In this letter, we consider Gaussian approximations of the optimal importance density in sequential importance sampling for nonlinear, non-Gaussian state-space models. The proposed method is based on generalized statistical linear regression and posterior linearization using conditional expectations. Simulation results show that the method outperforms the compared methods in terms of the effective sample size and provides a better local approximation of the optimal importance density.

中文翻译：

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使用迭代条件期望的粒子过滤的重要性密度

在这封信中，我们考虑了非线性非高斯状态空间模型的顺序重要性采样中最佳重要性密度的高斯近似。所提出的方法基于使用条件期望的广义统计线性回归和后验线性化。仿真结果表明，该方法在有效样本大小方面优于比较方法，并提供了最佳重要性密度的更好局部近似。