Distributed Particle Filters for State Tracking on the Stiefel Manifold Using Tangent Space Statistics

Claudio J. Bordin; Caio Gomes de Figueredo; Marcelo G. S. Bruno

ICASSP 2022accepted0 citations

Distributed Particle Filters for State Tracking on the Stiefel Manifold Using Tangent Space Statistics

Claudio J. Bordin, Caio Gomes de Figueredo, Marcelo G. S. Bruno

Abstract

This paper introduces a novel distributed diffusion algorithm for tracking the state of a dynamic system that evolves on the Stiefel manifold. To compress information exchanged between nodes, the algorithm builds a Gaussian parametric approximation to the particles that are previously projected onto the tangent space to the Stiefel manifold and mapped to real vectors. Observations from neighboring nodes are then assimilated for a general nonlinear observation model. Performance results are compared to those of competing linear diffusion Extended Kalman Filters and other particle filters.

BibTeX

@inproceedings{icassp2022_distributedparti,
  title = {Distributed Particle Filters for State Tracking on the Stiefel Manifold Using Tangent Space Statistics},
  author = {Claudio J. Bordin and Caio Gomes de Figueredo and Marcelo G. S. Bruno},
  booktitle = {ICASSP 2022},
  year = {2022}
}