A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems

Jiang, Zhengxiong; Li, Yingsong; Huang, Xinqi; Jin, Zuanming

doi:10.3390/sym11101218

Cited by 4 publications

(1 citation statement)

References 48 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Representative adaptive filtering algorithms include the least-mean-squares (LMS) algorithm and normalized LMS algorithm, which have a low computational complexity and can be easily implemented. In addition, the affine projection algorithm (APA) [7][8][9] has been developed to enhance the convergence performance associated with correlated input signals. However, because LMS-type and APA-type algorithms are based on the L 2 -norm optimization of error signals, their performance deteriorates in the presence of system output noise, which includes impulsive noise.…”

Section: Introductionmentioning

confidence: 99%

ℒp-Norm-like Affine Projection Sign Algorithm for Sparse System to Ensure Robustness against Impulsive Noise

Shin

Kim

et al. 2021

Symmetry

View full text Add to dashboard Cite

An improved affine projection sign algorithm (APSA) was developed herein using a ℒp-norm-like constraint to increase the convergence rate in sparse systems. The proposed APSA is robust against impulsive noise because APSA-type algorithms are generally based on the ℒ1-norm minimization of error signals. Moreover, the proposed algorithm can enhance the filter performance in terms of the convergence rate due to the implementation of the ℒp-norm-like constraint in sparse systems. Since a novel cost function of the proposed APSA was designed for maintaining the similar form of the original APSA, these have symmetric properties. According to the simulation results, the proposed APSA effectively enhances the filter performance in terms of the convergence rate of sparse system identification in the presence of impulsive noises compared to that achieved using the existing APSA-type algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%