Abstract
In this work, a novel algorithm named sign regressor least mean mixed-norm (SRLMMN) algorithm is proposed as an alternative to the well-known least mean mixed-norm (LMMN) algorithm. The SRLMMN algorithm is a hybrid version of the sign regressor least mean square (SRLMS) and sign regressor least mean fourth (SRLMF) algorithms. Analytical expressions are derived to describe the convergence, steady-state, and tracking behavior of the proposed SRLMMN algorithm. To validate our theoretical findings, a system identification problem is considered for this purpose. It is shown that there is a very close correspondence between theory and simulation. Finally, it is also shown that the SRLMMN algorithm is robust enough in tracking the variations in the channel.
| Original language | English |
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| Title of host publication | 2015 23rd European Signal Processing Conference, EUSIPCO 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2691-2695 |
| Number of pages | 5 |
| ISBN (Electronic) | 9780992862633 |
| DOIs | |
| State | Published - 22 Dec 2015 |
Publication series
| Name | 2015 23rd European Signal Processing Conference, EUSIPCO 2015 |
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Bibliographical note
Publisher Copyright:© 2015 EURASIP.
Keywords
- LMF
- LMMN
- LMS
- SRLMF
- SRLMMN
- SRLMS
- convergence
- mixed-norm
- sign regressor
- steady-state
- tracking
ASJC Scopus subject areas
- Media Technology
- Computer Vision and Pattern Recognition
- Signal Processing