The Euclidean direction search (EDS) algorithm provides an efficient iterative approach for adaptation without computationally expensive matrix inversion. However, existing EDS-based algorithms are unsuitable for the non-circular complex signals. To address this problem, this paper integrates the widely linear model with the EDS algorithm, proposing the widely linear complex-valued EDS (WL-CEDS) algorithm, which extends the conventional EDS algorithm to the augmented complex statistics domain. Moreover, to confirm the convergence, the Lyapunov stability theory (LST) is used, with the theoretical steady-state complementary behavior analyzed. Simulations validate the correctness of the analysis, and confirm that WL-CEDS achieves significant performance improvement for non-circular signals compared to the conventional widely linear-based algorithms.

The presence of impulsive noise can significantly compromise the localization accuracy of partial discharge (PD). To address this problem, the direct data-reusing time-delay estimation (TDE) algorithm with the generalized Cauchy loss (GCL) function (DDR-TDE-GCL) is proposed. Leveraging the robustness of the GCL function, the proposed algorithm demonstrates a superior capability to combat impulsive interference. In addition, the direct TDE method is integrated into the data-reusing strategy. The integration enables the DDR-TDE-GCL algorithm to achieve high localization accuracy in cable systems while remaining noniterative. Moreover, the convergence behavior of the DDR-TDE-GCL algorithm is analyzed, proving that the estimation for the attenuation factor is unbiased and consistent. Simulations and experiments demonstrate that the DDR-TDE-GCL algorithm has an improved localization accuracy compared to existing algorithms for PD signals, achieving an average localization accuracy of 99.59%.

The iterative Wiener filter (IWF) algorithm can achieve a fast convergence rate. However, its performance may degrade when it encounters noisy input scenarios. To tackle this problem, a novel IWF algorithm incorporating bias-compensation (BC-IWF) is proposed, which can enhance the performance of the algorithm by estimating the input noise variance. The BC-IWF algorithm optimizes the step size for each iteration and updates along the direction of the gradient. To further reduce the steady-state error, a normalized IWF by making use of the bias-compensation scheme (BC-NIWF) algorithm is proposed. Moreover, the steady-state performance of the BC-NIWF algorithm is analyzed. Simulation results demonstrate the validity of the theoretical analysis and the BC-NIWF algorithm achieves improved misadjustment compared with the state-of-the-art algorithms.

Based on the total least-squares (TLS) model, the gradient-descent TLS Euclidean direction search (GD-TLS-EDS) algorithm is proposed when both input and output signals are corrupted by noises. Taking advantage of the effectiveness of the EDS algorithm, the GD-TLS-EDS algorithm has improved performance and comparable computational complexity. Moreover, based on the time difference of arrival technique, the traditional location methods may suffer from performance degradation in the location problem with noisy inputs, the GD-TLS-EDS algorithm is exploited to mitigate the noise from the original and reflected partial discharge signals and achieve the one-step location result by estimating the time difference in cable systems. Simulation and experimental studies demonstrate the GD-TLS-EDS algorithm has improved location accuracy as compared with the existing algorithms.

The Euclidean direction search (EDS) algorithm can reduce the complexity by avoiding the matrix inversion operation. However, it may fail to work in impulsive environments. To address this problem, a novel EDS based upon the maximum correntropy criterion (EDS-MCC) algorithm is proposed, which provides computational savings and robustness for combating impulsive noise. Additionally, the EDS-MCC algorithm is analyzed to obtain the theoretical performance by utilizing the energy conservation argument (ECA) and the Taylor expansion method. Simulations are exhibited to show the robustness of the EDS-MCC algorithm and verify the accuracy of the theoretical analysis.

Partial discharge (PD) location techniques are a useful tool for condition monitoring of electrical apparatus in power systems. However, the noisy PD measurements may significantly degrade the performance of location algorithms. This article deals with the PD location problem by using adaptive filtering techniques. Heretofore, scarce literature focuses on addressing the PD location based on such method. A novel adaptive algorithm, termed as total least-squares (TLS)-Matérn kernel (TLS-MK), is proposed. Benefiting from the merits of the Matérn kernel, the TLS model can effectively suppress the noise from the direct and reflected waves of the PD source. Meanwhile, the TLS-MK algorithm is used to estimate the time difference, which is used in the PD location. Moreover, the convergence behavior of the TLS-MK algorithm is analyzed. Simulations and experiments show that the proposed algorithm can enhance the location accuracy as compared to state-of-the-art methods for various PD signals.

The partial discharge (PD) classification for electric power equipment based on machine learning algorithms often leads to insufficient generalization ability and low recognition accuracy. To solve the problem, this article develops an improved Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based data augmentation model. The improved WGAN-GP model can generate data samples to supplement the low-data input set in PD source classification. First, an improved WGAN-GP model with conditional generation is trained and various new data samples are generated. Then, the new data samples are utilized to expand the raw dataset. Finally, the expanded dataset is trained to get a new PD classifier. Experimental results demonstrate that the proposed model can generate new high-quality data samples more stably. Moreover, the proposed method can suppress the overfitting risk caused by low data or imbalanced data distributions and the classification accuracy is effectively improved.

We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey’s biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments. In particular, the TbMCG algorithm can achieve a faster convergence while retaining a reduced computational complexity as compared to the recursive least-squares (RLS) algorithm. Specifically, the Tukey’s biweight M-estimate incorporates a constraint into the CG filter to tackle impulsive noise environments. Moreover, the convergence behavior of the TbMCG algorithm is analyzed. Simulation results confirm the excellent performance of the proposed TbMCG algorithm for system identification and active noise control applications.

