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.

翻译：微分粒子滤波是一类新兴的粒子滤波方法，它利用神经网络构建并学习参数化状态空间模型。在实际应用中，状态动态与观测均可能在多个候选模型之间切换。例如，在目标跟踪中，车辆可能处于怠速、车流缓行或高速公路巡航等状态，而观测数据则在不同地理或气象条件下采集。本文针对体制切换状态空间模型提出一种新型微分粒子滤波方法。该方法能够学习一组未知的候选动态模型和观测模型，并实现状态后验的跟踪。我们在相关模型中评估了新算法的性能，结果表明该算法相较于其他竞争算法具有显著优势。