Abstract
The recently-raised Gaussian particle filtering (GPF) introduced the idea of Bayesian sampling into Gaussian filters. This note proposes to generalize the GPF by further relaxing the Gaussian restriction on the prior probability. Allowing the non-Gaussianity of the prior probability, the generalized GPF is provably superior to the original one. Numerical results show that better performance is obtained with considerably reduced computational burden. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Wu, Y., Hu, D., Wu, M., & Hu, X. (2006). Quasi-Gaussian particle filtering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3991 LNCS-I, pp. 689–696). Springer Verlag. https://doi.org/10.1007/11758501_92
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