Particle Filtering Under General Regime Switching

TL;DR

This paper introduces a flexible particle filtering method for general regime switching systems with unknown, time-varying models, outperforming traditional multiple-filter approaches in diverse real-world scenarios.

Contribution

A novel particle filtering algorithm that handles non-Markovian regime switching without multiple filters, accommodating long-term dependencies and broad application scope.

Findings

Outperforms state-of-the-art multiple model particle filters.

Handles non-Markovian, long-term dependencies in regime switching.

Validated on synthetic data demonstrating superior performance.

Abstract

In this paper, we consider a new framework for particle filtering under model uncertainty that operates beyond the scope of Markovian switching systems. Specifically, we develop a novel particle filtering algorithm that applies to general regime switching systems, where the model index is augmented as an unknown time-varying parameter in the system. The proposed approach does not require the use of multiple filters and can maintain a diverse set of particles for each considered model through appropriate choice of the particle filtering proposal distribution. The flexibility of the proposed approach allows for long-term dependencies between the models, which enables its use to a wider variety of real-world applications. We validate the method on a synthetic data experiment and show that it outperforms state-of-the-art multiple model particle filtering approaches that require the use of multiple filters.