A divide and conquer strategy for multinomial particle filter resampling
This work addresses a specific bottleneck in particle filter resampling for ensemble mixture models, offering an incremental improvement.
The paper tackles the problem of multinomial resampling in particle filters when the number of samples is less than or equal to the distribution size, common in ensemble mixture models like Gaussian mixture filters. It demonstrates superiority over existing methods through computational complexity analysis and numerical experiments.
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.