Differentiable Particle Filters through Conditional Normalizing Flow

• URL: http://arxiv.org/abs/2107.00488v1
• Date: Thu, 1 Jul 2021 14:31:27 GMT
• Title: Differentiable Particle Filters through Conditional Normalizing Flow
• Authors: Xiongjie Chen, Hao Wen, and Yunpeng Li
• Abstract summary: Differentiable particle filters provide a flexible mechanism to adaptively train dynamic and measurement models by learning from observed data. In this paper, we utilize conditional normalizing flows to construct proposal distributions for differentiable particle filters. We demonstrate the performance of the proposed conditional normalizing flow-based differentiable particle filters in a visual tracking task.
• Score: 6.230706386020833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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