@inproceedings{b7329dd4c14c4458979da3a2529ca4e4,
title = "Generalized Two-Stage Particle Filter for High Dimensions",
abstract = "The curse of dimensionality has been a long-standing problem in the field of particle filters (PFs), and prevents their use in real complex systems characterized by large number of unknowns. Recently a two-stage PF (TPF) for high dimensions was proposed, showing promising results with modest number of particles. The TPF modifies the proposal distribution by tempering each state dimension towards the most likely particle proposed and carrying out regular filtering using the constructed proposal. However, it is limited to cases where the measurement equations are completely separable. We propose a new filter inspired by the TPF principle as well as multiple PF (MPF), that can be applied to any setup and that provides a posterior distribution of the tempering coefficient that is updated recursively. Simulations show comparable, and in some cases, even better results than those of a recently proposed improved MPF for high dimensions.",
keywords = "high dimensions, particle filters, state-space models, two-stage filtering",
author = "Marija Iloska and Bugallo, \{M{\'o}nica F.\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
doi = "10.1109/ICASSP49357.2023.10096542",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",
}