| Full text | |
| Author(s): |
Claser, Raffaello
;
Nascimento, Vitor H.
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO); v. N/A, p. 4-pg., 2017-01-01. |
| Abstract | |
It is known that combinations of the least mean square (LMS) and recursive least squares (RLS) algorithms may achieve a performance in tracking better than what is possible to obtain with either kind of filter individually. In this paper, we consider combinations of LMS and RLS filters and compare their performance under a nonstationary condition with the optimal solution obtained via Kalman filter. We show that combination schemes may have a tracking performance close to that of a Kalman filter, but with lower computational complexity (linear in the filter length instead of quadratic - in the case of the example shown here - or cubic, for general Kalman models). (AU) | |
| FAPESP's process: | 14/04256-2 - Low-cost algorithms for parameter estimation |
| Grantee: | Vitor Heloiz Nascimento |
| Support Opportunities: | Regular Research Grants |