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A bias‐compensated proportionate NLMS algorithm with noisy input signals
Author(s) -
Yoo JinWoo,
Shin JaeWook,
Park PooGyeon
Publication year - 2019
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4167
Subject(s) - algorithm , computer science , convergence (economics) , noise (video) , recursive least squares filter , least mean squares filter , filter (signal processing) , rate of convergence , identification (biology) , system identification , adaptive filter , control theory (sociology) , artificial intelligence , key (lock) , botany , computer security , control (management) , database , economics , image (mathematics) , computer vision , biology , measure (data warehouse) , economic growth
Summary 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.