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.
关键词
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
Key words
Active false target/Multi Coherent Processing Intervals(CPIs)/Scattering characteristics/Fast-slow time domain frequency response/Unsupervised/Agglomerative clustering
维普
万方数据