中文摘要：

在球不变随机向量的非高斯背景下,针对估计协方差矩阵可能奇异的情况,研究了距离扩展目标的自适应检测方法。首先,推导了非高斯背景下未知协方差矩阵和目标散射点幅度的修正最大似然（maximum likelihood,ML）估计;然后,基于纹理分量的近似ML估计,建立了自适应检测器（adaptively modified generalized likelihood ratio test,AMGLRT）。仿真结果表明,AMGLRT在目标散射点能量均匀分布时检测性能最佳,随着杂波尖峰的减小或阵元数的增加,AMGLRT的检测性能有所改善;且其对不同杂波相关性表现出很好的鲁棒性。另外,AMGLRT的检测性能优于已有的M/K检测器,且这种性能优势随着散射点个数的增加而增大。

英文摘要：

In the case of a certain estimated covariance matrix becoming singular in the non-Gaussian clutter context,which is modeled as a spherical invariant random vector,a self-adaptive range-spread target detection is addressed in this paper.We derive the modified maximum likelihood estimation for unknown parameters including non-Gaussian clutter covariance matrix and scatterer amplitudes;then get through with the approximate ML estimation of the texture;and devise a detector,the adaptively modified generalized likelihood ratio test（AMGLRT）.Simulation results show that the AMGLRT had the best performance if the target energy was uniformly distributed,while with increasing number of sensors or decreasing clutter spikiness alsos improve the AMGLRT detection performance.Without secondary data,the AMGLRT detector outperforms the existing common M/K detector.