HRRP ship targets recognition based on double branches feature fusion convolutional neural network

doi:10.7523/j.ucas.2024.012

Abstract

Abstract: To improve the accuracy of radar high resolution range profile ship target recognition, a ship target recognition method based on double branches feature fusion convolutional neural network model was proposed. Two branches were designed to extract features at different levels. The method designs a stacked convolutional detail branches with fewer downsampling times to extract high resolution local features of ships. The global branch is composed of a modular structure used to extract low resolution global attitude features of ships. Based on the dimensional changes of the feature map after passing through two branches, the two features are changed in size separately in the feature fusion module, and the features are fused with each other to output recognition results. The experimental results show that the proposed method has faster convergence, fewer parameters, and higher accuracy compared to traditional recognition methods, verifying its effectiveness in HRRP ship classification.

Key words: ship target recognition, high resolution range profile, CNN, feature fusion

CLC Number:

TN957.52

ZHU Sijian, QI Xiangyang, FAN Huaitao. HRRP ship targets recognition based on double branches feature fusion convolutional neural network[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2024.012.

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