scholarly journals Diffusion Correntropy Subband Adaptive Filtering (SAF) Algorithm over Distributed Smart Dust Networks

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1335 ◽  
Author(s):  
Ying Guo ◽  
Jingjing Li ◽  
Yingsong Li
Keyword(s):  
Adaptive Filtering ◽  
Impulsive Noise ◽  
Gaussian Kernel ◽  
Sparse System ◽  
Smart Dust ◽  
Input Signals ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Weighted Values ◽  
Dynamic Gain

The diffusion subband adaptive filtering (DSAF) algorithm has attracted much attention in recent years due to its decorrelation ability for colored input signals. In this paper, a modified DSAF algorithm using the symmetry maximum correntropy criterion (MCC) with individual weighting factors is proposed and discussed to combat impulsive noise, which is denoted as the MCC-DSAF algorithm. During the iterations, the negative exponent in the Gaussian kernel of the MCC-DSAF eliminates the interference of outliers to provide a robust performance in non-Gaussian noise environments. Moreover, in order to enhance the convergence for sparse system identifications, a variant of MCC-DSAF named as improved proportionate MCC-DSAF (MCC-IPDSAF) is presented and investigated, which provides a dynamic gain assignment matrix in the MCC-DSAF to adjust the weighted values of each coefficient. Simulation results verify that the newly presented MCC-DSAF and MCC-IPDSAF algorithms are superior to the popular DSAF 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.

scholarly journals Diffusion Generalized MCC with a Variable Center Algorithm for Robust Distributed Estimation

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2807
Author(s):  
Wentao Ma ◽  
Panfei Cai ◽  
Fengyuan Sun ◽  
Xiao Kou ◽  
Xiaofei Wang ◽  
...  
Keyword(s):  
Adaptive Filtering ◽  
Gaussian Noise ◽  
Distributed Estimation ◽  
Filtering Algorithms ◽  
Combine Strategy ◽  
Stable Performance ◽  
The Mean ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Variable Center

Classical adaptive filtering algorithms with a diffusion strategy under the mean square error (MSE) criterion can face difficulties in distributed estimation (DE) over networks in a complex noise environment, such as non-zero mean non-Gaussian noise, with the object of ensuring a robust performance. In order to overcome such limitations, this paper proposes a novel robust diffusion adaptive filtering algorithm, which is developed by using a variable center generalized maximum Correntropy criterion (GMCC-VC). Generalized Correntropy with a variable center is first defined by introducing a non-zero center to the original generalized Correntropy, which can be used as robust cost function, called GMCC-VC, for adaptive filtering algorithms. In order to improve the robustness of the traditional MSE-based DE algorithms, the GMCC-VC is used in a diffusion adaptive filter to design a novel robust DE method with the adapt-then-combine strategy. This can achieve outstanding steady-state performance under non-Gaussian noise environments because the GMCC-VC can match the distribution of the noise with that of non-zero mean non-Gaussian noise. The simulation results for distributed estimation under non-zero mean non-Gaussian noise cases demonstrate that the proposed diffusion GMCC-VC approach produces a more robustness and stable performance than some other comparable DE methods.

scholarly journals Newtonian-Type Adaptive Filtering Based on the Maximum Correntropy Criterion

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 922
Author(s):  
Pengcheng Yue ◽  
Hua Qu ◽  
Jihong Zhao ◽  
Meng Wang
Keyword(s):  
Steady State ◽  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Impulsive Noise ◽  
Minimum Mean Square Error ◽  
Gradient Descent Method ◽  
Type Method ◽  
Newtonian Type ◽  
Maximum Correntropy Criterion ◽  
Steady State Performance

