首页|基于多相参处理间隔频响特征聚类的有源假目标鉴别方法

基于多相参处理间隔频响特征聚类的有源假目标鉴别方法

扫码查看
原文链接
  • 万方数据
  • 维普
现有真-假目标智能识别算法大多基于监督学习,且在低信噪比条件下表现不好.针对上述问题,该文分别利用真、假目标在多个相参处理间隔(CPIs)内散射特性的时变性和唯一性,提出一种多相参处理间隔频响特征聚类的真、假目标无监督鉴别方法.首先,在快-慢时域中沿快时间维度对真、假目标进行加窗截断,提取快-慢时间域频率响应特征用于构建初步样本集;然后,通过Agglomerative聚类和特征融合网络组成的两步识别算法对真-假目标进行识别;最后,提出一种多相参处理间隔联合决策方法提升识别性能和可靠性.经仿真和实测数据检验,证明了所提方法可实现真实目标和多种有源假目标的有效分离.
Active False Target Identification Method Based on Frequency Response Features Clustering in Multi-Coherent Processing Intervals
Most of the existing intelligent algorithms for identifying real and false targets are based on supervised learning and perform poorly under a low signal-to-noise ratio.Considering the above problems,an unsupervised clustering identification method of real and false targets based on frequency response features in multi-Coherent Processing Intervals(CPIs)is proposed by using the variability and uniqueness of the scattering characteristics of real and false targets in multi-CPIs,respectively.Firstly,the real and false targets are windowed and truncated along the fast time dimension in the fast-slow time domain,and the fast-slow time domain frequency response features are extracted to construct a preliminary sample set.Then,the real and false targets are identified by a two-step recognition algorithm composed of an Agglomerative clustering and a feature fusion network.Finally,a multi-CPI joint decision method is proposed to improve the recognition performance and reliability.It is proved by simulation and measured data that the proposed method can effectively identify real targets and multiple active false targets.

Active false targetMulti Coherent Processing Intervals(CPIs)Scattering characteristicsFast-slow time domain frequency responseUnsupervisedAgglomerative clustering

韦文斌、彭锐晖、孙殿星、谭顺成、宋颖娟、张家林

展开 >

哈尔滨工程大学青岛创新发展基地 青岛 266000

海军航空大学信息融合研究所 烟台 264001

有源假目标 多相参处理间隔 散射特性 快-慢时间域频率响应 无监督 Agglomerative聚类

航天科技集团稳定支持项目中国博士后科学基金国家自然科学基金

ZY01100200092021M69300361731023

2024

10.11999/JEIT231012

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子：1.302
ISSN：1009-5896
年,卷(期)：2024.46(7)
  • 8