多域浅层特征引导下雷达有源干扰多模态对比识别方法

Multidomain Characteristic-guided Multimodal Contrastive Recognition Method for Active Radar Jamming

在雷达有源干扰识别任务中,如何实现多域浅层特征与时频域深层网络特征的稳健联合,并在极端小样本下维持高干扰识别准确率是亟待解决的关键问题。针对此问题,该文提出一种多域浅层特征引导下雷达有源干扰多模态对比识别方法。在充分提取有源干扰多域浅层特征基础上,设计优选单元自动选择有效特征,生成对应含有隐式专家知识的文本模态。将文本模态与时频变换图像分别输入文本和图像编码器,构建多模态特征对并映射至模态对齐高维空间中,利用文本特征作为锚点,通过对比学习引导同类干扰的时频图像特征聚合,以优化图像编码器表征能力,实现干扰识别特征类内更聚集、类间更分离。实验结果表明,相较于已有深浅特征直接联合,所提引导式联合方法可以实现特征差异处理,从而提高识别特征判别力和泛化力。且在极端小样本条件(每类干扰训练样本为2~3个)下,所提识别方法较先进对比方法的准确率提升9.84%,证明了该文方法的有效性与鲁棒性。

Achieving robust joint utilization of multidomain characteristics and deep-network features while maintaining a high jamming-recognition accuracy with limited samples is challenging.To address this issue,this paper proposes a multidomain characteristic-guided multimodal contrastive recognition method for active radar jamming.This method involves first thoroughly extracting the multidomain characteristics of active jamming and then designing an optimization unit to automatically select effective characteristics and generate a text modality imbued with implicit expert knowledge.The text modality and involved time-frequency transformation image are separately fed into text and image encoders to construct multimodal-feature pairs and map them to a high-dimensional space for modal alignment.The text features are used as anchors and a guide to time-frequency image features for aggregation around the anchors through contrastive learning,optimizing the image encoder’s representation capability,achieving tight intraclass and separated interclass distributions of active jamming.Experiments show that compared to existing methods,which involve directly combining multidomain characteristics and deep-network features,the proposed guided-joint method can achieve differential feature processing,thereby enhancing the discriminative and generalization capabilities of recognition features.Moreover,under extremely small-sample conditions(2~3 training samples for each type of jamming),the accuracy of our method is 9.84%higher than those of comparative methods,proving the effectiveness and robustness of the proposed method.