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.
Supported in part by National Natural Science Foundation of China (60374006, 60234030 and 30370416), Distinguished Young Scholars Fund of China (60225015), and Ministry of Education of China (TRAPOYT Project).
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Wu, Y., Hu, D., Wu, M., Hu, X. (2006). Quasi-Gaussian Particle Filtering. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758501_92
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DOI: https://doi.org/10.1007/11758501_92
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