（1.College of Computer Science and Electronic Engineering，Hunan University，Changsha 410082，China； 2.ATR Laboratory，National University of Defense Technology，Changsha 410073，China ） 在知网中查找 在百度中查找 在本站中查找
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摘要:
提出一种基于卷积神经网络（Convolution Neural Network，CNN）的高分辨率雷达目标识别方法.首先针对小样本应用于深度CNN时训练过程中损失函数值收敛速度慢的问题，利用结合批归一化算法的改进CNN网络对高分辨距离像（High Resolution Range Profile，HRRP）进行自动特征提取；再利用支持向量机（Support Vector Machine，SVM）对距离像特征进行分类.使用军事车辆高保真电磁仿真数据对提出的方法进行验证，识别结果证明了该方法的有效性.
A new method of high resolution radar target recognition based on Convolution Neural Network (CNN) was presented. To solve the problem of slow convergence of loss function values during the training process when small samples are applied to the deep CNN, High Resolution Range Profile (HRRP) features were firstly extracted by using the improved CNN combined with the Batch Normalization (BN) algorithm, and then classified by using a Support Vector Machine (SVM). The experimental results using high-fidelity electromagnetic simulation data of military vehicles validate the effectiveness of the proposed method.
