Implicit sampling for particle filters
Implicit sampling for particle filters
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DOI：
10.1073/pnas.0909196106
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发表时间：
2009-10-13
影响因子：
11.1
通讯作者：
Tu, Xuemin
中科院分区：
文献类型：
--
作者：
Chorin, Alexandre J.;Tu, Xuemin
我们提出了一种基于粒子的非线性滤波方案，与最近关于无链蒙特卡罗的工作相关，旨在使粒子路径精确聚焦，从而减少对粒子的需求。该方案的主要特点是利用一组高斯变量的函数(每个粒子和步长对应一个不同的函数)来表示每个新的概率密度函数，并基于归一化因子和雅可比进行重采样。这种构造是在一个标准的病态测试问题上演示的。
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.