One dimensional convolution neural network radar target recognition based on direct sampling data

The traditional method for the recognition of low-resolution radar targets is based on artificial feature extraction, which requires the feature extraction of data first, which leads to the loss of other data information, and is not conducive to the generalization of recognition methods and improvement of recognition accuracy. Aiming at this problem, this paper proposes a one-dimensional convolution neural network low resolution radar target recognition method based on direct sampling data. In this method, the sampling data is taken as the network input data. By adjusting the weight and quantity of convolution kernel, the deep essential features are automatically obtained from the sampling data, and then the recognition of radar target is realized by softmax classifier. The simulation results show that this method can identify radar targets accurately and has 85% target recognition rate when SNR is -10dB.This paper provides a new solution for radar target recognition.