The one step fixed-lag particle smoother as a strategy to improve the prediction step of particle filtering

TL;DR

This paper introduces a simple one-step fixed-lag smoother to enhance the prediction phase of particle filters, aiming to reduce particle impoverishment and improve filter accuracy in stochastic dynamical systems.

Contribution

It proposes a novel approximation of the fixed-lag smoother that performs additional simulations during prediction to improve particle likelihoods in bootstrap particle filters.

Findings

Improved particle diversity in filtering process

Enhanced prediction accuracy in state-space models

Reduced particle degeneracy over time

Abstract

Sequential Monte Carlo methods have been a major breakthrough in the field of numerical signal processing for stochastic dynamical state-space systems with partial and noisy observations. However, these methods still present certain weaknesses. One of the most fundamental is the degeneracy of the filter due to the impoverishment of the particles: the prediction step allows the particles to explore the state-space and can lead to the impoverishment of the particles if this exploration is poorly conducted or when it conflicts with the following observation that will be used in the evaluation of the likelihood of each particle. In this article, in order to improve this last step within the framework of the classic bootstrap particle filter, we propose a simple approximation of the one step fixed-lag smoother. At each time iteration, we propose to perform additional simulations during the prediction step in order to improve the likelihood of the selected particles.