Adapting sample size in particle filters through KLD-resampling
This work addresses adaptive sample size adjustment in particle filters for applications like target tracking, but it is incremental as it builds on Fox's KLD-sampling with a different implementation.
The authors tackled the problem of adaptive resampling in particle filters by introducing a method that adjusts the number of particles based on the Kullback-Leibler distance (KLD) to maintain a pre-specified error bound, demonstrating its efficiency in target tracking simulations.
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.