A Unification of Weighted and Unweighted Particle Filters

TL;DR

This paper introduces a unified framework connecting weighted and unweighted particle filters in continuous-time filtering, revealing their relationship and potential for improved algorithms addressing key issues like weight degeneracy and gain estimation.

Contribution

The authors propose a unifying framework that links weighted and unweighted particle filters, showing how they are special cases within a broader class and enabling new algorithmic developments.

Findings

Bootstrap particle filter (BPF) and feedback particle filter (FPF) are special cases of the unified framework.

The framework allows a smooth transition between weighted and unweighted particle filters.

Potential to develop new algorithms that mitigate weight degeneracy and improve gain estimation.

Abstract

Particle filters (PFs), which are successful methods for approximating the solution of the filtering problem, can be divided into two types: weighted and unweighted PFs. It is well known that weighted PFs suffer from the weight degeneracy and curse of dimensionality. To sidestep these issues, unweighted PFs have been gaining attention, though they have their own challenges. The existing literature on these types of PFs is based on distinct approaches. In order to establish a connection, we put forward a framework that unifies weighted and unweighted PFs in the continuous-time filtering problem. We show that the stochastic dynamics of a particle system described by a pair process, representing particles and their importance weights, should satisfy two necessary conditions in order for its distribution to match the solution of the Kushner--Stratonovich equation. In particular, we demonstrate that the bootstrap particle filter (BPF), which relies on importance sampling, and the feedback particle filter (FPF), which is an unweighted PF based on optimal control, arise as special cases from a broad class and that there is a smooth transition between the two. The freedom in designing the PF dynamics opens up potential ways to address the existing issues in the aforementioned algorithms, namely weight degeneracy in the BPF and gain estimation in the FPF.