Impulsive Noise Environment Research Articles

Delayless Individual-Weighting-Factors Sign Subband Adaptive Filter With Band-Dependent Variable Step-Sizes

Sign subband adaptive filter algorithms with individual weighting factors (IWF-SSAF) were recently proposed to achieve improved convergence performance in impulsive noise environments by fully utilizing the decorrelating property of subband adaptive filters. However, such sign subband adaptive filter (SSAF) algorithms may have an inherent signal path delay problem for real-time applications, and thus, can be restrictively applied. In this paper, a delayless IWF-SSAF with band-dependent variable step-sizes (BD-VSS), robust in impulsive noise environments, is proposed for real-time applications, whereby two delayless filter structures developed for the $\ell _2$ -norm-based SAF are employed along with the IWF-SSAF. In particular, a BD-VSS algorithm is also introduced for better convergence by applying the recent $\ell _1$ -norm minimization technique to respective subband. Finally, the performance of the proposed delayless IWF-SSAF with BD-VSS is verified in various impulsive interference environments such as system identification with impulsive noise, acoustic echo cancellation in a double-talk scenario, and active impulsive noise control.

A Variable Step-Size Normalized Subband Adaptive Filter With a Step-Size Scaler Against Impulsive Measurement Noise

This brief introduces a variable step-size (VSS) normalized subband adaptive filter (NSAF) using a step-size scaler to improve the robustness against impulsive measurement noise. When impulsive measurement noise appears, the step size of the proposed VSS NSAF is scaled down by the step-size scaler, which is suitable for application in the NSAF. This removes a possibility of updating weight estimates based on defective information of the subband output errors due to impulsive measurement noise. In the proposed VSS NSAF, the equations for updating the step size are constructed by interpreting the behavior of the mean square deviation of the conventional NSAF and applying the step-size scaler. The step-size scaler utilizes the sum of the subband output errors, which can be influenced by impulsive measurement noise. Simulations using the proposed VSS NSAF show an excellent transient and steady-state behavior with colored input in impulsive-noise environments.

Robust proportionate adaptive filter based on maximum correntropy criterion for sparse system identification in impulsive noise environments

Proportionate-type adaptive filtering (PtAF) algorithms have been successfully applied to sparse system identification. The major drawback of the traditional PtAF algorithms based on the mean square error (MSE) criterion show poor robustness in the presence of impulsive noises or abrupt changes because MSE is only valid and rational under Gaussian assumption. However, this assumption is not satisfied in most real-world applications. To improve its robustness under non-Gaussian environments, we incorporate the maximum correntropy criterion (MCC) into the update equation of the PtAF to develop proportionate MCC (PMCC) algorithm. The mean and mean square convergence performance analysis are also performed. Simulation results in sparse system identification and echo cancellation applications are presented, which demonstrate that the proposed PMCC exhibits outstanding performance under the impulsive noise environments.

Lorentzian Based Adaptive Filters for Impulsive Noise Environments

In this paper, three Lorentzian based robust adaptive algorithms are proposed for identifying systems in presence of impulsive noise. The first algorithm called Lorentzian adaptive filtering (LAF) is derived from a sliding window type cost function with Lorentzian norm of past errors to combat adverse effect of impulsive noise on systems. The first and second order convergence analyses of the LAF algorithm are carried out in this paper. Then, to identify sparse systems in impulsive noise environment, $l_{0}$ norm penalty is introduced to the cost function of the LAF algorithm leading to a new algorithm called Lorentzian hard thresholding adaptive filtering (LHTAF) which employs hard thresholding operator with a fixed hard thresholding parameter to obtain sparse solutions. The effect of the hard thresholding operator is further analyzed, and the analysis shows that a variable hard thresholding parameter offers significant improvement in the performance of the algorithm, and this result in the final algorithm called Lorentzian variable hard thresholding adaptive filtering (LVHTAF) where the hard thresholding parameter is adjusted adaptively. Simulation results show that the LVTHAF outperforms the existing robust sparse adaptive algorithms by producing lesser steady state mean square error.

