Presence Of Impulsive Noise Research Articles

DOA Tracking With Nested Arrays via an Improved Multisource Multi-Bernoulli Filtering in the Presence of Impulsive Noise

Traditional DOA tracking algorithms have poor performance when considering source time-varying and impulsive noise interference. To address these issues, an improved multisource multi-Bernoulli (IMSMB) particle filtering algorithm based on phased fractional lower order moment is proposed. In the IMSMB algorithm, an unscented transform method is used to operate the surviving particles, which increases the diversity of resampling particles. Moreover, the proposed method is extended to nested array to improve tracking accuracy. Simulation results verify the superiority of the proposed method compared to the existing DOA tracking algorithm.

Nonlinear channel estimation based on robust distributed Hammerstein spline adaptive technique in wireless sensor network

In a wireless sensor network (WSN), channel estimation prior to data communication is an important task. The nonlinear behaviour of the channel makes the estimation procedure challenging. Further, the accuracy of the estimation algorithm deteriorates in the presence of impulsive noise. In such a scenario, the conventional adaptive filtering methods based on the mean square error cost function and its different variants are not suitable for estimating the channel. Hence, this paper proposes a robust Hammerstein spline adaptive filtering based on the inverse square root cost function to mitigate impulsive noise's effect and adapt to the nonlinearity. Further, to fasten the estimation procedure, the robust method is extended to the distributed WSN, where diffusion cooperation among the sensor node is incorporated to estimate the nonlinear channel. The theoretical steady state analysis of the proposed method is carried out and compared with the simulation results, which validates the algorithm performance. The simulation results confirm the robustness of the proposed method compared to other robust strategies based on maximum correntropy criteria, logarithmic hyperbolic cosine cost function, and Geman-McClure cost function.

Robust uplink channel estimation for massive MIMO systems with general array geometry

Least-squares (LS) estimator is the most commonly used uplink channel estimator for massive multiple-input multiple-output (MIMO) systems, but its performance depends critically on the choice of the training pilots and is also sensitive to outlier measurements. In this paper, we exploit the channel sparsity to significantly improve the uplink channel estimation performance for any pilot pattern in the presence of impulsive noise. The key to the success of the proposed method is the independent variational Bayesian inference (VBI) factorization imposed in the sparse Bayesian learning (SBL) framework, which can efficiently separate the sparse signal-of-interest and the sparse representation of the impulsive noise, as well as decoupling sparse signals from the pilots automatically. Moreover, we demonstrate that the proposed method can be extended to a general array geometry by introducing a coarse non-uniform sampling 2D-grid into the SBL framework. Simulation results verify the superiority of the developed method.

Variable Matrix-Type Step-Size Affine Projection Sign Algorithm for System Identification in the Presence of Impulsive Noise

This paper presents a novel variable matrix-type step-size affine projection sign algorithm (VMSS-APSA) characterized by robustness against impulsive noise. To mathematically derive a matrix-type step size, VMSS-APSA utilizes mean-square deviation (MSD) for the modified version of the original APSA. Accurately establishing the MSD of APSA is impossible. Therefore, the proposed VMSS-APSA derives the upper bound of the MSD using the upper bound of the L1-norm of the measurement noise. The optimal matrix-type step size is calculated at each iteration by minimizing the upper bound of the MSD, thereby improving the filter performance in terms of convergence rate and steady-state estimation error. Because a novel cost function of the proposed VMSS-APSA was designed to maintain a form similar to the original APSA, they have symmetric characteristics. Simulation results demonstrate that the proposed VMSS-APSA improves filter performance in a system-identification scenario in the presence of impulsive noise.

Maximum total generalized correntropy adaptive filtering for parameter estimation

In this study, we consider the parameter estimation problem for an errors-in-variables (EIV) model with impulse noise. New adaptive filtering, called the maximum total generalized correntropy (MTGC) adaptive filtering algorithm, is developed to further improve the robustness of conventional adaptive filtering algorithms. Specifically, the proposed approach is derived by integrating the generalized maximum correntropy (GMC) criterion into the total least square (TLS) framework. The local stability and steady-state properties are investigated with the aid of the generalized Gaussian process. Numerical simulations are presented to illustrate the effectiveness of the proposed method in the presence of impulse noise.

