  Differentiable particle filters are an emerging class of particle filtering
methods that use neural networks to construct and learn parametric state-space
models. In real-world applications, both the state dynamics and measurements
can switch between a set of candidate models. For instance, in target tracking,
vehicles can idle, move through traffic, or cruise on motorways, and
measurements are collected in different geographical or weather conditions.
This paper proposes a new differentiable particle filter for regime-switching
state-space models. The method can learn a set of unknown candidate dynamic and
measurement models and track the state posteriors. We evaluate the performance
of the novel algorithm in relevant models, showing its great performance
compared to other competitive algorithms.
