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State estimation with missing data and bounded uncertainty
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SYSNO ASEP 0357970 Document Type V - Research Report R&D Document Type The record was not marked in the RIV Title State estimation with missing data and bounded uncertainty Author(s) Pavelková, Lenka (UTIA-B) RID Issue data Praha: ÚTIA AV ČR, v.v.i, 2011 Series Research Report Series number 2296 Number of pages 15 s. Language eng - English Country CZ - Czech Republic Keywords state-space model ; filtering ; bounded noise ; incomplete data 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) Annotation The paper deals with two problems in the state estimation: (i) bounded uncertainty and (ii) missing measurement data. An algorithm for the state estimation of the discrete-time state space model whose uncertainties are bounded is proposed here. The algorithm also copes with situations when some data for identification are missing. The Bayesian approach is used and maximum a posteriori probability estimates are evaluated in the discrete time instants. The proposed estimation algorithm is applied to the estimation of vehicle position when incomplete data from global positioning system together with complete data from the inertial measurement unit are at disposal. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
Number of the records: 1