0393047 20240103202632.720130625235959.9 000319540500005 84877574625 10.1016/j.automatica.2013.02.046 10 s. P cav_un_epca*02562180005-1098496 (2013)1566-1575Elsevier Unknown Noise Statistics Adaptive Filtering Marginalized Particle Filter Bayesian Conjugate prior cav_un_auth*0291804 Ökzan E. 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. cav_un_auth*0291805 Saha S. SE cav_un_auth*0291806 Lundquist C. SE cav_un_auth*0273975 Gustafsson F. SE http://library.utia.cas.cz/separaty/2013/AS/smidl-0393047.pdf GAP102/11/0437 GA ČR CZ cav_un_auth*0273082 Knowledge of noise distribution is typically crucial for good estimation of a non-linear state-space model. However, properties of the noise process are often unknown in the majority of practical applications. Moreover, distribution of the noise may be non-stationary or state dependent, which prevents the use of off-line tuning methods. General estimation methods, such as particle filtering can be used to estimate the noise parameters, however at the price of heavy computational load. In this paper, we present an approach based on marginalized particle filtering where the noise parameters have analytical distribution. Explicit modeling of parameter non-stationarity is avoided and it is replaced by maximum-entropy estimation based on the assumption of slowly varying parameters. Properties of the resulting algorithm are illustrated on both a standard example and a navigation application based on odometry. The latter involves formulas for dead reckoning rotational speeds of two wheels with unknown radii. 2014 BC 5 4 A 4a 20231122135647.0 http://hdl.handle.net/11104/0221976 AUTOMATIONCONTROLSYSTEMS|ENGINEERINGELECTRICALELECTRONIC 4.423 0.392 7.II 0.05155 1.707 18940 446 3.342 ScienceCitationIndex Q1 94.953 89.350 Q1* Q1* 2013 Soubory v repozitáři: smidl-0393047.pdf 84877574625 SCOPUS 000319540500005 WOS cav_un_epca*0256218 Automatica 0005-1098 1873-2836 Roč. 49 č. 6 2013 1566 1575 Elsevier