In this paper, adaptive signal detection is addressed for range-spread targets in unknown zero-mean Gaussian clutter with persymmetric covariance matrix. The range-spread target can be expressed as the product of a known subspace and its unknown arbitrary coordinates. Under the above assumptions, a persymmetric subspace detector is devised by utilizing the generalized likelihood ratio test (GLRT). Moreover, the proposed persymmetric subspace GLRT-based detector is theoretically proved to be constant false alarm rate to the unknown clutter covariance matrix. Finally, the numerical results demonstrate the effectiveness of the proposed detector, compared with the existing unstructured competitors and persymmetric ones, especially in the limited training data scenarios.

在本文中，针对具有全对称协方差矩阵的未知零均值高斯杂波中的距离扩展目标进行自适应信号检测。范围扩展目标可以表示为已知子空间及其未知任意坐标的乘积。在上述假设下，利用广义似然比检验（GLRT）设计了一个全对称子空间检测器。此外，所提出的基于全对称子空间GLRT的检测器在理论上被证明对未知杂波协方差矩阵具有恒定的误报率。最后，与现有的非结构化竞争者和非对称竞争者相比，数值结果证明了所提出的检测器的有效性，特别是在有限的训练数据场景中。