| Texto completo | |
| Autor(es): |
Bordin, Claudio J., Jr.
;
Bruno, Marcelo G. S.
;
IEEE
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP); v. N/A, p. 5-pg., 2019-01-01. |
| Resumo | |
Many problems in statistical signal processing involve tracking the state of a dynamic system that evolves on a Stiefel manifold. To this aim, we introduce in this paper a novel particle filter algorithm that approximates the optimal importance function on the Stiefel manifold and is capable of handling nonlinear observation functions. To sample from the required importance function, we develop adaptations of previous MCMC algorithms. We verify via numerical simulations that, in a scenario with a strongly nonlinear observation model, the new proposed method outperforms existing algorithms that use the prior importance function at the cost, however, of increased computational complexity. (AU) | |
| Processo FAPESP: | 18/26191-0 - Métodos Bayesianos para estimação distribuída em redes cooperativas |
| Beneficiário: | Marcelo Gomes da Silva Bruno |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |