the new method uses the sequential importance sampling (sis) procedures to fuse the multi-dimensions measurements.
该方法采用序贯重要性采样的思想,对各种测量数据进行融合滤波。
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。
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 of non_gaussian nonlinear systems.
针对非线性、非高斯系统状态的在线估计问题,提出一种新的基于序贯重要性抽样的粒子滤波算法。
particle filtering is based on the concept of sequential importance sampling and the bayesian theory, it is particularly useful in dealing with nonlinear and non-gaussian problems.
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 。