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Radar Signal Intra-Pulse Modulation Recognition Based on Contour Extraction

机译:基于轮廓提取的雷达信号脉冲内调制识别

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Recent evidence suggests that several methods are focused on changing the convolution neural network (CNN) structure to improve the recognition rate of radar signal intra-pulse modulation. As a result, the CNN structure is becoming significantly complex with poor interpretability. Aiming at this problem, we proposed a recognition method based on contour extraction. The signal binary map is replaced by the contour of signal sending to the CNN for recognition. Because the input is limited to the signal contour, CNN only considers the contour feature extraction, without complex structures. Simulations show that the method can effectively identify five kinds of radar signals when the SNR > 2dB. When the SNR > 4dB, the method has a recognition rate of 2.6% higher than the method without contour extraction.
机译:最近的证据表明,若干方法集中在改变卷积神经网络(CNN)结构上以提高雷达信号脉冲内调制的识别率。结果,CNN结构具有显着复杂,可解释性差。针对这个问题,我们提出了一种基于轮廓提取的识别方法。信号二进制图被发送到CNN的信号的轮廓替换为用于识别。因为输入限制为信号轮廓,所以CNN仅考虑轮廓特征提取,而无需复杂的结构。模拟表明,当SNR> 2dB时,该方法可以有效地识别五种雷达信号。当SNR> 4dB时,该方法的识别率高于没有轮廓提取的方法高2.6%。

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