Abstract
A class of algorithms representing a robust variant of the proportionate normalised least-mean-square (PNLMS) algorithm is proposed. To achieve this, adaptive regularisation is introduced within the PNLMS update, with the analysis conducted for both individual and global regularisation factors. The update of the adaptive regularisation parameter is also made robust, to improve steady state performance and reduce computational complexity. The proposed algorithms are better suited not only for operation in nonstationary environments, but are also less sensitive to changes in the input dynamics and the choice of their parameters. Simulations in a sparse environment show the proposed class of algorithms offer enhanced performance and increased stability over the standard PNLMS.
Original language | English |
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Title of host publication | 2007 15th International Conference on Digital Signal Processing, DSP 2007 |
Pages | 19-22 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 2007 |
Externally published | Yes |
Keywords
- Adaptive regularisation
- LMS
- Normalised LMS (NLMS)
- Proportionate NLMS (PNLMS)
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering