Midcourse Ballistic Target Recognition Based on ResNet18-SVM
In view of the problems of poor robustness and low recognition rate of midcourse ballistic target recognition algorithm based solely on deep learning in small samples,a classification model combining ResNet18 and support vector machine(SVM)was proposed to identify four typical midcourse ballistic targets,including warhead,heavy decoy,light decoy and debris.The time-frequency images of the radar echo signal were used as the input of the residual network,and the target features were automat-ically extracted by the residual unit and imported to the SVM for classification.The recognition model combines the advantages of ResNet's deep receptive field and SVM's good classification ability for high-dimensional feature samples.The experimental radar echo data is obtained by STK ballistic simulation and FEKO target electromagnetic simulation.The comparison experiments based on the narrow-band radar echo signal of the target in the middle of the ballistic trajectory show that the SVM of the combined model improves the recognition rate by 1.9％on average based on the original ResNet18;Under different Signal Noise Ratios,the pro-posed model has the highest recognition rate;Also,the average precision of proposed model is 14.3％higher than SqueezeNet,16.3％higher than ZFNet,and 4.46％higher than AlexNet,which demonstrates the advantages of cascaded residual network mod-el and SVM model.
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