Differentiable particle filters are an emerging class of sequential Bayesian inference techniques that use neural networks to construct components in state space models. Existing approaches are mostly based on offline supervised training strategies. This leads to the delay of the model deployment and the obtained filters are susceptible to distribution shift of test-time data. In this paper, we propose an online learning framework for differentiable particle filters so that model parameters can be updated as data arrive. The technical constraint is that there is no known ground truth state information in the online inference setting. We address this by adopting an unsupervised loss to construct the online model updating procedure, which involves a sequence of filtering operations for online maximum likelihood-based parameter estimation. We empirically evaluate the effectiveness of the proposed method, and compare it with supervised learning methods in simulation settings including a multivariate linear Gaussian state-space model and a simulated object tracking experiment.

翻译：可微粒子滤波是一种新兴的序列贝叶斯推断技术，其利用神经网络构建状态空间模型中的组件。现有方法主要基于离线监督训练策略，这导致模型部署延迟，且获得的滤波器易受测试时数据分布偏移的影响。本文提出一种面向可微粒子滤波的在线学习框架，使模型参数能够随数据到达实时更新。技术难点在于在线推断场景中不存在已知的基准真实状态信息。我们通过采用无监督损失函数构建在线模型更新流程来解决这一问题，该流程包含一系列基于在线最大似然参数估计的滤波操作。通过多变量线性高斯状态空间模型和模拟目标跟踪实验等仿真场景，我们实证评估了所提方法的有效性，并与监督学习方法进行了对比分析。