A New Robust Adaptive Algorithm for Second Order Blind Source Separation

This paper deals with the adaptive blind source separation problem in presence of impulsive noise. New algorithms extending the well known SOBI method from batch to adaptive scheme are introduced. At first, the standard Gaussian noise case is considered, leading to our first algorithm referred to as Adaptive SOBI (A-SOBI). Later on, a robust version of A-SOBI, referred to as RA-SOBI is derived to handle the impulsive noise case. RA-SOBI relies on robust subspace tracking for the whitening stage together with robust correlation estimation for the separation stage. All proposed algorithms are of relatively low complexity and allow to achieve good separation quality as illustrated by our simulation results.

A Primal–Dual Fixed-Point Algorithm for TVL1 Wavelet Inpainting Based on Moreau Envelope

In this paper, we present a novel variational wavelet inpainting based on the total variation (TV) regularization and the l1-norm fitting term. The goal of this model is to recover incomplete wavelet coefficients in the presence of impulsive noise. By incorporating the Moreau envelope, the proposed model for wavelet inpainting can better handle the non-differentiability of the l1-norm fitting term. A modified primal dual fixed-point algorithm is developed based on the proximity operator to solve the proposed variational model. Moreover, we consider the existence of solution for the proposed model and the convergence analysis of the developed iterative scheme in this paper. Numerical experiments show the desirable performance of our method.

Proportionate M-estimate adaptive filtering algorithms: Insights and improvements

In the literature, the proportionate least mean M-estimate (PLMM) algorithm exhibits good performance when dealing with sparse systems in the presence of impulsive noises. In this paper, the mean and mean-square behaviors of three recursion types of the PLMM algorithm are studied in depth. We derive analytically the stability, transient and steady-state results of these PLMM recursions, and find that they can achieve the same performance when properly choosing the step-size and the proportionate matrix. To improve the filter performance in both convergence rate and steady-state error, we derive a variable step-size (VSS) scheme and then present the VSS-based PLMM algorithms. In addition, based on the adaptive decorrelation strategy aiming at the colored input signals, the VSS adaptive decorrelation PLMM algorithms are developed to further speed up the convergence. Computer simulations have verified our theoretical analyses and the effectiveness of the proposed algorithms.

Robust NLMS algorithms with combined step-size against impulsive noises

In the presence of impulsive noises, the normalized least mean M-estimate (NLMM) algorithm has behaved better robustness and convergence than the normalized least mean square (NLMS) algorithm. In order to further solve the trade-off of the NLMM algorithm between convergence rate and steady-state misadjustment, we design a combined step-size (CSS) scheme that combines large and small step-sizes through an adaptive mixing factor, and the resulting CSS-NLMM algorithm obtains fast convergence and low steady-state misadjustment simultaneously. Importantly, the proposed CSS scheme can be straightforwardly extended to other robust NLMS algorithms. Moreover, the performance analysis of the CSS-NLMM algorithm is provided. Simulation results in impulsive noises have supported the effectiveness of the proposed CSS-NLMM algorithm and its performance analysis.

Power quality enhancement in microgrid using adaptive affine projection controlled medium voltage distribution static compensator

This work presents a unique application of affine projection like M-estimate adaptive filtering technique. Affine projection like M-estimate adaptive filter has been used to control the medium voltage distribution static compensator connected at the main grid of hybrid AC/DC microgrid. The effectiveness of the proposed control technique is presented against the conventional controls. The proposed control technique uses M-estimate cost function for the suppression of current harmonics using medium voltage distribution static compensator in IEEE 14 bus system. M-estimate cost function protects the filter weights in the presence of impulsive noise in the system. Also, the proposed affine projection-like M-estimate algorithm includes direct calculation of weight update whereas its parent version affine projection algorithm requires inversion of the input signal which sometimes leads to instability and computational burden. The presented adaptive control is used to separate the fundamental constituents of currents from the polluted load currents due to the presence of nonlinear loads in IEEE 14 bus system. The IEEE 14 bus system is designed in MATLAB/Simulink platform. The dynamic performance of the proposed affine projection like M-estimate algorithm is analysed under the varying load condition. The proposed adaptive control-based technique is validated to maintain IEEE-519 standard.

