This paper addresses the adaptive detection of subspace signals in the noise whose covariance matrix is unknown. The partially homogeneous scenario, where the primary data have the same noise covariance matrix with the training data up to an unknown scaling factor is considered. We exploit the persymmetric structure of the noise covariance matrix to enhance the matched detection performance in the case of limited number of training data. Three persymmetric subspace detectors are proposed by applying the generalized likelihood ratio (GLR), Rao and Wald design criteria, respectively. It is proved that the three persymmetric subspace detectors can ensure the constant false alarm rate (CFAR) property. Experimental results show that the new persymmetric subspace detectors significantly outperform the conventional subspace detector in terms of the matched detection performance. Compared with the persymmetric rank-one signal detectors, the proposed persymmetric subspace detectors are more robust in the mismatched signal case.

中文翻译：

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部分均匀环境中的自适应过对称子空间检测器

本文讨论了协方差矩阵未知的噪声中子空间信号的自适应检测。部分同质场景，其中主要数据与训练数据具有相同的噪声协方差矩阵，直到一个未知的比例因子被考虑。我们利用噪声协方差矩阵的过对称结构来提高训练数据数量有限的情况下的匹配检测性能。通过分别应用广义似然比 (GLR)、Rao 和 Wald 设计标准，提出了三个过对称子空间检测器。证明三个过对称子空间检测器可以保证恒定的误报率（CFAR）特性。实验结果表明，新的过对称子空间检测器在匹配检测性能方面明显优于传统的子空间检测器。与过对称秩一信号检测器相比，所提出的过对称子空间检测器在信号不匹配的情况下更加鲁棒。