sequential importance sampling

基本解釋

In this paper, a new particle filter based on sequential importance sampling (SIS) is proposed for the on-line estimation problem of non-Gauss nonlinear systems.
針對非線性、非高斯系統(tǒng)狀態(tài)的在線估計問題，本文提出一種新的基於序貫重要性抽樣的粒子濾波算法。
The sequential importance re-sampling particle filter can abate the influence of particle degeneracy but will easily lead to another problem-sample impoverishment.
再採樣粒子濾波雖可緩解粒子退化，但易導致樣本貧化；擴展粒子濾波也可在一定程度上解決退化問題，但難以跟蹤突變狀態(tài)。
A single Gaussian distribution is obtained to approximate the posterior distribution of state parameters based on sequential importance sampling and Monte Carlo methods.
通過基於重要性採樣和矇特卡羅模擬方法得到一高斯分佈來近似未知狀態(tài)變量的後騐分佈。