Modeling Impulsive Ball Mill Forces Effects on the Dynamic Behavior of a Single-Stage Gearbox

Gearboxes are frequently used in the mining industry, especially for power transmission between the electric drive and the ball mill; besides the extreme complexity of a ball mill gear transmission system, the fault diagnosis by vibration analysis can be easily distorted by the presence of impulsive noises due to the ball pulses on the mill shell. Although several works in the literature are related to the influence of an impulsive noise on the accuracy of the diagnosis, no dynamic model exists yet in the literature that can explain the influence of these forces on the dynamic behavior of gearboxes. This paper presents a new approach to determine the influence of the grinding forces in crack defects diagnosis. This approach is based on a hybrid numerical model of a 24-degree-of-freedom gearbox, simulating one gear train and two drive shafts. The impact forces of the mill drum are modelled by a discrete element method (DEM). The ball-filling rate (Fr), the mill speed (Nr), and the ball size (Db) are considered to study this phenomenon. The simulations results show by a time series representation, fast Fourier transform, and short-time Fourier transform (STFT), that the acceleration is significantly affected by the presence of the grinding forces, developing an impulsive noise due to the impact of the balls governed by the studied parameters.

Image restoration in the presence of impulse noise by adaptive equidistant median filter

Image restoration in the presence of impulse noise by adaptive equidistant median filter

Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

This work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a denoising network to suppress outliers and filter out impulsive noise. Secondly, the denoising network output is fed into a model order selection network to estimate the model order. Next, according to the estimation, the denoising network output is fed into a DOA subnetwork corresponding to the model order in a DOA network to estimate the DOA of each signal. Comprehensive simulations demonstrate that, in the presence of impulsive noise, the proposed method is effective and superior in accuracy and computation speed for multisource DOA estimation. Therefore, it is concluded that CNN can be well generalized for DOA estimation.

ECF-MUSIC: An empirical characteristic function based direction of arrival (DOA) estimation in the presence of impulsive noise

DOA estimation in the presence of impulsive signals and noises is a challenging issue. Conventional and fractional lower order based methods do not work properly in the presence of very heavy-tailed noise. Interesting properties of empirical characteristic function (ECF) such as great simplicity and high accuracy leads to develop new methods, which are presented in this paper. It is analytically shown that by using the characteristic function of the array signal, a new sample covariance matrix can be extracted to use in algorithms like MUSIC. Numerical methods and the optimum set of parameters are also presented to estimate this matrix from the ECF of the array signal. Monte-Carlo simulation results are also presented which demonstrate the effectiveness and improved performance of the algorithms in highly impulsive environments. It is particularly effective in comparison to available methods in terms of probability of resolution and estimation error. The results indicate that the proposed method has a superior performance under the condition of heavy-tailed noise.

A robust diffusion algorithm using logarithmic hyperbolic cosine cost function for channel estimation in wireless sensor network under impulsive noise environment

In a wireless sensor network (WSN), the performance of the error-squared based adaptive channel estimation algorithm degrades in the presence of impulsive noise. Robust methods are used to minimize the impulsive noise impact at the cost of a slow convergence rate. We propose a robust diffusion adaptive channel estimation algorithm using the logarithmic hyperbolic cosine cost function assuming that the received signal at each node is corrupted by impulsive noise. The sensor nodes experiencing the common channel with the base station are grouped using their initial channel estimates. After grouping, the robust diffusion algorithm is applied in each group to estimate the corresponding channel. The steady-state mean square deviation (MSD) and excess mean square error (EMSE) of the robust algorithm are derived. The algorithm's steady-state and convergence rate performance are simulated for Rayleigh fading channel corrupted by Bernoulli-Gaussian distributed impulsive noise. The simulation results demonstrate that the proposed algorithm gives faster convergence without compromising steady-state performance compared to other robust algorithms.

