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, the performance of particle filters relies on the knowledge of dynamic models and measurement models, and the construction of effective proposal distributions. An emerging trend in designing particle filters is the differentiable particle filters (DPFs). By constructing particle filters' components through neural networks and optimising them by gradient descent, differentiable particle filters are a promising computational tool to perform inference for sequence data in complex high-dimensional tasks such as vision-based robot localisation. In this paper, we provide a review of recent advances in differentiable particle filters and their applications. We place special emphasis on different design choices of key components of differentiable particle filters, including dynamic models, measurement models, proposal distributions, optimisation objectives, and differentiable resampling techniques.

本文综述了近年来不同的可微粒子滤波器及其应用，重点介绍了其中的关键组件设计，包括动态模型、测量模型、提议分布、优化目标和可微重采样技术。

可微粒子滤波器概述：数据自适应序贝叶斯推断