Yingying Zhu

For active control of impulsive noise, the conventional filtered-x least mean square (FxLMS) algorithm has poor noise reduction performance. To address this issue, this paper designs a robust cost function by embedding the cost function of the FxLMS algorithm into the framework of the adjustable Softsign function, thereby designing a robust Softsign-FxLMS (SFxLMS) algorithm for ANC systems. Furthermore, the parameter λ of the SFxLMS algorithm significantly influences its robustness and convergence speed. Therefore, a variable λ-parameter SFxLMS (VSFxLMS) algorithm is designed to improve the performance of the ANC system. Simulation studies indicate that the proposed SFxLMS algorithm and VSFxLMS algorithm exhibit stronger robustness, faster convergence rates, and better tracking performance compared to several robust FxLMS algorithms. Moreover, the symmetric properties of the proposed Softsign function contribute to balanced error suppression in both positive and negative directions, enhancing the robustness and stability of the ANC system under asymmetric impulsive noise conditions.

System modeling in distributed adaptive networks remains challenging under sensor aging-induced nonlinear data censoring and non-Gaussian interference. This brief proposes a robust hierarchical control framework that incorporates a Laguerre-based random Fourier filter to capture network nonlinearities. To address censored measurements and impulsive noise, a robust Laguerre RFF Diffusion Hierarchical (LRFF-DH) algorithm is developed. It leverages maximum likelihood estimation to correct bias and introduces a connectivity-adaptive robustness strategy based on node connectivity, enhancing edge-node resilience while reducing computational load on critical nodes. Simulations on a 20-node ad-hoc network verify the algorithm's effectiveness in interference suppression and mean squared error reduction under complex nonlinear conditions.

In this work, we present a subband adaptive filtering (SAF) algorithm with good robustness for impulsive noise environments. The proposed squared sine normalized SAF (SS-NSAF) algorithm employs a squared sine function and is inspired by the idea of normalization. A statistical analysis of the proposed SS-NSAF algorithm is carried out under impulsive noise environments, which includes stability conditions for the convergence and analytical formulas to predict the mean-square deviation of the SS-NSAF algorithm. Numerical simulations for system-identification and echo cancellation applications are conducted to illustrate the performance of the SS-NSAF algorithm and competing techniques in impulsive noise environments.

For accurate Direction of Arrival (DOA) estimation in challenging high reverberation and low signal-to-noise ratio (SNR) scenarios, various deep learning (DL) techniques have been developed to incorporate and enhance existing algorithms. However, addressing the challenges of enhancing network manipulability, constructing efficient learning features, and integrating algorithms in a rational manner remains a set of significant hurdles. In this paper, we use DL to obtain the Speech Presence Probability (SPP) to construct the optimal statistics estimates, which are then combined with traditional algorithms to achieve accurate DOA estimation. Specifically, we explore the application of the a posteriori SPP in DOA estimation, design a reverberation separation model for practical scenarios, and derive and validate a new computational equation for SPP under this model. Besides, we propose a frequency bin-wise network structure to improve network fitting efficiency and construct input features accordingly. Moreover, by adopting a combined structure, we avoid full-angle network feature training and instead train on partial angles under deliberate subset classification. We then evaluate the DOA estimation performance for the entire direction range with fine resolution using this approach. Simulation results demonstrate that the proposed method requires smaller data sets compared to end-to-end deep learning algorithms. Furthermore, the results validate that the proposed method outperforms both DL-based end-to-end approaches and traditional full-band approaches in terms of accuracy and error rate across various reverberation and signal-to-noise ratio conditions.

The conventional Minimum Error Entropy criterion (MEE) has its limitations, showing reduced sensitivity to error mean values and uncertainty regarding error probability density function locations. To overcome this, a MEE with fiducial points criterion (MEEF), was presented. However, the efficacy of the MEEF is not consistent due to its reliance on a fixed Gaussian kernel. In this paper, a generalized minimum error with fiducial points criterion (GMEEF) is presented by adopting the Generalized Gaussian Density (GGD) function as kernel. The GGD extends the Gaussian distribution by introducing a shape parameter that provides more control over the tail behavior and peakedness. In addition, due to the high computational complexity of GMEEF criterion, the quantized idea is introduced to notably lower the computational load of the GMEEF-type algorithm. Finally, the proposed criterions are introduced to the domains of adaptive filter, kernel recursive algorithm, and multilayer perceptron. Several numerical simulations, which contain system identification, acoustic echo cancellation, times series prediction, and supervised classification, indicate that the novel algorithms' performance performs excellently.