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- W2325097067 abstract "A novel Gaussian particle filter based on QuasiMonte Carlo integration is proposed for nonlinear filtering problems with Gaussian distributions. The conventional Gaussian particle filter, with less complexity than particle filter, also has the same problem that approximation accuracy deteriorating caused by the randomness of particles from Monte-Carlo sampling. The proposed algorithm, called QMCGPF, circumvents this difficulty by placing the particles deterministically according to a Quasi-Monte Carlo integration rule. With weighted low-discrepancy particles in place of weighted random samples, the Quasi-Monte Carlo methods can obtain better approximation performance in nonlinear estimation. Experimental results with a simulated 2D tracking problem demonstrates the effectiveness of the proposed new QMC-based filtering algorithm." @default.
- W2325097067 created "2016-06-24" @default.
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- W2325097067 date "2008-04-01" @default.
- W2325097067 modified "2023-10-06" @default.
- W2325097067 title "A Quasi-Monte Carlo Based Gaussian Particle Filter" @default.
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- W2325097067 doi "https://doi.org/10.1142/9789812799524_0073" @default.
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