As an indispensable part of electronic support measure, the de-interleaving technique is used to separate interleaved radar pulse streams. At present, clustering based on radar features is one of the most effective de-interleaving methods. In a dynamically varying electromagnetic environment, the features of intercepted radar pulses overlap each other. Compared with other de-interleaving algorithms, the fuzzy adaptive resonance theory (Fuzzy ART) has obvious advantages in classifying such radar features. However, it still faces several problems: (i) the vigilance parameter used to regulate de-interleaving performance is difficult to reach its optimal value and (ii) since the unified discrimination threshold is selected for different regions, the algorithm suffers from category proliferation problem. This study addresses these problems by constructing a new vigilance model to replace the unified vigilance parameter and introducing a dual-vigilance mechanism to ART-based de-interleaving systems. It demonstrates this idea in the context of Fuzzy ART, presented as Fuzzy ART based on a 3D fuzzy model with two vigilance thresholds (2VT-3DFA). 2VT-3DFA suppressed the excessive proliferation of categories, and its clustering quality was 20% higher than that of conventional algorithms in dynamically varying signal environment.

中文翻译：

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在动态变化的电磁环境中具有交叠特征的雷达信号解交织的改进算法

作为电子支持措施必不可少的部分，解交织技术用于分离交织的雷达脉冲流。当前，基于雷达特征的聚类是最有效的解交织方法之一。在动态变化的电磁环境中，拦截的雷达脉冲的特征相互重叠。与其他解交织算法相比，模糊自适应共振理论（Fuzzy ART）在对此类雷达特征进行分类方面具有明显优势。但是，它仍然面临几个问题：（i）用于调节解交织性能的警戒参数难以达到其最佳值；（ii）由于针对不同区域选择了统一的判别阈值，因此该算法存在类别扩散问题。本研究通过构建新的警戒模型来代替统一的警戒参数，并在基于ART的解交织系统中引入双重警戒机制，从而解决了这些问题。它在Fuzzy ART的背景下演示了这一思想，该思想以基于具有两个警戒阈值（2VT-3DFA）的3D模糊模型的Fuzzy ART表示。在动态变化的信号环境中，2VT-3DFA抑制了类别的过度扩散，其聚类质量比传统算法高20％。