The Fractional Lower Order Moments Based ESPRIT Algorithm for Noncircular Signals in Impulsive Noise Environments

By applying the covariation statistics, this paper generalizes the non-circularity to the scenario of complex isotropic symmetric alpha-stable (\( S\alpha S \)) signals. The matrices based on fractional lower order moments for the extended sensor array outputs, which have been shown to have similar forms to the covariance matrix for the Gaussian distributed signals and noise, are formulated for the case of complex \( S\alpha S \) signals and noise. Therefore, the similarity transformation based estimating signal parameter via rotational invariance techniques can be applied to these matrices resulting to the improved direction of arrival estimates for noncircular signals in the presence of impulsive noise environments. The robustness of our proposed algorithm, especially for low generalized signal to noise ratio situations, and for quite highly impulsive noise environments is demonstrated by Monte-Carlo simulations.

Subspace-based Localization Approach for Near-field Source in Impulse Noise Environment

In this paper, we propose a subspace-based matching (SBM) approach based on fractional lower order moment (FLOM) matrix to estimate the location of near-field acoustic signals in additive nonGaussian impulsive noise environment. By matching the subspace in the fingerprint database and the subspace computed from the current received signals, we associate the received signal with its transmission location with high accuracy. The proposed algorithm has two advantages: firstly, it can locate multiple sources without estimating bearing and range of sources. Secondly, it is more robust to multipath, environment changing and noise level. The simulation results show the efficacy of our proposed algorithm.

Watermark Detection in Impulsive Noise Environment Based on the Compressive Sensing Reconstruction

Watermark Detection in Impulsive Noise Environment Based on the Compressive Sensing Reconstruction

영오차 확률 기반 알고리즘의 입력 정력 정규화

충격성 잡음 환경에서 최대 영확률 (MZEP) 알고리듬은 최소자승오차 (MSE) 기반의 알고리듬 보다 우수한 성능을 지닌다. 그리고 알고리듬 자체에 내재한 크기 조절 입력 (MCI)가 MZEP 알고리듬을 충격성 잡음으로부터 알고리듬을 안정되게 유지하는 역할을 하는 것으로 알려져 있다. 이 논문에서는 MCI 입력의 평균전력으로 MZEP 알고리듬의 스텝 사이즈를 정규화하는 방식을 제안하였다. 충격파 발생률이 0.03인 충격성 잡음하의 시뮬레이션에서 정상상태 MSE 성능 비교에서 기존 MZEP에 비해 제안한 방식이 약 2dB 정도 향상된 특성을 보인다. The maximum zero error probability (MZEP) algorithm outperforms MSE (mean squared error)-based algorithms in impulsive noise environment. The magnitude controlled input (MCI) which is inherent in that algorithm is known to plays the role in keeping the algorithm undisturbed from impulsive noise. In this paper, a new approach to normalize the step size of the MZEP with average power of the MCI is proposed. In the simulation under impulsive noise with the impulse incident rate of 0.03, the performance enhancement in steady state MSE of the proposed algorithm, compared to the MZEP, is shown to be by about 2 dB.

Robust Detection Method of Arrival Time Difference Under Minimum Maximum Entropy Criterion

Accurate measurement of arrival time difference is one of the key technologies in many areas, such as the global positioning system. Due to the effects of environmental noises around receiver, the classic methods under least mean-square error rule are the lack of robustness. In this paper, a robust method of the time difference detection is addressed based on the minimum maximum entropy, referred to MMEATD. The maximum entropy function used in this method is a smooth approximation of the L1 norm. It has robustness to large outliers, but also is differentiable. Under the minimum maximum entropy criterion, the adaptive filter weight vector will be convergence, and its peak position indicates the arrival time difference. The computer simulation experiments show the estimation performance of this algorithm under different signal and noise ratio or different impulsive noise intension. Meanwhile, its estimation performance is compared with minimum mean square error algorithm. Results show that the proposed method has a good robustness under the impulsive noise environment.