This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive noise. The proposed algorithm generalizes multiple algorithms only by replacing the specified criterion of robustnessand sparsity-aware penalty. Furthermore, by jointly optimizing the forgetting factor and the sparsity penalty parameter, we develop the jointly-optimized S-RRLS (JO-S-RRLS) algorithm, which not only exhibits low misadjustment but also can track well sudden changes of a sparse system. Simulations in impulsive noise scenarios demonstrate that the proposed S-RRLS and JO-S-RRLS algorithms outperform existing techniques.

In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forward-backward splitting and the soft-thresholding methods. We analyze the mean and mean square behaviors of the algorithm, which is supported by simulations. In addition, an adaptive approach for the choice of the thresholding parameter in the proximal step is also proposed based on the minimization of the mean square deviation. Simulations in the contexts of system identification and acoustic echo cancellation verify the superiority of the proposed algorithm over its counterparts.

Active noise control (ANC) is gaining attention for attenuating noise from a remote location. Considering the problem of nonlinear active noise control (NLANC) at a virtual location, a robust filtered-s subband adaptive filtering algorithm based on the q-gradient maximum correntropy criterion (RFsSAF-qMCC) is proposed in this paper. The proposed RFsSAF-qMCC algorithm develops the functional link artificial neural network (FLANN)-SAF structure as the controller, and embeds the MCC with the concept of q-gradient, thereby improving the convergence speed in the impulsive environment. To solve the trade-off between fast convergence and low noise residue caused by the fixed q-gradient, a variable q-gradient algorithm, termed as RFsSAF-vqMCC, is further developed. As an additional contribution, the convergence behavior of the proposed RFsSAF-qMCC and RFsSAF-vqMCC algorithms is analyzed. Simulation results corroborate the effectiveness of the proposed algorithms as compared to state-of-the-art algorithms.

Summary Medical image fusion is a technology of combining multi‐modal images to generate a composite image, which is favorable to improve the capability of doctors in diagnosis and treatment of the disease. In order to achieve good performance, a fusion method by combining Laplacian pyramid (LP) and convolutional sparse representation (CSR) is proposed. In the proposed fusion method, LP transform is performed on each pair of pre‐registered computed tomography image and magnetic resonance image to obtain their detail layers and base layer. Then, the base layer is fused with a CSR‐based approach, whereas the detail layers are merged using the popular “max‐absolute” rule. Finally, the fused image is reconstructed by performing the inverse LP transform over the fused base layer and detail layers. The advantages of our method are that the texture detail information contained in source images can be fully extracted and the overall contrast of the final fused image will not be decreased. Experimental results demonstrate the superiority of the proposed method.

The Internet of Vehicles (IoV) plays a central role in intelligent transportation systems. Components, such as motor and transmission in the vehicle may produce noise, which seriously affects comfort. Therefore, vehicle manufacturers attach great importance to active noise control (ANC) technology. However, such an ANC system may have some nonlinear distortions in practical, thereby the nonlinear ANC (NANC) system is warranted. Moreover, we consider using IoV for rational resource allocation and record historical data for fault diagnosis, early warning, etc. So far, no work on NANC in the IoV environment is reported. In this article, based on the Hermite polynomial, a class of functional link artificial neural network (FLANN) algorithms is developed for NANC. The first proposed algorithm, called filtered-h least mean ${\mathcal {L}}_{p}$ -norm (FhLMP), incorporates the ${\mathcal {L}}_{p}$ -norm to obtain reliable performance. To further enhance the performance, the recursive FhLMP (RFhLMP) and hyperbolic recursive FhLMP (HRFhLMP) algorithms are designed by formulating two recursive structures. The proposed RFhLMP algorithm takes the filter output as part of the input and is expanded by the Hermite FLANN. The HRFhLMP algorithm activates the output by a hyperbolic tangent function and then recursively returns the activated output to the filter input. Simulations verify the improvement of the proposed algorithms for the NANC system.

This article studies the mean and mean-square behaviors of the M-estimate based normalized subband adaptive filter algorithm (M-NSAF) with robustness against impulsive noise. Based on the contaminated-Gaussian noise model, the stability condition, transient and steady-state results of the algorithm are formulated analytically. These analysis results help us to better understand the M-NSAF performance in impulsive noise. To further obtain fast convergence and low steady-state estimation error, we derive a variable step size (VSS) M-NSAF algorithm. This VSS scheme is also generalized to the proportionate M-NSAF variant for sparse systems. Computer simulations on the system identification in impulsive noise and the acoustic echo cancellation with double-talk are performed to demonstrate our theoretical analysis and the effectiveness of the proposed algorithms.

In this letter, a novel diffusion Volterra (DV) algorithm is proposed for distributed in-network system identification in the presence of α-stable noise. The proposed algorithm is based on the logarithmic least mean pth-power criterion, which makes it robust against impulsive interferences, at the price of increased complexity. To overcome this shortcoming, we further develop the diffusion interpolated Volterra algorithm, which provides computational savings and good performance in comparison with the DV algorithm. Simulations results show that the proposed adaptive algorithms achieve better performance than the state-of-the-art approaches for distributed nonlinear system identification in impulsive noise.