A hierarchical clustering architecture is proposed to deal with the problem of jamming environment classification when multiple noise-like jammers are possibly present. Assuming the availability of clutter-free multichannel data, a two-level hierarchical procedure is devised to unveil the presence of clusters containing range cells experiencing the same jamming interference as the cell under test. Level 1 relies on the use of covariance smoothing and model-order selection rules to make inference on the number of jamming signals affecting each range bin within the radar range swath. Level 2 allows to discriminate among possible different interfering scenarios characterized by the same number of jammers via an unsupervised learning clustering fed by a suitable feature set. At the analysis stage, the performance of the devised architecture is investigated over simulated and measured data (via software-defined radio devices) to highlight the benefits of the approach.

中文翻译：

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干扰环境分类的聚类方法

当可能存在多个类噪声干扰器时，提出了一种分层聚类架构来处理干扰环境分类问题。假设无杂波多通道数据的可用性，设计了一个两级分层程序来揭示包含与被测小区相同干扰干扰的距离小区的集群的存在。级别 1 依赖于协方差平滑和模型阶数选择规则的使用来推断影响雷达范围内每个距离区间的干扰信号的数量。级别 2 允许通过由合适的特征集馈送的无监督学习聚类，区分以相同数量干扰器为特征的可能不同干扰场景。在分析阶段，