Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors

Understand

We present differentiable particle filters (DPFs): a differentiable implementation of the particle filter algorithm with learnable motion and measurement models.

• Since DPFs are end-to-end differentiable, we can efficiently train their models by optimizing end-to-end state estimation performance, rather than proxy objectives such as model accuracy.
• DPFs encode the structure of recursive state estimation with prediction and measurement update that operate on a probability distribution over states.
• This structure represents an algorithmic prior that improves learning performance in state estimation problems while enabling explainability of the learned model.