卷积线性混合模型下的复非高斯信号盲源提取

雷达信号的多径效应导致基于瞬时线性混合模型的盲源分离算法不再适用。为此，提出了一种基于FastICA的复非高斯信号盲源提取方法。该方法将混合系统建模为卷积线性混合模型，使得信号模型中不需要将每个多径信号都看作一个独立的源信号，既节约了接收通道数量，又降低了盲源分离过程的复杂度，利用待提取信号的非高斯性实现高斯背景下复非高斯信源的提取。实验结果表明：在信干比为-30 dB时，所提方法能够快速、有效地处理卷积线性混合模型下复非高斯信源的提取问题，为该场景下的微弱信号提取提供了一种新的方法。

Blind source extraction of complex non-Gaussian signals based on convolution linear mixture model

Due to the multipath effect of the radar signal, the blind source separation algorithm based on the instantaneous linear mixture model is no longer applicable. A blind source extraction method for complex non-Gaussian signals based on the FastICA algorithm is proposed. The mixed system is modeled as a convolutional linear mixture model, so that each multipath signal does not need to be regarded as an independent source signal in the signal model, which not only saves the number of receiving channels, but also reduces the complexity of blind source separation process. The non-Gaussian feature of the signal to be extracted is used to extract complex non-Gaussian sources in Gaussian background. The experimental results show that when the signal to interference ratio is ?30 dB, the proposed method can quickly and effectively deal with the extraction of complex non-Gaussian sources in the convolutional linear mixture model, which provides a new method for weak signal extraction in this scene.