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 A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 683 ◽  
Author(s):  
Yingsong Li ◽  
Yanyan Wang ◽  
Laijun Sun
Keyword(s):  
Impulse Response ◽  
Adaptive Filter ◽  
Finite Impulse Response ◽  
Least Mean Square ◽  
Sparse Systems ◽  
Simulation Results ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Fir Systems ◽  
Filter Algorithms

A proportionate-type normalized maximum correntropy criterion (PNMCC) with a correntropy induced metric (CIM) zero attraction terms is presented, whose performance is also discussed for identifying sparse systems. The proposed sparse algorithms utilize the advantage of proportionate schemed adaptive filter, maximum correntropy criterion (MCC) algorithm, and zero attraction theory. The CIM scheme is incorporated into the basic MCC to further utilize the sparsity of inherent sparse systems, resulting in the name of the CIM-PNMCC algorithm. The derivation of the CIM-PNMCC is given. The proposed algorithms are used for evaluating the sparse systems in a non-Gaussian environment and the simulation results show that the expanded normalized maximum correntropy criterion (NMCC) adaptive filter algorithms achieve better performance than those of the squared proportionate algorithms such as proportionate normalized least mean square (PNLMS) algorithm. The proposed algorithm can be used for estimating finite impulse response (FIR) systems with symmetric impulse response to prevent the phase distortion in communication system.

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.

The Research of Adaptive Filtering Algorithm and System Modeling

2014 ◽  
Vol 556-562 ◽  
pp. 3722-3725
Author(s):  
Sheng Ping Zhang ◽  
Dong Ya Chen
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Filter ◽  
System Modeling ◽  
Input Sequence ◽  
Filter Theory ◽  
Recognition Model ◽  
Filtering Algorithm ◽  
Lms Method ◽  
Unknown System ◽  
Fir Model

This paper introduces an adaptive adjusting FIR filter’s parameters (LMS) method and presents a system recognition model based on the adaptive filter theory. The adaptive filter is directly with adjustable coefficient h (0),h (1),...h (N-1).The unknown system and FIR model have the same input sequence. The simulation result confirms the feasibility of the model.

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.

An Adaptive Filtering Method for Chirp Signal Based on FRFT

2014 ◽  
Vol 989-994 ◽  
pp. 3934-3937
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Gang Cheng
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Gaussian Noise ◽  
Fractional Fourier Transform ◽  
Energy Concentration ◽  
Filter Method ◽  
Chirp Signal ◽  
Filtering Method ◽  
Filtering Algorithm ◽  
Snr Improvement

In this paper, an adaptive filter method for Chirp signal in additional white Gaussian noise (AWGN) is studied. First, we analyze conception and character of FRFT (Fractional Fourier Transform), and the results shows that Chirp signal has energy concentration character in FRFT domain. Then, an adaptive filtering method is introduced in FRFT domain, and a test is done for separating Chirp signal from AWGN by the filtering method. Last, performance of this filtering method is studied, and the SNR improvement expression is concluded to verifying its advantages, it depends on the performance of adaptive filter basically. At last, the results show that this filtering algorithm is simple in computation and easy in implementation.

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 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.

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