Output format
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