Implicit sampling for particle filters
2009
Chorin, Alexandre J. | Tu, Xuemin
We present a particle-based nonlinear filtering scheme, related to recent work on chainless Monte Carlo, designed to focus particle paths sharply so that fewer particles are required. The main features of the scheme are a representation of each new probability density function by means of a set of functions of Gaussian variables (a distinct function for each particle and step) and a resampling based on normalization factors and Jacobians. The construction is demonstrated on a standard, ill-conditioned test problem.
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书目信息
Proceedings of the National Academy of Sciences of the United States of America
卷
106
期
41
页码
17249
- 17254
ISSN
0027-8424
出版者
National Academy of Sciences
其它主题
Probability distribution
语言
英语
许可
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类型
Journal Article; Text
2024-02-28
MODS