In particle filters (PF), sequential importance sampling will result in sample impoverishment and further the loss of diversity after resampling.
粒子滤波算法(PF)中，序列重要性采样引起采样点贫化，进一步经过重采样后造成分集度损失。
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.
针对非线性、非高斯系统状态的在线估计问题，本文提出一种新的基于序贯重要性抽样的粒子滤波算法。
A single Gaussian distribution is obtained to approximate the posterior distribution of state parameters based on sequential importance sampling and Monte Carlo methods.
通过基于重要性采样和蒙特卡罗模拟方法得到一高斯分布来近似未知状态变量的后验分布。
The sequential importance re-sampling particle filter can abate the influence of particle degeneracy but will easily lead to another problem-sample impoverishment.
再采样粒子滤波虽可缓解粒子退化，但易导致样本贫化；扩展粒子滤波也可在一定程度上解决退化问题，但难以跟踪突变状态。
The sequential importance re-sampling particle filter can abate the influence of particle degeneracy but will easily lead to another problem-sample impoverishment.
再采样粒子滤波虽可缓解粒子退化，但易导致样本贫化；扩展粒子滤波也可在一定程度上解决退化问题，但难以跟踪突变状态。
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