Soft resampling for improved information retention in particle filtering

The reweighing process of particle filtering can result in very large variance of particle weights; a problem known as degeneracy. The usual solution to this is an intermediary resampling step in which particles with lower weights are replaced by copies of those with large weights. This resampling inevitably results in loss of the information contained in those particles of low weights. Most of the existing stochastic and deterministic resampling schemes cause further loss of information because all the resampled particles have equal weights. These current techniques are what we would call, “hard resamplers,” and they impede the accumulation of information over many successive observations, which affects the detection of very covert targets. This paper presents two variants of a soft and deterministic resampling procedure that retains information contained in the weights over multiple observations. We demonstrate their effectiveness using a track-before-detect application.