0347241 20240103193842.220101004235959.9 8 s. www cav_un_epca*0347240978-0-9824438-1-11-8EdinburghIET2010 marginalized particle filter unknown noise statistics bayesian conjugate prior cav_un_auth*0264144 Saha S. SE cav_un_auth*0264145 Okzan E. SE cav_un_auth*0264146 Gustafsson F. SE 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-marginalized particle filters for bayesian estimation of gaussian noise parameters.pdf CEZ:AV0Z10750506 The particle filter provides a general solution to the nonlinear filtering problem with arbitrarily accuracy. However, the curse of dimensionality prevents its application in cases where the state dimensionality is high. Further, estimation of stationary parameters is a known challenge in a particle filter framework. We suggest a marginalization approach for the case of unknown noise distribution parameters that avoid both aforementioned problem. First, the standard approach of augmenting the state vector with sensor offsets and scale factors is avoided, so the state dimension is not increased. Second, the mean and covariance of both process and measurement noises are represented with parametric distributions, whose statistics are updated adaptively and analytically using the concept of conjugate prior distributions. The resulting marginalized particle filter is applied to and illustrated with a standard example from literature. cav_un_auth*0264130 13th International Conference on Information Fusion Edinburgh 26.07.2010-29.07.2010 GB 2011 BC http://hdl.handle.net/11104/0188060 2010 cav_un_epca*0347240 Proceedings of the 13th International Conference on Information Fusion 978-0-9824438-1-1 1 8 Edinburgh IET 2010