The identification of multi-source signals with time-frequency aliasing is a complex problem in wideband signal reception. The traditional method of first separation and identification especially fails due to the significant separation error under underdetermined conditions when the degree of time-frequency aliasing is high. The single-mode recognition method does not need to be separated first. However, the single-mode features contain less signal information, making it challenging to identify time-frequency aliasing signals accurately. To solve the above problems, this article proposes a time-frequency aliasing signal recognition method based on multi-mode fusion (TRMM). This method uses the U-Net network to extract pixel-by-pixel features of the time-frequency and wave-frequency images and then performs weighted fusion. The multimodal feature scores are used as the classification basis to realize the recognition of the time-frequency aliasing signals. When the SNR is 0 dB, the recognition rate of the four-signal aliasing model can reach more than 97.3%.

中文翻译：

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基于多模态特征融合的时频混叠信号识别

时频混叠多源信号的识别是宽带信号接收中的一个复杂问题。尤其是在时频混叠程度较高的欠定条件下，传统的首次分离识别方法尤其失败。单模识别方法不需要先分离。然而，单模特征包含的信号信息较少，使得准确识别时频混叠信号具有挑战性。针对上述问题，本文提出一种基于多模融合（TRMM）的时频混叠信号识别方法。该方法利用U-Net网络逐像素提取时频和波频图像的特征，然后进行加权融合。以多模态特征得分作为分类依据，实现时频混叠信号的识别。当信噪比为0 dB时，四信号混叠模型的识别率可以达到97.3%以上。