摘要
在球不变随机向量杂波背景下,研究了稀疏距离扩展目标的自适应检测问题.基于有序检测理论,利用协方差矩阵估计方法,分析了自适应检测器(Adaptive detector,AD).其中,基于采样协方差矩阵(Sample covariance matrix,SCM)和归一化采样协方差矩阵(Normalized sample covariance matrix,NSCM),分别建立了AD-SCM和AD-NSCM检测器.从恒虚警率特性和检测性能综合来看,AD-NSCM的性能优于AD-SCM和已有的修正广义似然比检测器.最后,通过仿真实验验证了所提方法的有效性.
In the background where the clutter is modeled as a spherically invariant random vector,the adaptive detection of sparsely range-spread targets is addressed.By exploiting the order statistics and the covariance matrix estimators,the adaptive detector（AD） is assessed.Herein,the detectors of AD-SCM and AD-NSCM are proposed based on the sample covariance matrix（SCM） and normalized sample covariance matrix（NSCM）,respectively.In terms of constant false alarm rate properties and detection performance,the AD-NSCM outperforms the AD-SCM and the existing detector of modified generalized likelihood ratio.Finally,the performance assessment conducted by simulation confirms the effectiveness of the proposed detectors.
出处
《自动化学报》
EI
CSCD
北大核心
2013年第7期1126-1132,共7页
Acta Automatica Sinica
基金
国家自然科学基金(61174007
61102166)
山东省优秀中青年科学家科研奖励基金(BS2010DX022)资助~~
关键词
稀疏距离扩展目标
自适应检测
采样协方差矩阵
归一化采样协方差矩阵
有序统计量
Sparsely range-spread target
adaptive detection（AD）
sample covariance matrix（SCM）
normalized sample covariance matrix（NSCM）
order statistics
