稀疏距离扩展目标自适应检测及性能分析

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.

Sparsely Range-spread Target Detector and Performance Assessment

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.