解耦(概率)
稳健性(进化)
趋同(经济学)
理论(学习稳定性)
噪音(视频)
频域
计算机科学
算法
主动噪声控制
控制理论(社会学)
控制(管理)
工程类
人工智能
降噪
控制工程
机器学习
计算机视觉
化学
经济
图像(数学)
基因
生物化学
经济增长
作者
Siyuan Lian,Jincheng Gu,Shuping Wang,Kai Chen,Yuxiang Hu,Changbao Zhu,Jing Lü
摘要
Active road noise control (ARNC) is an effective method for mitigating low-frequency noise in vehicle cabins. Previous work introduced a decoupling-whitening frequency domain filtered-error least mean square (DWFDFeLMS) algorithm, which exhibits rapid convergence characteristics for steady-state environments. However, uncorrelated disturbances significantly impact the convergence performance and stability of adaptive algorithms in practical applications. In this paper, a coherence-based robust frequency-dependent variable step size method is proposed to dynamically adjust the step size, using the multichannel coherence coefficients between reference signals and error signals for system stability. Additionally, it is combined with the DWFDFeLMS algorithm for fast convergence speed in ARNC systems. The proposed algorithm ensures fast initial convergence, small steady-state error, and resilience to in-cabin interference. The superiority of this algorithm in terms of convergence speed and stability is confirmed through simulations with measured road noise data and in a real-time ANC system in a car cabin.
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