Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality
Signal Processing
2025-01-20 v2
Abstract
This paper deals with the state estimation of stochastic systems and examines the possible employment of tensor decompositions in grid-based filtering routines, in particular, the tensor-train decomposition. The aim is to show that these techniques can lead to a massive reduction in both the computational and storage complexity of grid-based filtering algorithms without considerable tradeoffs in accuracy. This claim is supported by an algorithm descriptions and numerical illustrations.
Keywords
Cite
@article{arxiv.2501.07942,
title = {Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality},
author = {J. Matoušek and M. Brandner and J. Duník and I. Punčochář},
journal= {arXiv preprint arXiv:2501.07942},
year = {2025}
}
Comments
This work has been accepted for IFAC SYSID24