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摘要: 为了提高宽窄带混合噪声的消噪效果，本文提出一种基于总体平均经验模态分解（Ensemble empirical mode decomposition，EEMD）的主动噪声控制（Active noise control，ANC）系统，利用实时EEMD算法逐段将混合噪声分解成若干个固有模态函数（Intrinsic mode functions，IMF）分量.因为这些IMF分量的频带各不相同，所以实现了混合噪声中宽带分量和窄带分量的有效分离，独立进行ANC处理后成功解决了处理混合噪声时带来的“火花”现象，而且避免了传统混合ANC（Hybrid ANC，HANC）系统中频率失调的影响. EEMD算法也是对混合噪声的平稳化处理过程，因此当混合噪声中出现非平稳变化时，本文提出的系统也能保持较好的系统稳定性.通过不同噪声环境下进行仿真分析，提出的ANC系统比HANC系统具有更好的系统稳定性和更小的稳态误差.
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关键词:
- 混合噪声 /
- 主动噪声控制 /
- 总体平均经验模态分解 /
- 固有模态函数 /
- 非平稳变化
Abstract: In order to obtain a better de-noising performance of mixture noise containing both wideband components and narrowband components, a new active noise control (ANC) system based on ensemble empirical mode decomposition (EEMD) is proposed in this paper. Real-time EEMD algorithm is used to decompose the mixture noise into several intrinsic mode functions (IMF) which have a different frequency range each other, so this decomposition can separate wideband components and narrowband components from the mixture noise adaptively. Each component controlled independently can not only process mixture noise without "firework", but also avoid the frequency mismatch occurring in conventional hybrid ANC (HANC) system. The EEMD algorithm can smooth the mixture noise to make sure the proposed system has better stability when non-stationary phenomenon happens in mixture noise. Compared with HANC system, the proposed ANC system has better system stability and smaller steady-state error in processing different noise. -
图 4 平稳宽窄带混合噪声系统性能对比
Fig. 4 Comparisons between systems$'$ performance for stationary mixture noise of wideband and narrowband
图 5 非平稳宽窄带混合噪声的系统性能对比
Fig. 5 Comparisons between systems$'$ performance for non-stationary mixture noise of wideband and narrowband
图 9 平稳宽窄带混合噪声的EEMD时域图
Fig. 9 Time domain EEMD view of stationary mixture noise of wideband and narrowband
图 10 平稳宽窄带混合噪声的EEMD频域图
Fig. 10 Frequency domain EEMD view of stationary mixture noise of wideband and narrowband
图 11 非平稳宽窄带混合噪声的EEMD图
Fig. 11 EEMD view of non-stationary mixture noise of wideband and narrowband
图 12 混合噪声和EEMD后各分量的Hilbert谱
Fig. 12 Hilbert spectrum of the mixture noise and the components after EEMD
表 1 次级路径估计参数设置
Table 1 Parameters of secondary path estimation
参数名称 参数值 初级路径$P(z)$的长度 ${\rm{40 (}}L{\rm{ = 41)}}$ 次级路径$S(z)$的长度 ${\rm{20 (}}M{\rm{ = 21)}}$ 次级路径估计${\hat S}(z)$的长度 ${\rm{30 ( }}\hat M{\rm{ = 31)}}$ 计算步长 0.001 训练输入信号的方差 $\sigma _{xs}^2(n)= 1$ 训练观测噪声的方差 $\sigma _{vs}^2(n)= 0.1$ 训练次数 10 000 
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