摘要
针对开集条件下多视高分辨距离像(HRRP)目标识别问题,提出了一种基于联合动态稀疏表示(JDSR)的开集识别方法。该方法利用JDSR求解多视HRRP在过完备字典上的重构误差,采用极值理论(EVT)对匹配和非匹配类别的重构误差拖尾进行建模,将开集识别问题转化为假设检验问题求解。识别时利用重构误差确定候选类,根据尾部分布的置信度获得匹配类与非匹配类得分,并将两者的加权和作为类别判据最终确定库外目标或候选类。该方法能够有效利用多视观测来自相同目标的先验信息提高开集条件下的HRRP识别性能,并且对多视数据不同的获取场景具有良好的适应性。利用从MSTAR反演生成的HRRP数据对算法进行了测试,结果表明所提方法的性能优于主流开集识别方法。
Focusing on the issue of multi-view High-Resolution Range Profile(HRRP)target recognition in an open set,a novel method based on Joint Dynamic Sparse Representation(JDSR)is presented.First,JDSR is used to solve the reconstruction error of multi-view HRRP on the over completed dictionary.The reconstruction error trails of matched and unmatched categories are modeled using Extreme Value Theory(EVT),and subsequently,the open-set recognition problem is transformed into a hypothesis test problem.The reconstruction error is used to determine candidate classes during the identification phase.The matched and nonmatched class scores are obtained based on the confidence level of the tail distribution,and their weighted sum is used to decide whether the inputs are from nonlibrary categories or candidate classes.The input HRRPs are obtained from the same target and can be used as useful information to improve recognition performance.The proposed method can effectively use such prior information for performance enhancement under the openset condition.Moreover,performance can remain robust under multiview data acquisition scenarios.The HRRP data generated from MSTAR chips are used for the identification experiments,and the results reveal that the proposed method performs considerably better than some state-of-the-art methods.
作者
刘盛启
张会强
滕书华
瞿爽
吴中杰
LIU Shengqi;ZHANG Huiqiang;TENG Shuhua;QU Shuang;WU Zhongjie(National Key Laboratory of Science and Technology on Automatic Target Recognition,National University of Defense Technology,Changsha 410073,China;College of Electronic Information,Hunan First Normal University,Changsha 410205,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2023年第11期4101-4109,共9页
Journal of Electronics & Information Technology
基金
国家自然科学基金(62001486,62201587)
湖南省自然科学基金(2023JJ0185)
湖南省教育厅科学研究重点项目(22A0640)。
关键词
开集识别
联合动态稀疏表示
极值理论
高分辨距离像
Open-Set Recognition(OSR)
Joint Dynamic Sparse Representation(JDSR)
Extreme value theory
High-Resolution Range Profile(HRRP)
