Radar Target Recognition Based on Feature Pyramid Fusion Lightweight CNN

Abstract

In order to improve the accuracy and robustness of radar target recognition under low SNR conditions, a novel radar high range resolution profile (HRRP) target recognition method based on feature pyramid fusion lightweight CNN is proposed in this paper. The proposed method combines the multi-scale space theory with a deep convolutional neural network. Because of the local connection characteristic of convolutional kernel, feature extracted by CNN mainly focus on the local information of the target. To make full use of both the local and global information in the target HRRP, multi-scale representation of the HRRP with different Gaussian kernels is introduced to construct the multi-channel input of the model. The generalization performance is improved by reducing the parameters of the proposed model with a depthwise separable convolution feature extraction block. Simultaneously, feature pyramid fusion is adapted to take full advantage of the features extracted by each block, which effectively improves the stability of the model and the training efficiency. The experimental results show that the multi-scale representation of the HRRP contributes to robust feature extraction. Meanwhile, the proposed feature pyramid fusion lightweight CNN can effectively prevent over-fitting and improve the stability of the model.