Multichannel multicomponent signals can be decomposed into individual signal components by exploiting the eigendecomposition of the corresponding autocorrelation matrix. Recently, we have shown that such decomposition is possible even in the particularly challenging case of non-stationary components with significantly overlapped supports in their time, frequency, and joint time-frequency domains. Each signal component can be recovered as a linear combination of the eigenvectors of the autocorrelation matrix, by minimizing its time-frequency concentration measure. However, as the local minima of the concentration measure do exist for each signal component and for each combination of signal components, such minimizations can be challenging and numerically demanding, particularly when considering the associated decomposition procedure which should, for each component, iteratively remove the influence of other components. To confront these challenges, we present a multichannel multicomponent nonstationary signal decomposition procedure which exploits a carefully tuned genetic algorithm for the minimization of the concentration measure of eigenvectors, each comprising the linear combination of the overlapped signal components. Concentration measures are calculated in the time-frequency domain. The presented theory is verified by numerical examples.

通过利用相应自相关矩阵的特征分解，可以将多通道多分量信号分解为单独的信号分量。最近，我们已经表明，即使在非平稳组件的时域，频域和联合时频域中具有明显重叠的支持的非平稳组件的特殊挑战性情况下，这种分解也是可能的。通过最小化其时频集中度度量，可以将每个信号分量恢复为自相关矩阵的特征向量的线性组合。但是，由于浓度度量的局部最小值确实存在于每个信号分量和信号分量的每种组合中，因此这种最小化可能是具有挑战性的，并且在数字上要求很高，尤其是在考虑相关联的分解程序时，对于每个组件，迭代地消除其他组件的影响。为了应对这些挑战，我们提出了一种多通道多分量非平稳信号分解程序，该程序利用经过精心调优的遗传算法来最小化特征向量的浓度度量，每个特征向量都包含重叠信号分量的线性组合。在时频域中计算集中度。数值算例验证了所提出的理论。每个包括重叠信号分量的线性组合。在时频域中计算集中度。数值算例验证了所提出的理论。每个包括重叠信号分量的线性组合。在时频域中计算集中度。数值算例验证了所提出的理论。