Authors:
Jae Jin Jeong
;
Seung Hun Kim
;
Gyogwon Koo
and
Sang Woo Kim
Affiliation:
Pohang University of Science and Technology (POSTECH), Korea, Republic of
Keyword(s):
Adaptive Filter, Impulsive Noise, Sign Algorithm, Mean-Square Deviation.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
Abstract:
The sign subband adaptive filter (SSAF) algorithm is introduced to reduce performance degradation of least-mean-square-type algorithms due to a correlated input signal or an impulsive noise environments. However,
this algorithmh has huge computational complexity when the length of the unknown system is large. In
this paper, we focus on reduce computational complexity of the conventional SSAF algorithm and propose
an SSAF algorithm which selects number of subbands according to convergence state. The specific bands
which contributes to decrease the mean-square deviation are used to update the adaptive filter. Thus, the
proposed algorithm reduces the computational complexity compared to the conventional SSAF algorithm.
The selection mehtod is derived by analysing the mean-square deviation. Through the computer simulation,
simulation results are presented that demonstrate the fast convergence rate of the proposed algorithm and save
the computational cost.