Improved multiband structured subband adaptive filter algorithm with L0-norm regularization for sparse system identification

2021 ◽  
pp. 103348
Author(s):  
Esmail Heydari ◽  
Mohammad Shams Esfand Abadi ◽  
Seyed Mahmoud Khademiyan
Keyword(s):  
System Identification ◽  
Adaptive Filter ◽  
Sparse System ◽  
Subband Adaptive Filter ◽  
Sparse System Identification

scholarly journals Subband Adaptive Filtering withl1-Norm Constraint for Sparse System Identification

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Young-Seok Choi
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Sparse System ◽  
Gradient Based ◽  
Subband Adaptive Filter ◽  
Sparse System Identification ◽  
Norm Constraint ◽  
Optimum Solution ◽  
The Cost ◽  
Normalized Subband Adaptive Filter

This paper presents a new approach of the normalized subband adaptive filter (NSAF) which directly exploits the sparsity condition of an underlying system for sparse system identification. The proposed NSAF integrates a weightedl1-norm constraint into the cost function of the NSAF algorithm. To get the optimum solution of the weightedl1-norm regularized cost function, a subgradient calculus is employed, resulting in a stochastic gradient based update recursion of the weightedl1-norm regularized NSAF. The choice of distinct weightedl1-norm regularization leads to two versions of thel1-norm regularized NSAF. Numerical results clearly indicate the superior convergence of thel1-norm regularized NSAFs over the classical NSAF especially when identifying a sparse system.

scholarly journals Block-Sparsity Log-Sum-Induced Adaptive Filter for Cluster Sparse System Identification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 175265-175276
Author(s):  
Aihua Zhang ◽  
Pengcheng Liu ◽  
Jun Sun ◽  
Bing Ning
Keyword(s):  
System Identification ◽  
Adaptive Filter ◽  
Sparse System ◽  
Sparse System Identification ◽  
Block Sparsity

scholarly journals A New Subband Adaptive Filtering Algorithm for Sparse System Identification with Impulsive Noise

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Young-Seok Choi
Keyword(s):  
System Identification ◽  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Impulsive Noise ◽  
Weight Vector ◽  
Sparse System ◽  
Subband Adaptive Filter ◽  
Sparse System Identification ◽  
The Cost ◽  
Norm Penalty

This paper presents a novel subband adaptive filter (SAF) for system identification where an impulse response is sparse and disturbed with an impulsive noise. Benefiting from the uses ofl1-norm optimization andl0-norm penalty of the weight vector in the cost function, the proposedl0-norm sign SAF (l0-SSAF) achieves both robustness against impulsive noise and remarkably improved convergence behavior more than the classical adaptive filters. Simulation results in the system identification scenario confirm that the proposedl0-norm SSAF is not only more robust but also faster and more accurate than its counterparts in the sparse system identification in the presence of impulsive noise.

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