Differentiable Bootstrap Particle Filters for Regime-Switching Models
This addresses state estimation challenges in applications like target tracking with varying conditions, but it appears incremental as it extends differentiable particle filters to regime-switching scenarios.
The paper tackles the problem of state estimation in regime-switching models where dynamics and measurements switch between candidate models, proposing a differentiable particle filter that learns unknown candidate models and tracks state posteriors, showing great performance compared to competitive algorithms.
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.