Deep Variational Sequential Monte Carlo for High-Dimensional Observations

Wessel L. van Nierop; Nir Shlezinger; Ruud J. G. van Sloun

ICASSP 2025accepted0 citations

Deep Variational Sequential Monte Carlo for High-Dimensional Observations

Wessel L. van Nierop, Nir Shlezinger, Ruud J. G. van Sloun

Abstract

Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a differentiable particle filter that leverages the unsupervised variational SMC objective to parameterize the proposal and transition distributions with a neural network, designed to learn from high-dimensional observations. Experimental results demonstrate that our approach outperforms established baselines in tracking the challenging Lorenz attractor from high-dimensional and partial observations. Furthermore, an evidence lower bound based evaluation indicates that our method offers a more accurate representation of the posterior distribution.

BibTeX

@inproceedings{icassp2025_deepvariationals,
  title = {Deep Variational Sequential Monte Carlo for High-Dimensional Observations},
  author = {Wessel L. van Nierop and Nir Shlezinger and Ruud J. G. van Sloun},
  booktitle = {ICASSP 2025},
  year = {2025}
}