Low-resolution radar target classification algorithm based on one-dimensional densely connected network

Abstract

To address the problem of low accuracy of traditional low-resolution radar target classification and recognition. In this paper, a low-resolution radar target classification algorithm based on a one-dimensional Densely Connected Convolutional Network (DenseNet) is proposed. The algorithm first directly downscales the Densely Connected Convolutional Network, then takes the original 1D radar target signal as the input for training, uses a segmented loss function for the characteristics of different classes of signals, makes the network use different loss functions in different training stages, and then back-propagates the loss to optimize the weights to improve the recognition effect of the network. The experimental results show that the recognition rate of the proposed method is higher than that of traditional radar target classification methods and simple one-dimensional convolutional neural networks (CNN) for low-spectral radar target classification, especially under low signal-to-noise ratio conditions, which fully demonstrates the effectiveness of the proposed method.