Recognition of multifunction radar (MFR) is an open problem in the field of electronic intelligence. Parameters of MFR pulses are generally agile and difficult to distinguish statistically. A prospective way to realize credible MFR recognition is mining and exploiting more distinguishable high-dimensional patterns buried in pulse groups, which may be designed for implementing infrequently used radar modes such as target tracking. A high-dimensional pattern is defined according to the agile range and switching law of sequential pulse repetitive intervals within a pulse group. This article establishes deep recurrent neural networks (RNN) to discriminate and coarsely cluster different pulse groups hierarchically with respect to their sequential structures. Afterwards, RNN-based classifiers are trained to extract and exploit features within different pulse group clusters. Distinct degrees of confidence are then attached to these classifiers to indicate the discriminabilities of the corresponding pulse group clusters. The pulse group clustering and classifying models are finally cascaded to form an integrated classification model, which mines distinguishable patterns from sequentially arriving pulse groups of the same radar and accumulate them to realize MFR recognition. Simulation results demonstrate the much improved performance of the proposed method over existing counterparts in different scenarios.

中文翻译：

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通过分层挖掘和利用脉冲群模式识别多功能雷达

多功能雷达（MFR）的识别是电子智能领域的一个开放性问题。MFR 脉冲的参数通常是灵活的并且难以统计区分。实现可信 MFR 识别的一种前瞻性方法是挖掘和利用隐藏在脉冲群中的更可区分的高维模式，这些模式可能被设计用于实现不常用的雷达模式，例如目标跟踪。根据脉冲群内连续脉冲重复间隔的敏捷范围和切换规律，定义高维模式。本文建立了深度循环神经网络 (RNN)，以根据其顺序结构分层区分和粗略聚类不同的脉冲组。然后，训练基于 RNN 的分类器以提取和利用不同脉冲组群中的特征。然后将不同的置信度附加到这些分类器以指示相应脉冲组簇的可辨别性。脉冲群聚类和分类模型最终级联形成一个综合分类模型，从同一雷达依次到达的脉冲群中挖掘可区分的模式并积累起来实现MFR识别。仿真结果表明，在不同场景下，所提出方法的性能比现有方法有很大提高。脉冲群聚类和分类模型最终级联形成一个综合分类模型，从同一雷达依次到达的脉冲群中挖掘可区分的模式并积累起来实现MFR识别。仿真结果表明，在不同场景下，所提出方法的性能比现有方法有很大提高。脉冲群聚类和分类模型最终级联形成一个综合分类模型，从同一雷达依次到达的脉冲群中挖掘可区分的模式并积累起来实现MFR识别。仿真结果表明，在不同场景下，所提出方法的性能比现有方法有很大提高。