0363163 20240103195438.620110913235959.9 Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering 4 s. www cav_un_epca*0363842978-1-4577-0539-7Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 20115924-5927PiscatawayIEEE2011 Particle filtering Dirichlet process Bayesian Estimation cav_un_auth*0264145 Okzan E. SE cav_un_auth*0264144 Saha S. 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/2011/AS/smidl-non-parametric bayesian measurement noise density estimation in non-linear filtering.pdf CEZ:AV0Z10750506 In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Dirichlet processes are widely used in statistics for nonparametric density estimation. In the proposed method, the unknown noise is modeled as a Gaussian mixture with unknown number of components. The joint estimation of the state and the noise density is done via particle filters. Furthermore, the number of components and the noise statistics are allowed to vary in time. An extension of the method for the estimation of time varying noise characteristics is also introduced. cav_un_auth*0273976 IEEE International Conference on Acoustics, Speech and Signal Processing Praha 22.05.2011-27.05.2011 CZ 2012 BD http://hdl.handle.net/11104/0199219 2011 cav_un_epca*0363842 Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2011 978-1-4577-0539-7 5924 5927 Piscataway IEEE 2011