| Full text | |
| Author(s): |
Bordin, Claudio J., Jr.
;
de Figueredo, Caio G.
;
Bruno, Marcelo G. S.
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP); v. N/A, p. 5-pg., 2022-01-01. |
| 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. (AU) | |
| FAPESP's process: | 18/26191-0 - Bayesian methods for distributed estimation in cooperative networks |
| Grantee: | Marcelo Gomes da Silva Bruno |
| Support Opportunities: | Regular Research Grants |