Widely Linear Maximum Complex Correntropy Criterion Affine Projection Algorithm and Its Performance Analysis

Recently, affine projection algorithm has been extensively studied in the Gaussian noise environment. However, the performance of affine projection algorithm will deteriorate rapidly in the presence of impulsive noise and other non-Gaussian noise. To address this issue, this paper proposes a novel affine projection algorithm based on the complex Gaussian kernel function, called widely linear maximum complex correntropy criterion affine projection algorithm (WL-MCCC-APA). With the maximum complex correntropy criterion (MCCC), the robustness of affine projection algorithm is improved significantly in the non-Gaussian noise environment, especially in the presence of impulsive noise. Meanwhile, a variable step size WL-MCCC-APA (VSS-WL-MCCC-APA) method based on fixed-point theory is proposed to further balance the convergence rate and steady-state misalignment of the algorithm. Finally, the step size boundary analysis and steady-state analysis are presented for theoretical analysis. Simulation results verify the correctness of obtained theoretical results and the superiority of the proposed WL-MCCC-APA and VSS-WL -MCCC-APA.

Prediction of chaotic time series based on Nyström Cauchy kernel conjugate gradient algorithm

Chaotic time series can well reflect the nonlinearity and non-stationarity of real environment changes. The traditional kernel adaptive filter (KAF) with second-order statistical characteristics suffers performance degeneration dramatically for predicting chaotic time series containing noises and outliers. In order to improve the robustness of adaptive filters in the presence of impulsive noise, a nonlinear similarity measure named Cauchy kernel loss (CKL) is proposed, and the global convexity of CKL is guaranteed by the half-quadratic (HQ) method. To improve the convergence rate of stochastic gradient descent and avoid a local optimum simultaneously, the conjugate gradient (CG) method is used to optimize CKL. Furthermore, to address the issue of kernel matrix network growth, the Nyström sparse strategy is adopted to approximate the kernel matrix and then the probability density rank-based quantization (PRQ) is used to improve the approximation accuracy. To this end, a novel Nyström Cauchy kernel conjugate gradient with PRQ (NCKCG-PRQ) algorithm is proposed for the prediction of chaotic time series in this paper. Simulations on prediction of synthetic and real-world chaotic time series validate the advantages of the proposed algorithm in terms of filtering accuracy, robustness, and computational storage complexity.

Turbo Code with Hybrid Interleaver in the Presence of Impulsive Noise

In this paper, a numerical density function of Impulsive symmetric α-stable (ISAS) noise was presented and simulated in the turbo coding environment. The ISAS noise has no closed-form expression, making it difficult to realize. The ISAS noise can be used to approximate many kinds of noise sources. In this paper, one of the parameters is used to set it as the Cauchy distribution. In addition, an improved interleaver structure is presented where the multiple interleavers of smaller capacity replace a single epic interleaver. Simulation results show that the hybrid interleaver outperforms the other interleavers presented in this paper.

Robust Proportionate Normalized Least Mean M-Estimate Algorithm for Block-Sparse System Identification

In practical applications, the impulse responses (IRs) of some network echo paths are blocksparse (BS), while the traditional proportionate and zero attraction algorithms do not consider the prior sparsity of the BS system, so they do not perform well in the block-sparse system identification (BSSI). In addition, most of the current BS filtering algorithms are based on the assumption of Gaussian noise, so the performance will deteriorate seriously in the background of impulse noise. To overcome the shortcoming, we use the mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,1} $ </tex-math></inline-formula> norm of the filter weight vector to fully tap the sparsity of the BS system, and combine the anti impulse noise characteristic of the M-estimate function to design and derive the BS proportionate normalized least mean M-estimate (BSPNLMM) algorithm from the perspective of basis pursuit (BP), which well realizes the BSSI in the presence of impulse noise. Then, we analyze the mean performance of the BSPNLMM algorithm in detail and give the stable step size bound. Finally, the superiority of the proposed BSPNLMM algorithm is verified by numerical simulations

A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise

This paper proposes a nonconvex model (called LogTVSCAD) for deblurring images with impulsive noises, using the log-function penalty as the regularizer and adopting the smoothly clipped absolute deviation (SCAD) function as the data-fitting term. The proposed nonconvex model can effectively overcome the poor performance of the classical TVL1 model for high-level impulsive noise. A difference of convex functions algorithm (DCA) is proposed to solve the nonconvex model. For the model subproblem, we consider the alternating direction method of multipliers (ADMM) algorithm to solve it. The global convergence is discussed based on Kurdyka–Lojasiewicz. Experimental results show the advantages of the proposed nonconvex model over existing models.