雷达脉内调制识别的改进残差神经网络算法

The artificially extracted features are computationally intensive and subjective, fail to fully reflect the nature of the signal, and take too long to generate time-frequency images. To overcome these problems, We propose an improved residual neural network (ResNet) ResNet32 as a framework to extract and identify radar time-domain signal features. We build a time-domain signal dataset of 9 types of intra-pulse signals and input them into the ResNet32 framework for training and classification. The algorithm saves a lot of time to generate time-frequency images, and the experimental verification algorithm has a better recognition rate at low signal to noise ratio (SNR). In the experimental conditions of mixed SNR, the recognition rate of the 9 modulation types with SNR=-14 dB and SNR=-8 dB achieved more than 90%.