Robust Shrinkage Normalized Sign Algorithm in an Impulsive Noise Environment

In this brief, a robust shrinkage normalized sign (RSHNS) algorithm is presented, which uses an optimal step size by minimizing the energy of the noise-free a posteriori error signal and using Price's theorem. To reduce the impact of impulsive noise, the noise-free error signal is estimated by using the robust shrinkage method. It uses two threshold parameters as compared to that in the conventional shrinkage method. Finally, the improved convergence performance of the proposed RSHNS algorithm is demonstrated through simulation results in system identification and echo-cancellation applications.

Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments

Time delay estimation (TDE) is applied in many areas. Its estimation performance plays an important role in many actual systems, such as malfunction sound location. In this paper, estimation performances of three TDE algorithms, correlation, covariance, and fractional lower order covariance, are compared. Traditional, additive noises in the actual collected signals are described by Gaussian distribution. However, they have often impulsiveness in practice, and are modeled as α-stable distribution. First, correlation, covariance, and fractional lower order covariance method are analyzed in theory. Then, computer simulation experiments are carried out. Computer sound card records pure audio signals, different pulse intensity noises added to simulate actual environments. Next, results of three algorithms for time delay estimation were obtained in different signal to noise ratio (SNR) conditions. Under the same conditions, estimated RMS (root-mean-square) errors of three algorithms are analyzed and compared. Experimental results show that under low SNR and strong impulsive noise environments, fractional lower order covariance method indicates best estimation performance.

Robust Cyclic MUSIC Algorithm for Finding Directions in Impulsive Noise Environment

This paper addresses the issue of direction finding of a cyclostationary signal under impulsive noise environments modeled byα-stable distribution. Sinceα-stable distribution does not have finite second-order statistics, the conventional cyclic correlation-based signal-selective direction finding algorithms do not work effectively. To resolve this problem, we define two robust cyclic correlation functions which are derived from robust statistics property of the correntropy and the nonlinear transformation, respectively. The MUSIC algorithm with the robust cyclic correlation matrix of the received signals of arrays is then used to estimate the direction of cyclostationary signal in the presence of impulsive noise. The computer simulation results demonstrate that the two proposed robust cyclic correlation-based algorithms outperform the conventional cyclic correlation and the fractional lower order cyclic correlation based methods.

Recursive Generalized Maximum Correntropy Criterion Algorithm with Sparse Penalty Constraints for System Identification

AbstractTo address the sparse system identification problem in a non‐Gaussian impulsive noise environment, the recursive generalized maximum correntropy criterion (RGMCC) algorithm with sparse penalty constraints is proposed to combat impulsive‐inducing instability. Specifically, a recursive algorithm based on the generalized correntropy with a forgetting factor of error is developed to improve the performance of the sparsity aware maximum correntropy criterion algorithms by achieving a robust steady‐state error. Considering an unknown sparse system, the l1‐norm and correntropy induced metric are employed in the RGMCC algorithm to exploit sparsity as well as to mitigate impulsive noise simultaneously. Numerical simulations are given to show that the proposed algorithm is robust while providing robust steady‐state estimation performance.

A robust correntropy based subspace tracking algorithm in impulsive noise environments

The maximum correntropy criterion (MCC) demonstrates the inherent robustness to outliers in adaptive filtering. By employing the MCC based cost function in projection approximation subspace tracking (PAST) algorithm, the MCC-PAST algorithm is deduced and utilized for the subspace tracking under impulsive noise environments. To handle the fast varying subspaces circumstances, the variable forgetting factor (VFF) technique is developed and incorporated into the MCC-PAST algorithm. To assess the robustness of the proposed MCC-PAST with VFF algorithm, SαS processes are employed to comprehensively model different scenarios of impulsive noises. The simulation results show the proposed MCC-PAST algorithm with VFF performs better than the other two PAST algorithms developed for subspace tracking in impulsive noise environments, namely, the robust PAST algorithm and the robust Kalman filter based algorithm with variable number of measurements (KFVNM), especially when the noise is extremely impulsive or the GSNR (generalized signal to noise ratio) is relatively low.

