A bias-compensated proportionate NLMS algorithm with noisy input signals
- Authors
- Yoo, JinWoo; Shin, JaeWook; Park, PooGyeon
- Issue Date
- Dec-2019
- Publisher
- John Wiley & Sons Inc.
- Keywords
- adaptive filter; bias; input noise; noisy input; proportionate normalized least-mean-squares (PNLMS); sparse system
- Citation
- International Journal of Communication Systems, v.32, no.18
- Journal Title
- International Journal of Communication Systems
- Volume
- 32
- Number
- 18
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/4073
- DOI
- 10.1002/dac.4167
- ISSN
- 1074-5351
1099-1131
- Abstract
- This paper proposes a novel proportionate normalized least-mean-squares (PNLMS) algorithm that is robust to input noises. Through compensating for biases due to input noise added at the filter input, the proposed PNLMS algorithm avoids performance deterioration owing to the noisy input signals. Moreover, since the proposed PNLMS algorithm uses a new gain-distribution matrix, it has a fast convergence rate compared with the existing PNLMS algorithms, even when there is no input noise. The experimental results verify that the proposed PNLMS algorithm enhances the filter performance for sparse system identification in the presence of input noises.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Medical Sciences > Department of Biomedical Mechatronics > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.