In this paper, the problem of the detection and localization of clutter edges within training data is addressed. This is accomplished through a procedure capable of discriminating between either a unique homogeneous set or two heterogeneous subsets within a sliding window moving over the set of range bins of interest. The problem is first formulated as a binary hypothesis test assuming that the rank of the covariance clutter component is known and solved resorting to the generalized likelihood ratio test. Then, in the case of no a priori knowledge about the rank of the clutter covariance matrix, a preliminary estimation stage relying on the model order selection rules is devised. Interestingly, the estimates provided by the detection stage can be processed by a fusion algorithm in order to improve the quality of the location estimate of the clutter edge. Finally, the performance analysis conducted in comparison with a suitable competitor highlights the effectiveness of the proposed solutions.

在本文中，解决了训练数据中杂波边缘的检测和定位问题。这是通过能够在滑动窗口内在目标范围仓的集合上移动的唯一同构集合或两个异类子集进行区分的过程来实现的。假设协方差杂波分量的秩已知并通过广义似然比检验解决，该问题首先被公式化为二元假设检验。然后，在没有关于杂波协方差矩阵的秩的先验知识的情况下，设计依赖于模型顺序选择规则的初步估计阶段。有趣的是，检测阶段提供的估计可以通过融合算法进行处理，以提高杂波边缘的位置估计的质量。最后，与合适的竞争对手进行的性能分析突出了所提出解决方案的有效性。