scholarly journals A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 697
Author(s):  
Yingsong Li ◽  
Aleksey Cherednichenko ◽  
Zhengxiong Jiang ◽  
Wanlu Shi ◽  
Jinqiu Wu
Keyword(s):  
Adaptive Filtering ◽  
Impulsive Noise ◽  
Sparse Constraint ◽  
Error Criterion ◽  
Filtering Algorithm ◽  
Sparse System ◽  
Sparse Systems ◽  
Constraint Method

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.

scholarly journals Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 196
Author(s):  
Jun Lu ◽  
Qunfei Zhang ◽  
Wentao Shi ◽  
Lingling Zhang ◽  
Juan Shi
Keyword(s):  
Adaptive Filtering ◽  
Interference Cancellation ◽  
Impulsive Noise ◽  
Weight Vector ◽  
Filtering Algorithm ◽  
Convergence Performance ◽  
Simulation Results ◽  
Robust Adaptive ◽  
Arctangent Cost Function ◽  
Simultaneous Transmission

Self-interference (SI) is usually generated by the simultaneous transmission and reception in the same system, and the variable SI channel and impulsive noise make it difficult to eliminate. Therefore, this paper proposes an adaptive digital SI cancellation algorithm, which is an improved normalized sub-band adaptive filtering (NSAF) algorithm based on the sparsity of the SI channel and the arctangent cost function. The weight vector is hardly updated when the impulsive noise occurs, and the iteration error resulting from impulsive noise is significantly reduced. Another major factor affecting the performance of SI cancellation is the variable SI channel. To solve this problem, the sparsity of the SI channel is estimated with the estimation of the weight vector at each iteration, and it is used to adjust the weight vector. Then, the convergence performance and calculation complexity are analyzed theoretically. Simulation results indicate that the proposed algorithm has better performance than the referenced algorithms.

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
Keyword(s):  
Convergence Rate ◽  
Impulsive Noise ◽  
Affine Projection ◽  
Filter Performance ◽  
Norm Minimization ◽  
Sparse System ◽  
Impulsive Noises ◽  
Sparse Systems ◽  
Sparse System Identification ◽  
Sign Algorithm

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.

A New Proportionate Adaptive Filtering Algorithm with Coefficient Reuse and Robustness Against Impulsive Noise

2018 ◽  
Author(s):  
Rodrigo M. S. Pimenta ◽  
Leonardo C. Resende ◽  
Newton N. Siqueira ◽  
Idiego B. Haddad ◽  
Mariane R. Petraglia
Keyword(s):  
Adaptive Filtering ◽  
Impulsive Noise ◽  
Filtering Algorithm

Dynamic RLS-DCD for Sparse System Identification

2014 ◽  
Vol 602-605 ◽  
pp. 2411-2414
Author(s):  
Qing Xia ◽  
Yun Lin ◽  
Hui Luo
Keyword(s):  
System Identification ◽  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Coordinate Descent ◽  
Identification Performance ◽  
Computationally Efficient ◽  
Filtering Algorithm ◽  
Sparse System ◽  
Sparse System Identification ◽  
Better Than

In this passage we propose a computationally efficient adaptive filtering algorithm for sparse system identification.The algorithm is based on dichotomous coordinate descent iterations, reweighting iterations,iterative support detection.In order to reduce the complexity we try to discuss in the support.we suppose the support is partial,and partly erroneous.Then we can use the iterative support detection to solve the problem.Numerical examples show that the proposed method achieves an identification performance better than that of advanced sparse adaptive filters (l1-RLS,l0-RLS) and its performance is close to the oracle performance.

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