| Full text | |
| Author(s): |
Bordin Jr, Claudio J.
;
Bruno, Marcelo G. S.
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING; v. N/A, p. 5-pg., 2020-01-01. |
| Abstract | |
In this paper, we extend previous particle filtering methods whose states were constrained to the (real) Stiefel manifold to the complex case. The method is then applied to a Bayesian formulation of the subspace tracking problem. To implement the proposed particle filter, we modify a previous MCMC algorithm so as to simulate from densities defined on the complex manifold. Also, to compute subspace estimates from particle approximations, we extend existing averaging methods to complex Grassmannians. As we verify via numerical simulations, the proposed method is advantageous over traditional SVD-based subspace tracking algorithms for scenarios with low signal-to-noise ratio. (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 |