Robust Variable Step-Size Decorrelation Normalized Least-Mean-Square Algorithm and its Application to Acoustic Echo Cancellation

In this paper, we present a robust variable step-size decorrelation normalized least-mean-square (RVSSDNLMS) algorithm. A new constrained minimization problem is developed by minimizing the l 2 norm of the a decorrelated posteriori error signal with a constraint on the filter coefficients in the l 2 norm sense. Solving this minimization problem gives birth to the efficient RVSSDNLMS algorithm. The convergence performance and computational complexity of RVSSDNLMS algorithm are analyzed. Finally, simulations show that the proposed RVSSDNLMS considerably outperforms the normalized least-mean-square (NLMS), robust variable step-size NLMS, and pseudoaffine projection algorithms in terms of convergence rate and steady-state error in Gaussian noise and impulsive noise environments.

Adaptive combination of affine projection sign subband adaptive filters for modeling of acoustic paths in impulsive noise environments

The affine projection sign subband adaptive filter (APSSAF) algorithm has attracted much attention because of its fast convergence rate and robustness against impulsive interference. However, a drawback of this algorithm is that the step size implies a compromise between the convergence speed and steady-state error. To solve this problem, a new adaptive combination of the APSSAFs based on convex combination scheme is proposed for modeling of acoustic paths under impulsive noise environments. Moreover, a weight transfer approach is applied to further improve the performance. Simulation results demonstrate that the proposed algorithm achieves improved performance than existing algorithms when applied to modeling sparse/dispersive systems.

Cyclostationary correntropy: Definition and applications

Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and the Spectral Cyclic Density (SCD) can be used to extract second-order cyclostationary information. However,second-order statistics tightly depends on the assumption of gaussianity, as the cyclostationary analysis in this case should comprise higher-order statistical information. This paper proposes a new mathematical formulation for the higher-order cyclostationary analysis based on the correntropy function. The cyclostationary analysis is revisited focusing on the information theory, while the Cyclic Correntropy Function (CCF) and Cyclic Correntropy Spectral Density (CCSD) are also presented. The CCF has different properties compared with CAF that can be very useful in non-gaussian signal processing, especially in the impulsive noise environment which implies in the expansion of the class of problems addressed by the second-order cyclostationary analysis. In particular, we prove that the CCF contains information regarding second- and higher-order cyclostationary moments, being a generalization of the CAF. The performance of the aforementioned functions in the extraction of higher-order cyclostationary characteristics is analyzed in a wireless communication system in which non-gaussian noise is present. The results demonstrate the advantages of the proposed method over the second-order cyclostationary.

A robust DOA estimator based on the correntropy in alpha-stable noise environments

By introducing correntropy as the robust statistics, a novel direction of arrival estimator for α-stable noise is proposed. In this method, the signal subspace is estimated by solving the correntropy based optimization problem under the maximum correntropy criterion. An optimal step size based iterative algorithm is developed and the convergence of it is proved. Comprehensive simulation results demonstrate that the proposed method is superior to several existing algorithms in terms of the probability of resolution and the estimation accuracy, especially in the highly impulsive noise environments.

Diffusion Sign Subband Adaptive Filtering Algorithm with Individual Weighting Factors for Distributed Estimation

A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price's theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts.

Improvements in PLC transmissions via difference sets schemes with compressive sampling

The paper deals with the damaging effects produced by impulsive noise in PLC transmissions and proposes a cancellation scheme using difference-set-based compressive sensing.Authors focus on a popular narrow-band PLC solution to evaluate its performance: PRIME. A simulation environment based on Matlab was developed to analyze PRIME’s behavior in an impulsive noise environment. To model the transmission channel, Middleton’s Class-A noise model was used in conjunction with noise parameters reported in the literature for the narrow-banded spectrum. The performance is measured in terms of bit error rate versus signal to noise ratio.Although the use of compressive sensing to cancel impulsive noise in communications has already been proposed in other studies, this paper details a modification based on Partial Fourier Matrix indexing according to difference sets. Results from simulations report an almost complete cancellation of the impulsive noise effects. The analysis is done studying the achieved bit error rate as a function of the signal to noise ratio at the receiver.