针对脉冲噪声，提出一种新型降噪自适应滤波算法。
Design and Analysis of Robust Adaptive Filtering with the Hyperbolic Tangent Exponential Kernel M-Estimator Function for Active Noise Control
- 用双曲正切指数核函数设计鲁棒估计器
- 在α稳定噪声下均方误差更低，降噪效果更优
- 适合高干扰环境下的主动降噪系统
本文针对脉冲噪声环境下的主动降噪应用，提出一种基于滤波-x双曲正切指数广义核M估计函数（FXHEKM）的鲁棒自适应滤波方法。对所提FXHEKM算法进行了统计分析，并研究了其计算成本。为评估性能，采用均方误差（MSE）和平均降噪量（ANR）作为评价指标。数值结果表明，该算法在抑制加性脉冲信号（如α-稳定噪声）方面优于现有竞争算法，表现出更高的效率与鲁棒性。
原文摘要 · Abstract (English)
In this work, we propose a robust adaptive filtering approach for active noise control applications in the presence of impulsive noise. In particular, we develop the filtered-x hyperbolic tangent exponential generalized Kernel M-estimate function (FXHEKM) robust adaptive algorithm. A statistical analysis of the proposed FXHEKM algorithm is carried out along with a study of its computational cost. {In order to evaluate the proposed FXHEKM algorithm, the mean-square error (MSE) and the average noise reduction (ANR) performance metrics have been adopted.} Numerical results show the efficiency of the proposed FXHEKM algorithm to cancel the presence of the additive spurious signals, such as \textbf{$α$}-stable noises against competing algorithms.
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