Quasi-Gaussian Particle Filtering

机译：拟高斯粒子滤波

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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.

机译：最近提出的高斯粒子滤波（GPF）将贝叶斯采样的思想引入了高斯滤波器。本注释建议通过进一步放宽对先验概率的高斯限制来概括GPF。考虑到先验概率的非高斯性，广义GPF可证明优于原始概率。数值结果表明，在降低计算负担的情况下可以获得更好的性能。

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来源
《International Conference on Computational Science(ICCS 2006) pt.1; 20060528-31; Reading(GB)》|2006年|P.689-696|共8页
会议地点 Reading(GB)
作者
Yuanxin Wu; Dewen Hu; Meiping Wu; Xiaoping Hu;
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作者单位

Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, P.R. China, 410073;

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原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
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Quasi-Gaussian Particle Filtering

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