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

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1916
Author(s):  
Jaewook Shin ◽  
Jeesu Kim ◽  
Tae-Kyoung Kim ◽  
Jinwoo Yoo

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.

2012 ◽  
Vol 48 (15) ◽  
pp. 927 ◽  
Author(s):  
JinWoo Yoo ◽  
JaeWook Shin ◽  
Hyun-Tack Choi ◽  
PooGyeon Park

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 697
Author(s):  
Yingsong Li ◽  
Aleksey Cherednichenko ◽  
Zhengxiong Jiang ◽  
Wanlu Shi ◽  
Jinqiu Wu

A novel adaptive filtering (AF) algorithm is proposed for group-sparse system identifications. In the devised algorithm, a novel mixed error criterion (MEC) with two-order logarithm error, p-order errors and group sparse constraint method is devised to give a resistant to the impulsive noise. The proposed group-sparse MEC can fully use the known group-sparse characteristics in the cluster sparse systems, and it is derived and analyzed in detail. Various simulations are presented and analyzed to give a verification on the effectiveness of the developed group-sparse MEC algorithms, and the simulated results shown that the developed algorithm outperforms the previously developed sparse AF algorithms for identifying the systems.


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