Since Kelly's pioneering work on GLRT-based adaptive detection, many solutions have been proposed to enhance either selectivity or robustness of radar detectors to mismatched signals. In this paper such a problem is addressed in a different space, called CFAR feature plane and given by a suitable maximal invariant, where observed data are mapped to clusters that can be analytically described. The characterization of the trajectories and shapes of such clusters is provided and exploited for both analysis and design purposes, also shedding new light on the behavior of several well-known detectors. Novel linear and non-linear detectors are proposed with diversified robust or selective behaviors, showing that through the proposed framework it is not only possible to achieve the same performance of well-known receivers obtained by a radically different design approach (namely GLRT), but also to devise detectors with unprecedented behaviors: in particular, our results show that the highest standard of selectivity can be achieved without sacrifying neither detection power under matched conditions nor CFAR property.

中文翻译：

---

CFAR 特征平面：一种用于雷达探测器分析和设计的新框架

自从 Kelly 在基于 GLRT 的自适应检测方面的开创性工作以来，已经提出了许多解决方案来增强雷达检测器对不匹配信号的选择性或鲁棒性。在本文中，此类问题在称为 CFAR 特征平面的不同空间中得到解决，并由合适的最大不变量给出，其中观察到的数据映射到可以分析描述的集群。这些簇的轨迹和形状的特征被提供并用于分析和设计目的，也为几个著名探测器的行为提供了新的思路。提出了具有多样化鲁棒性或选择性行为的新型线性和非线性检测器，