Generalized division-free architecture and compact memory structure for resampling in particle filters

The most challenging step of implementing particle filtering is the resampling step which replicates particles with large weights and discards those with small weights. In this paper, we propose a generic architecture for resampling which uses double multipliers to avoid normalization divisions and make the architecture equally efficient for non-powers-of-two number of particles. Furthermore, the complexity of resampling is greatly affected by the size of memories used to store weights. We illustrate that by storing the original weights instead of their cumulative sum and calculating them online reduces the total complexity, in terms of area, ranging from 21% to 45%, while giving up to 50% reduction in memory usage.