Chorin Alexandre J, Tu Xuemin
Department of Mathematics, University of California and Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Proc Natl Acad Sci U S A. 2009 Oct 13;106(41):17249-54. doi: 10.1073/pnas.0909196106. Epub 2009 Sep 24.
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.
我们提出了一种基于粒子的非线性滤波方案，该方案与最近关于无链蒙特卡罗的工作相关，旨在使粒子路径急剧聚焦，从而所需粒子数量更少。该方案的主要特点是通过一组高斯变量的函数（每个粒子和步骤对应一个不同的函数）来表示每个新的概率密度函数，以及基于归一化因子和雅可比行列式进行重采样。在一个标准的病态测试问题上展示了该构造。
