基于卷积稀疏编码与多分类器融合的雷达HRRP目标识别方法  

Radar HRRP target recognition based on convolutional sparse coding and multi-classifier fusion

作　　者：王彩云[1] 胡允侃 李晓飞 魏文怡 赵焕玥 WANG Caiyun;HU Yunkan;LI Xiaofei;WEI Wenyi;ZHAO Huanyue(College of Astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Beijing Institute of Electronic System Engineering,Beijing 100854,China)

机构地区：[1]南京航空航天大学航天学院,江苏南京210016 [2]北京电子工程总体研究所,北京100854

出　　处：《系统工程与电子技术》2018年第11期2433-2437,共5页Systems Engineering and Electronics

基　　金：青年科学基金(61301211)资助课题

摘　　要：针对雷达高分辨距离像(high resolution range profile,HRRP)目标识别问题,提出基于卷积稀疏编码与多分类融合(convolutional sparse coding and multi-classifier fusion,CSCMF)的识别方法。首先,该方法利用CSC方法对目标HRRP进行特征提取,同时实现数据压缩;然后,将测试样本的特征分别输入随机森林分类器、朴素贝叶斯分类器和最小值分类器进行预分类,得到3个预测标签。采用多数投票法对3个预测标签进行分类器融合,得到最终的识别决策。实验中研究了分类器融合方法。基于5种飞机目标的HRRP仿真数据进行了实验验证,实验结果表明该方法的分类准确率较高,而且对噪声有较强的鲁棒性。A radar high resolution range profile(HRRP)target recognition algorithm based on convolutional sparse coding and classifier fusion method,named convolutional sparse coding and multi-classifier fusion(CSCMF)is proposed.Firstly,it extracts the features from the HRRPs using the convolutional sparse coding(CSC)method,and realizes the compression of the data set.Secondly,three different classifiers(random forest classifier,naive Bayesian classifier,and minimum classifier)that fuse the sparse coding characteristics were used to obtain three predictive labels.Finally,we adopt classifier fusion by the majority of the voting methods to get the final recognition decision.We researched some classifiers algorithms in our experiments,and the simulation results based on the radar high resolution range profile database demonstrate the presented method can achieve remarkable classification performance and more robust to noise.

关 键 词：雷达目标识别 高分辨一维距离像 稀疏编码 卷积字典学习 

分 类 号：TP957.52[自动化与计算机技术]