Feedback Particle Filter With Stochastically Perturbed Innovation And Its Application to Dual Estimation

TL;DR

This paper introduces a stochastically perturbed feedback particle filter that addresses particle degeneracy, demonstrating its exactness and comparing its performance with other filters in dual estimation tasks.

Contribution

The paper proposes a novel stochastically perturbed feedback particle filter and a resampled Sinkhorn particle filter, enhancing dual estimation methods.

Findings

The stochastically perturbed filter is shown to be exact.

Performance comparisons demonstrate advantages over existing filters.

Resampled Sinkhorn particle filter improves estimation accuracy.

Abstract

Particle filters have, in recent years, been found to perform well in highly nonlinear problems as well as in estimation of parameters. However, there is still the problem of particle degeneracy in particle filters which has led to the invention of, among others, feedback particle filters. In this paper, we introduce a stochastically perturbed feedback particle filter and show that it is exact. The novelty is in the fact that the innovation process is stochastically perturbed. Resampled sinkhorn particle filter is also introduced. We then compare their performance with that of other filters in simultaneous state and parameter estimation.