Implicit Particle Filtering via a Bank of Nonlinear Kalman Filters

TL;DR

This paper introduces a novel implicit particle filtering method that uses a bank of nonlinear Kalman filters to improve computational efficiency and mitigate particle degeneracy in state estimation tasks.

Contribution

It proposes a new realization of the implicit particle filter leveraging nonlinear Kalman filters, enhancing computational tractability and efficiency.

Findings

The new method reduces computational complexity.

It effectively mitigates particle degeneracy.

The approach shows improved performance in state estimation.

Abstract

The implicit particle filter seeks to mitigate particle degeneracy by identifying particles in the target distribution's high-probability regions. This study is motivated by the need to enhance computational tractability in implementing this approach. We investigate the connection of the particle update step in the implicit particle filter with that of the Kalman filter and then formulate a novel realization of the implicit particle filter based on a bank of nonlinear Kalman filters. This realization is more amenable and efficient computationally.