零记忆增量学习的复合有源干扰识别  

Compound Active Jamming Recognition for Zero-memory Incremental Learning

作　　者：吴振华 崔金鑫 曹宜策 张强 张磊 杨利霞 WU Zhenhua;CUI Jinxin;CAO Yice;ZHANG Qiang;ZHANG Lei;YANG Lixia(Information Materials and Intelligent Sensing Laboratory of Anhui Province,Anhui University,Hefei 230601,China;Thirty-eighth Research Institute of China Electronics Technology Group Corporation,Hefei 230088,China;National Key Laboratory of Space Integrated Information System,Beijing 100094,China;School of Electronics and Communication Engineering,Sun Yat-sen University,Shenzhen 518107,China)

机构地区：[1]安徽大学信息材料与智能感知安徽省实验室,合肥230601 [2]中国电子科技集团公司第三十八研究所,合肥230088 [3]天基综合信息系统全国重点实验室,北京100094 [4]中山大学电子与通信工程学院,深圳518107

出　　处：《电子与信息学报》2025年第1期188-200,共13页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62201007,62401007);中国博士后科学基金(2020M681992);安徽省自然科学基金(2308085QF199)。

摘　　要：非完备、高动态有源干扰对抗作战环境下,现阶段针对库内多类型单一有源干扰样本所优化训练的静态模型,在面对库外类型多样、参数多变、组合方式多元的复合干扰时,模型无法快速更新且难以应对测试样本数非均衡问题。针对此问题,该文提出一种基于零记忆增量学习的雷达复合有源干扰识别方法。首先,利用元学习训练模式对库内单一干扰进行原型学习,训练出高效的特征提取器,使其具备对库外复合干扰特征有效提取能力。进而,基于超维空间和余弦相似度计算,构建零记忆增量学习网络(ZMILN),将复合干扰原型向量映射到超维空间并存储,从而实现识别模型动态更新。此外,为解决样本数非均衡下复合干扰识别问题,设计直推式信息最大化(TIM)测试模块,通过在互信息损失函数中加入散度约束,对识别模型进一步强化训练以应对非均衡测试样本。实验结果表明,该文所提方法在非均衡测试条件下对4种单一干扰和7种复合干扰进行增量学习后,平均识别准确率达到了93.62%。该方法通过对库内多类型单一干扰知识充分提取,实现对多种组合条件下库外复合干扰的快速动态识别。Objective:In contemporary warfare,radar systems serve a crucial role as vital instruments for detection and tracking.Their performance is essential,often directly impacting the progression and outcome of military engagements.As these systems operate in complex and hostile environments,their susceptibility to adversarial interference becomes a significant concern.Recent advancements in active jamming techniques,particularly compound active jamming,present considerable threats to radar systems.These jamming methods are remarkably adaptable,employing a range of signal types,parameter variations,and combination techniques that complicate countermeasures.Not only do these jamming signals severely impair the radar’s ability to detect and track targets,but they also exhibit rapid adaptability in high-dynamic combat scenarios.This swift evolution of jamming techniques renders traditional radar jamming recognition models ineffective,as they struggle to address the fast-changing nature of these threats.To counter these challenges,this paper proposes a novel incremental learning method designed for recognizing compound active jamming in radar systems.This innovative approach seeks to bridge the gaps of existing methods when confronted with incomplete and dynamic jamming conditions typical of adversarial combat situations.Specifically,it tackles the challenge of swiftly updating models to identify novel out-of-database compound jamming while mitigating the performance degradation caused by imbalanced sample distributions.The primary objective is to enhance the adaptability and reliability of radar systems within complex electronic warfare environments,ensuring robust performance against increasingly sophisticated and unpredictable jamming techniques.Methods:The proposed method commences with prototypical learning within a meta-learning framework to achieve efficient feature extraction.Initially,a feature extractor is trained utilizing in-database single jamming signals.This extractor is thoroughly designed to proficiently

关 键 词：雷达有源干扰 零记忆增量学习 非均衡 直推式信息最大化 复合干扰识别 

分 类 号：TN974[电子电信—信号与信息处理]