By approximating posterior distributions with weighted samples, particle filters (PFs) provide an efficient mechanism for solving non-linear sequential state estimation problems. While the effectiveness of particle filters has been recognised in various applications, their performance relies on the knowledge of dynamic models and measurement models, as well as the construction of effective proposal distributions. An emerging trend involves constructing components of particle filters using neural networks and optimising them by gradient descent, and such data-adaptive particle filtering approaches are often called differentiable particle filters. Due to the expressiveness of neural networks, differentiable particle filters are a promising computational tool for performing inference on sequential data in complex, high-dimensional tasks, such as vision-based robot localisation. In this paper, we review recent advances in differentiable particle filters and their applications. We place special emphasis on different design choices for key components of differentiable particle filters, including dynamic models, measurement models, proposal distributions, optimisation objectives, and differentiable resampling techniques.

翻译：通过加权样本近似后验分布，粒子滤波器为解决非线性序贯状态估计问题提供了高效机制。尽管粒子滤波器的有效性已在多个应用领域得到验证，但其性能依赖于动态模型与测量模型的先验知识，以及有效提议分布的构造。一项新兴趋势是使用神经网络构建粒子滤波器的各组件，并通过梯度下降进行优化——这类数据自适应粒子滤波方法通常被称为可微分粒子滤波器。凭借神经网络的强大表达能力，可微分粒子滤波器成为复杂高维序贯数据推断任务（如基于视觉的机器人定位）中极具前景的计算工具。本文综述了可微分粒子滤波器的近期进展及其应用，重点关注其关键组件的不同设计选择，包括动态模型、测量模型、提议分布、优化目标及可微分重采样技术。