基于卷积神经网络的超短波特定信号谱图识别

针对超短波通信中特定信号的识别问题,提出一种将时频谱图和卷积神经网络相结合的超短波特定信号识别方法。该方法首先对特定信号进行短时傅里叶变换得到时频谱图,然后使用时频谱图对改进的卷积神经网络模型进行训练,最后测试网络模型,实现超短波特定信号识别。实验结果表明,该方法对特定信号的识别率能达到98%,在信噪比为0 dB时仍能达到97%的识别率,并且在混叠50%时识别率达到了90%。相比传统算法,该方法具有更好的抗低信噪比和抗混叠干扰能力,验证了卷积神经网络在特定信号识别领域的有效性,为该领域的后续研究奠定了基础。

Ultra short wave specific signal spectrogram recognition based on convolution neural network

To correctly identify specific signals in ultra short wave communication, an approach is proposed by using the time frequency spectrogram and the convolution neural network for the ultra short wave specific signal recognition, which transforms the classification of specific signals into image recognition. The time frequency spectrogram of specific signals are obtained by using the short time Fourier transform. Then the time frequency spectrogram is used to train the modified convolution neural network model. Finally the network model is tested to realize the ultra short wave specific signal recognition. Simulation results show that the recognition rate of the proposed approach can reach 98% for a specific signal, the recognition rate can reach 97% when the signal-to-noise ratio (SNR) is 0 dB, and the recognition rate can reach 90% when the aliasing interference is 50%. Compared with traditional algorithms, the proposed approach has a better ability in low SNR and aliasing interference, which verifies the effectiveness of the convolution neural network in the field of specific signal recognition, and lays a foundation for subsequent research in this field.