Set‐membership normalised least M‐estimate spline adaptive filtering algorithm in impulsive noise

2018 ◽  
Vol 54 (6) ◽  
pp. 393-395 ◽  
Author(s):  
Chang Liu ◽  
Zhi Zhang
Keyword(s):  
Adaptive Filtering ◽  
Impulsive Noise ◽  
Filtering Algorithm ◽  
Set Membership ◽  
M Estimate

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 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.

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
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