Differentiable particle filters provide a flexible mechanism to adaptively train dynamic and measurement models by learning from observed data. However, most existing differentiable particle filters are within the bootstrap particle filtering framework and fail to incorporate the information from latest observations to construct better proposals. In this paper, we utilize conditional normalizing flows to construct proposal distributions for differentiable particle filters, enriching the distribution families that the proposal distributions can represent. In addition, normalizing flows are incorporated in the construction of the dynamic model, resulting in a more expressive dynamic model. We demonstrate the performance of the proposed conditional normalizing flow-based differentiable particle filters in a visual tracking task.

翻译：可区别的粒子过滤器提供了一个灵活的机制,通过从观测到的数据中学习,对动态和测量模型进行适应性培训。然而,大多数现有可区别的粒子过滤器都位于“靴子”粒子过滤框架之内,没有纳入最新观测结果的信息以构建更好的建议。在本文中,我们利用有条件的正常流来构建可区别的粒子过滤器的配置建议,丰富建议分布能够代表的分布式。此外,在构建动态模型的过程中,将流动的正常化纳入到动态模型中,从而形成一个更清晰的动态模型。我们展示了在视觉跟踪任务中拟议有条件的基于流动的可区别粒子过滤器的性能。