0347219 20240103193840.820101004235959.9 7 s. www cav_un_epca*0347240978-0-9824438-1-11-7EdinburghIET2010 marginalized particle filter software analysis Bayesian filtering cav_un_auth*0101207 Šmídl Václav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2010/AS/smidl-software analysis unifying particle filtering and marginalized particle filtering.pdf 1M0572 GA MŠk cav_un_auth*0001814 GP102/08/P250 GA ČR cav_un_auth*0241640 CEZ:AV0Z10750506 Abstract Particle filtering has evolved into wide range of techniques giving rise to many implementations and specialized algorithms. In theory, all these techniques are closely related, however this fact is usually ignored in software implementations. In this paper, particle filtering is studied together with marginalized particle filtering and a generic software scheme unifying these two areas is proposed. It is presented in general terms of object-oriented programming so that it may be implemented in existing Bayesian filtering toolboxes that are briefly reviewed. The power of the approach is illustrated on a new variant of the marginalized particle filter. A range of new variants of the filter is obtained by plugging this class into the proposed software structure. The framework and the illustrative example is implemented in the BDM library. cav_un_auth*0264130 13th International Conference on Information Fusion Edinburgh 26.07.2010-29.07.2010 GB 2011 BC http://hdl.handle.net/11104/0188045 2010 cav_un_epca*0347240 Proceedings of the 13th International Conference on Information Fusion 978-0-9824438-1-1 1 7 Edinburgh IET 2010