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。
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。
The optimum sampling number, the sequential sampling application and the sampling method at different densities were determined based on the characteristics of the larva's distribution.
根据竹螟幼虫分布的特点，确定了不同密度下的最适抽样数、序贯抽样的应用和调查取样方法。
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