基于稳健变分自编码模型的雷达高分辨距离像目标识别算法  

Robust Variational Auto-Encoder for Radar HRRP Target Recognition

作　　者：翟颖 陈渤[2] ZHAI Ying;CHEN Bo(Xi’an Research Institute of Navigation Technology,Xi’an,Shaanxi 710068,China;National Laboratory of Radar Signal Processing,Xidian University,Xi’an,Shaanxi 710071,China)

机构地区：[1]西安导航技术研究所,陕西西安710068 [2]西安电子科技大学雷达信号处理国家重点实验室,陕西西安710071

出　　处：《电子学报》2020年第6期1149-1155,共7页Acta Electronica Sinica

摘　　要：对于雷达高分辨距离像的识别问题,传统深层网络通常忽略了HRRP自身的目标特性,不利于学习有效的分类特征,导致其识别性能受到限制.针对这一问题,本文提出了一种基于稳健变分自编码模型的目标识别算法.该算法结合HRRP数据特性,利用平均像在散射点不发生越距离单元走动的方位帧内具有稳健物理特性的性质,基于变分自编码器构建了稳健变分自编码模型.该模型不仅能够获取稳健有效的识别特征,而且在一定程度上保存了数据的帧内结构信息,较大地提高了目标的平均识别率.基于实测HRRP数据验证了所提算法的有效性.Traditional deep networks used for radar High-Resolution Range Profile(HRRP)target recognition usually ignore the inherent characteristics of the target,which results in the limited capability to learn effective features for classification task.To address this issue,a novel nonlinear feature learning method,called Robust Variational Auto-Encoder model(RVAE)is proposed.According to the stable physical properties of the average profile in each HRRP frame without migration through resolution cell,RVAE is developed based on variational auto-encoder,and such model is able to not only explore the latent representations of HRRP but also reserve structure characteristics of the HRRP frame.We use the measured HRRP data to show the effectiveness and efficiency of our algorithm.

关 键 词：雷达自动目标识别 高分辨距离像 特征提取 稳健变分自编码模型 

分 类 号：TN95[电子电信—信号与信息处理]