# Adapting sample size in particle filters through KLD-resampling

arXiv: https://arxiv.org/abs/1306.3172 · HTML: https://mlsanity.com/paper/1306.3172/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2013.

## Built on by (most cited first, 2 of 2)

- [Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes](https://mlsanity.com/paper/2503.19302/) (2025)
- [Closed-loop Teaching via Demonstrations to Improve Policy Transparency](https://mlsanity.com/paper/2406.11850/) (2024)

## Abstract (arXiv)

This letter provides an adaptive resampling method. It determines the number of particles to resample so that the Kullback-Leibler distance (KLD) between distribution of particles before resampling and after resampling does not exceed a pre-specified error bound. The basis of the method is the same as Fox's KLD-sampling but implemented differently. The KLD-sampling assumes that samples are coming from the true posterior distribution and ignores any mismatch between the true and the proposal distribution. In contrast, we incorporate the KLD measure into the resampling in which the distribution of interest is just the posterior distribution. That is to say, for sample size adjustment, it is more theoretically rigorous and practically flexible to measure the fit of the distribution represented by weighted particles based on KLD during resampling than in sampling. Simulations of target tracking demonstrate the efficiency of our method.