In this paper, we deal with the problem of detecting point-like targets in the presence of Gaussian disturbance with unknown covariance matrix. In particular, we consider the so-called partially homogeneous environment, where the disturbances in both the cell under test (CUT) and the secondary data share the same covariance matrix up to an unknown power scaling factor. Specifically, we model the disturbance as a multichannel autoregressive Gaussian process and exploit the spillover of target energy to consecutive range samples, in order to improve the performances of detection and range estimation. In this context, we come up with three adaptive architectures relying on the ad hoc modifications of the generalized likelihood ratio test and Wald test. The performance assessment, conducted resorting to both simulated data and recorded live data, highlights that the proposed decision schemes can provide accurate estimates of the target position within the CUT and ensure enhanced detection performance compared with their natural competitors.

中文翻译：

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部分均匀环境下点状目标的参数时空检测与距离估计

在本文中，我们处理在存在协方差矩阵未知的高斯扰动的情况下检测点状目标的问题。特别是，我们考虑了所谓的部分同质环境，其中被测单元 (CUT) 和辅助数据中的干扰共享相同的协方差矩阵，直到未知的功率缩放因子。具体来说，我们将干扰建模为多通道自回归高斯过程，并利用目标能量对连续距离样本的溢出，以提高检测和距离估计的性能。在这种情况下，我们提出了三种自适应架构，这些架构依赖于广义似然比检验和 Wald 检验的临时修改。使用模拟数据和记录的实时数据进行的性能评估，