Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality
arXiv:2501.07942 · doi:10.1016/j.ifacol.2024.08.498
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.
This work has been accepted for IFAC SYSID24
References in corpus (5)
- Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions
- Quasioptimality of maximum-volume cross interpolation of tensors
- Design of Efficient Point-Mass Filter with Application in Terrain Aided Navigation
- Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics
- Reducing Computational Complexity of Tensor Contractions via Tensor-Train Networks