This paper develops an efficient adaptive filtering algorithm for active impulsive noise control (AINC) systems. For AINC systems, the filtered-x least mean square (FxLMS) algorithm fails to converge due to the impulsive nature of the noise source. In previous work, the step-size of the FxLMS algorithm was normalized using the power estimate of the error as well as the reference signals, resulting in the improved normalized step-size FxLMS (INSS-FxLMS) algorithm. The INSS-FxLMS algorithm exhibits a robust performance for AINC systems; however, it uses a preselected fixed step-size. Therefore, the INSS-FxLMS algorithm results in a compromise between convergence speed and noise reduction. The proposed algorithm employs a convex-combined step-size (CCSS) within the framework of the INSS-FxLMS algorithm. While normalization takes care of the impulsive nature of noise, the CCSS solves the above-mentioned trade-off issue. Essentially, the CCSS selects a large (small) value of the step-size in the transient (steady) state of the AINC system. It is demonstrated by extensive computer simulations that the proposed algorithm outperforms the existing counterparts for a variety of case studies in AINC systems.

中文翻译：

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关于主动脉冲噪声控制 (AINC) 系统

本文为主动脉冲噪声控制 (AINC) 系统开发了一种高效的自适应滤波算法。对于 AINC 系统，由于噪声源的脉冲性质，filtered-x 最小均方 (FxLMS) 算法无法收敛。在之前的工作中，FxLMS 算法的步长使用误差的功率估计以及参考信号进行了归一化，从而产生了改进的归一化步长 FxLMS (INSS-FxLMS) 算法。INSS-FxLMS 算法对 AINC 系统表现出强大的性能；但是，它使用预选的固定步长。因此，INSS-FxLMS 算法导致收敛速度和降噪之间的折衷。所提出的算法在INSS-FxLMS 算法的框架内采用凸组合步长(CCSS)。虽然归一化处理噪声的脉冲特性，但 CCSS 解决了上述权衡问题。本质上，CCSS 在 AINC 系统的瞬态（稳定）状态中选择一个大（小）步长值。大量计算机模拟表明，在 AINC 系统中的各种案例研究中，所提出的算法优于现有算法。