In this paper, four adaptive radar architectures for target detection in heterogeneous Gaussian environments are devised. The first architecture relies on a cyclic optimization exploiting the Maximum Likelihood Approach in the original data domain, whereas the second detector is a function of transformed data which are normalized with respect to their energy and with the unknown parameters estimated through an Expectation-Maximization-based alternate procedure. The remaining two architectures are obtained by suitably combining the estimation procedures and the detector structures previously devised. Performance analysis, conducted on both simulated and measured data, highlights that the architecture working in the transformed domain guarantees the constant false alarm rate property with respect to the interference power variations and a limited detection loss with respect to the other detectors, whose detection thresholds nevertheless are very sensitive to the interference power.

中文翻译：

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用于异构环境的雷达自适应检测架构

在本文中，设计了四种用于异构高斯环境中目标检测的自适应雷达架构。第一种架构依赖于利用原始数据域中最大似然法的循环优化，而第二个检测器是转换数据的函数，这些数据根据能量进行归一化，并通过基于期望最大化的未知参数估计替代程序。其余两种架构是通过适当组合估计程序和先前设计的检测器结构而获得的。性能分析，对模拟和测量数据进行，