这种方法将一个复杂的非线性非平稳信号逐级分解成若干平稳的数据层与剩余的最终趋势项的叠加。
This method will gradually decompose a complex nonlinear and non-stationary signal into some stable data layers and the remaining final trend.
对相关函数进行时域滤波，挑选出互相关函数中窄信号自相关函数部分，去掉剩余的大部分噪声能量，得到加窗互相关函数（CCF）。
The time-domain filter can then be used to select the narrow central portion of the cross correlation function (CCF), capturing virtually all of the signal but rejecting most of the noise.
提出了一种自适应变步长恒模盲均衡算法，利用剩余误差信号的自相关函数估计值作为控制步长的因子来自适应改变步长的大小，克服了恒模算法存在的固有缺陷。
A new variable step-size CMA blind equalization algorithm is introduced to conquer the defects of CMA, in which the step size is controlled by the estimation of error signal's autocorrelation.
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