This paper provides a novel Newtonian-type optimization method for robust adaptive filtering inspired by information theory learning. With the traditional minimum mean square error (MMSE) criterion replaced by criteria like the maximum correntropy criterion (MCC) or generalized maximum correntropy criterion (GMCC), adaptive filters assign less emphasis on the outlier data, thus become more robust against impulsive noises. The optimization methods adopted in current MCC-based LMS-type and RLS-type adaptive filters are gradient descent method and fixed point iteration, respectively. However, in this paper, a Newtonian-type method is introduced as a novel method for enhancing the existing body of knowledge of MCC-based adaptive filtering and providing a fast convergence rate. Theoretical analysis of the steady-state performance of the algorithm is carried out and verified by simulations. The experimental results show that, compared to the conventional MCC adaptive filter, the MCC-based Newtonian-type method converges faster and still maintains a good steady-state performance under impulsive noise. The practicability of the algorithm is also verified in the experiment of acoustic echo cancellation.

scholarly journals Frequency domain maximum correntropy criterion spline adaptive filtering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenyan Guo ◽  
Yongfeng Zhi ◽  
Kai Feng
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Adaptive Filters ◽  
Operation Time ◽  
Filtering Algorithm ◽  
Impulsive Noises ◽  
Type Frequency ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian

AbstractA filtering algorithm based on frequency domain spline type, frequency domain spline adaptive filters (FDSAF), effectively reducing the computational complexity of the filter. However, the FDSAF algorithm is unable to suppress non-Gaussian impulsive noises. To suppression non-Gaussian impulsive noises along with having comparable operation time, a maximum correntropy criterion (MCC) based frequency domain spline adaptive filter called frequency domain maximum correntropy criterion spline adaptive filter (FDSAF-MCC) is developed in this paper. Further, the bound on learning rate for convergence of the proposed algorithm is also studied. And through experimental simulations verify the effectiveness of the proposed algorithm in suppressing non-Gaussian impulsive noises. Compared with the existing frequency domain spline adaptive filter, the proposed algorithm has better performance.

scholarly journals An adaptive kernel width convex combination method for maximum correntropy criterion

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Aluisio I. R. Fontes ◽  
Leandro L. S. Linhares ◽  
João P. F. Guimarães ◽  
Luiz F. Q. Silveira ◽  
Allan M. Martins
Keyword(s):  
Adaptive Filtering ◽  
Convergence Rates ◽  
Impulsive Noise ◽  
Convex Combination ◽  
Combination Method ◽  
Kernel Width ◽  
Optimal Value ◽  
Adaptive Kernel ◽  
Novel Method ◽  
Maximum Correntropy Criterion

AbstractRecently, the maximum correntropy criterion (MCC) has been successfully applied in numerous applications regarding nonGaussian data processing. MCC employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the adaptive filtering. However, determining the optimal value for such parameter is not always a trivial task. Within this context, this paper proposes a novel method called adaptive convex combination maximum correntropy criterion (ACCMCC), which combines an adaptive kernel algorithm with convex combination techniques. ACCMCC takes advantage from a convex combination of two adaptive MCC-based filters, whose kernel widths are adjusted iteratively as a function of the minimum error value obtained in a predefined estimation window. Results obtained in impulsive noise environment have shown that the proposed approach achieves equivalent convergence rates but with increased accuracy and robustness when compared with other similar algorithms reported in literature.

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.

scholarly journals A Correntropy-Based Proportionate Affine Projection Algorithm for Estimating Sparse Channels with Impulsive Noise

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 555 ◽  
Author(s):  
Zhengxiong Jiang ◽  
Yingsong Li ◽  
Xinqi Huang
Keyword(s):  
Wireless Communication ◽  
Impulsive Noise ◽  
Communication Channels ◽  
Projection Algorithm ◽  
Identification Performance ◽  
Affine Projection ◽  
Sparse Channels ◽  
Input Signals ◽  
Simulation Results ◽  
Maximum Correntropy Criterion

A novel robust proportionate affine projection (AP) algorithm is devised for estimating sparse channels, which often occur in network echo and wireless communication channels. The newly proposed algorithm is realized by using the maximum correntropy criterion (MCC) and the data reusing scheme used in AP to overcome the identification performance degradation of the traditional PAP algorithm in impulsive noise environments. The proposed algorithm is referred to as the proportionate affine projection maximum correntropy criterion (PAPMCC) algorithm, which is derived in the context of channel estimation framework. Many simulation results were obtained to verify that the PAPMCC algorithm is superior to early reported AP algorithms with different input signals under impulsive noise environments.

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