This work provides a new multinomial resampling procedure for particle filter resampling, focused on the case where the number of samples required is less than or equal to the size of the underlying discrete distribution. This setting is common in ensemble mixture model filters such as the Gaussian mixture filter. We show superiority of our approach with respect two of the best known multinomial sampling procedures both through a computational complexity analysis and through a numerical experiment.

翻译：本文提出了一种适用于粒子滤波重采样的新型多项式重采样方法，重点针对所需样本数量小于或等于底层离散分布规模的情况。该场景在混合集成模型滤波器（如高斯混合滤波器）中较为常见。通过计算复杂度分析与数值实验，我们证明了本方法相较于两种最著名的多项式采样过程具有优越性。