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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: IResearch Article The Rao-Blackwellized Particle Filter: A Filter Bank Implementation | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 724087 10 pages doi 10.1155 2010 724087 Research Article The Rao-Blackwellized Particle Filter A Filter Bank Implementation Gustaf Hendeby 1 Rickard Karlsson 2 and Fredrik Gustafsson EURASIP Member 3 1 Department of Augmented Vision German Research Center for Artificial Intelligence 67663 Kaiserslatern Germany 2 Competence Unit Informatics Division of Information Systems Swedish Defence Research Agency FOI 581 11 Linkoping Sweden 3 Department of Electrical Engineering Linkoping University 581 83 Linkoping Sweden Correspondence should be addressed to Gustaf Hendeby gustaf.hendeby@dfki.de Received 7 June 2010 Revised 6 September 2010 Accepted 25 November 2010 Academic Editor Ercan Kuruoglu Copyright 2010 Gustaf Hendeby et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. For computational efficiency it is important to utilize model structure in particle filtering. One of the most important cases occurs when there exists a linear Gaussian substructure which can be efficiently handled by Kalman filters. This is the standard formulation of the Rao-Blackwellized particle filter RBPF . This contribution suggests an alternative formulation of this well-known result that facilitates reuse of standard filtering components and which is also suitable for object-oriented programming. Our RBPF formulation can be seen as a Kalman filter bank with stochastic branching and pruning. 1. Introduction The particle filter PF 1 2 provides a fundamental solution to many recursive Bayesian filtering problems incorporating both nonlinear and non-Gaussian systems. This extends the classic optimal filtering theory developed for linear and Gaussian systems where the optimal solution is given by the Kalman filter KF 3 4 . .
