In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets. The first strategy, which appears here for the first time, jointly exploits the maximum likelihood approach and Bayesian learning to estimate targets’ parameters including their positions in terms of range bins. The second strategy relies on the intuition that for high signal-to-interference-plus-noise ratio values, the energy of data containing target components projected onto the nominal steering direction should be higher than the energy of data affected by interference only. The adaptivity with respect to the interference covariance matrix is also considered exploiting a training data set collected in the proximity of the window under test. Finally, another important innovation aspect concerns the adaptive estimation of the unknown number of targets by means of the model order selection rules.

中文翻译：

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多个点状目标的新型参数估计和雷达检测方法：设计和比较

在这项工作中，我们开发并比较了两种用于多个点状目标的参数估计和雷达检测的创新策略。第一种策略，首次出现在这里，联合利用最大似然方法和贝叶斯学习来估计目标的参数，包括它们在范围仓方面的位置。第二种策略依赖于直觉，即对于高信号干扰加噪声比值，包含投影到标称转向方向的目标分量的数据能量应高于仅受干扰影响的数据能量。还考虑利用在被测窗口附近收集的训练数据集来考虑关于干扰协方差矩阵的适应性。最后，