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Nonlinear bayesian state filtering with missing measurements and bounded noise and its application to vehicle position estimation
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SYSNO ASEP 0360239 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Nonlinear bayesian state filtering with missing measurements and bounded noise and its application to vehicle position estimation Author(s) Pavelková, Lenka (UTIA-B) RID Source Title Kybernetika. - : Ústav teorie informace a automatizace AV ČR, v. v. i. - ISSN 0023-5954
Roč. 47, č. 3 (2011), s. 370-384Number of pages 15 s. Language eng - English Country CZ - Czech Republic Keywords non-linear state space model ; bounded uncertainty ; missing measurements ; state filtering ; vehicle position estimation Subject RIV BC - Control Systems Theory R&D Projects 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10750506 - UTIA-B (2005-2011) UT WOS 000293207900005 EID SCOPUS 83455262553 Annotation The paper deals with parameter and state estimation and focuses on two problems that frequently occur in many practical applications: (i) bounded uncertainty and (ii) missing measurement data. An algorithm for the state estimation of the discrete-time non-linear state space model whose uncertainties are bounded is proposed. The algorithm also copes with situations when some measurements are missing. It uses Bayesian approach and evaluates maximum a posteriori probability (MAP) estimates of states and parameters. As the model uncertainties are supposed to have a bounded support, the searched estimates lie within an area that is described by the system of inequalities. In consequence, the problem of MAP estimation becomes the problem of nonlinear mathematical programming (NLP). The estimation with missing data reduces to the omission of corresponding inequalities in NLP formulation. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
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