刘永祥,,
霍凯
国防科技大学电子科学学院 ??长沙 ??410073
基金项目:国家自然科学基金（61422114，61501481），湖南省****科学基金（2015JJ1003）
详细信息
作者简介:赵飞翔(1989–)，男，河南洛阳人，国防科技大学电子科学学院在读博士生，研究方向为雷达目标识别。E-mail: zfxkj123@sina.cn
刘永祥(1976–)，男，河北唐山人，博士，国防科技大学电子科学学院智能感知系主任，教授，博士生导师，主要研究方向为目标微动特性分析与识别。E-mail: lyx_bible@sina.com
霍凯：霍? 凯(1983–)，男，湖北黄冈人，博士，国防科技大学电子科学学院讲师，主要研究方向为雷达信号处理与目标识别。E-mail: huokai2001@163.com
通讯作者:刘永祥? lyx_bible@sina.com
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出版历程
收稿日期:2018-06-22
修回日期:2018-08-29
A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine
Zhao Feixiang,Liu Yongxiang,,
Huo Kai
College of Electronic Science, National University of Defense Technology, Changsha 410073, China
Funds:The National Natural Science Foundation of China (61422114, 61501481), The Natural Science Fund for Distinguished Young Scholars of Hunan Province (2015JJ1003)
摘要
摘要:雷达目标分类在军事和民用领域发挥着重要作用。极限学习机(Extreme Learning Machine, ELM)因其学习速度快、泛化能力强而被广泛应用于分类任务中。然而，由于其浅层结构，ELM无法有效地捕获数据深层抽象信息。虽然许多研究者已经提出了深度极限学习机，它可以用于自动学习目标高级特征表示，但是当训练样本有限时，模型容易陷入过拟合。为解决此问题，该文提出一种基于Dropout约束的深度极限学习机雷达目标分类算法，在雷达测量数据上的实验结果表明所提算法在分类准确率上达到93.37%，相较栈式自动编码器算法和传统深度极限学习机算法分别提高了5.25%和8.16%，验证了算法有效性。
关键词:极限学习机/
深度学习/
Dropout约束/
雷达目标分类/
栈式自动编码器
Abstract:Radar target classification is very important in military and civilian fields. Extreme Learning Machines (ELMs) are widely used in classification because of their fast learning speed and good generalization performance. However, because of their shallow architecture, ELMs may not effectively capture the data high level abstractions. Although many researchers have proposed the Deep Extreme Learning Machine (DELM), which can be used to automatically learn high level feature representations, the model easily falls into overfitting when the training sample is limited. To address this issue, Dropout Constrained Deep Extreme Learning Machine (DCDELM) is proposed in this paper. The experimental results on the measured radar data show that the accuracy of the proposed algorithm can reach 93.37%, which is 5.25% higher than that of the stacked autoencoder algorithm, and 8.16% higher than that of the traditional DELM algorithm.
Key words:Extreme Learning Machine (ELM)/
Deep learning/
Dropout constrained/
Radar target classification/
Stacked autoencoder
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