Research on Radar Target Recognition Method Based on Deep Learning

At present, after radar target detection, radar target recognition mainly depends on manual judgment. Manual identification relies too much on the operator's personal experience and subjective consciousness, which takes a long time and has a large error. To solve this problem, a radar target recognition method based on deep learning is proposed. By analyzing the differential characteristics of radar echoes of different targets, taking the range profile of radar echo sequence as the data set, a deep residual network classifier model is designed to classify and identify the types of radar targets. In order to improve the generalization ability of the network and avoid the over-fitting problem, the deep convolutional generative adversarial network is used to expand the range profile data set of radar echo sequence. In order to ensure the quality of the generated samples during data set expansion, the expanded samples are screened with the peak signal-to-noise ratio as the index. The experimental results of radar measured data show that this method has a good effect on radar target recognition.