  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.
