中文摘要：

雷达目标识别中特征提取是关键步骤,所提取特征的好坏决定着识别效果的优劣,但传统特征提取方法很难发掘目标数据深层次本质特征。深度学习理论中的自动编码器模型能够用数据去学习特征,获得数据不同层次的特征表达。同时为消除噪声影响,该文提出一种基于栈式降噪稀疏自动编码器的雷达目标识别方法,通过设置不同隐藏层数和迭代次数,从雷达数据中直接高效地提取识别所需的各层次特征。暗室仿真数据实验结果验证了该方法较K近邻分类方法及传统栈式自编码器有更好的识别效果。

英文摘要：

Feature extraction is a key step in radar target recognition.The quality of the extracted features determines the performance of target recognition.However,obtaining the deep nature of the data is difficult using the traditional method.The autoencoder can learn features by making use of data and can obtain feature expressions at different levels of data.To eliminate the influence of noise,the method of radar target recognition based on stacked denoising sparse autoencoder is proposed in this paper.This method can extract features directly and efficiently by setting different hidden layers and numbers of iterations.Experimental results show that the proposed method is superior to the K-nearest neighbor method and the traditional stacked autoencoder.