In this paper, we consider the distributed target detection problem with unknown signal signatures in Gaussian noise with unknown covariance matrix. Two adaptive detectors are proposed by using the persymmetry of the noise covariance matrix. We derive analytical expressions for the probabilities of false alarm of the proposed detectors, which indicate their constant false alarm rate properties against the noise covariance matrix. All the theoretical expressions are confirmed by Monte Carlo simulations. Numerical examples demonstrate that the proposed detectors have better detection performance than their counterparts, especially in the case of limited training data.

中文翻译：

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具有未知转向矢量的分布式目标的过对称自适应检测

在本文中，我们考虑具有未知协方差矩阵的高斯噪声中具有未知信号特征的分布式目标检测问题。利用噪声协方差矩阵的过对称性提出了两种自适应检测器。我们推导出所提出的检测器误报概率的解析表达式，这表明它们相对于噪声协方差矩阵的恒定误报率属性。所有的理论表达式都得到了蒙特卡罗模拟的证实。数值例子表明，所提出的检测器比同类检测器具有更好的检测性能，尤其是在训练数据有限的情况下。