Compound Active Jamming Recognition for Zero-memory Incremental Learning
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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。Abstract: Under the non-complete and highly dynamic active jamming confrontation combat environment, the static model optimized and trained for multiple types of single active jamming samples in the library at this stage is unable to update the model quickly and difficult to cope with the problem of imbalance of test samples in the face of compound jamming outside the library of diverse types, variable parameters and multiple combinations. Considering this problem, a zero-memory incremental learning-based radar compound active jamming recognition method is proposed in this paper. Firstly, a meta-learning training model is utilized to learn a prototype of a single jamming within the library, and an efficient feature extractor is trained so that it has the ability to effectively extract the features of the compound jamming outside the library. Further, based on the hyperdimensional space and cosine similarity calculation, a Zero-Memory Incremental Learning Network (ZMILN) is constructed to map the compound jamming prototype vectors into the hyperdimensional space and store them, so as to realize the dynamic update of the recognition model. In addition, in order to solve the compound jamming recognition problem under sample imbalance, the Transductive Information Maximization (TIM) test module is designed to further strengthen the training of the recognition model to cope with the imbalanced test samples by introducing scatter constraints in the mutual information loss function. Experimental results show that the method proposed in this paper achieves an average recognition accuracy of 93.62% after incremental learning for four types of single jamming and seven types of compound jamming under imbalance test conditions. The method realizes fast and dynamic recognition of compound jamming outside the library under multiple combination conditions by fully extracting the knowledge of multiple types of single jamming in the library.
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表 1 雷达干扰参数
信号 参数 数值范围 LFM 信号宽度
带宽
采样频率10 μs
50 μs
125 MHzSMSP 干扰个数 3~7 SNJ 转发个数
切片长度
占空比3~5
10 μs
0.5~0.8DFTJ 假目标个数
假目标时延3~7
1~10 μsMISRJ 转发个数
切片长度
占空比3~5
10 μs
0.5~0.8表 2 增量干扰数据集配置
阶段 序号 干扰名称 训练样本个数 单次测试样本个数 基础 1 SMSP 100 1 2 SNJ 100 2 3 DFTJ 100 2 4 MISRJ 100 8 增量 5 SNJ+SMSP 5 12 6 SMSP+MISRJ 5 5 7 DFTJ+MISRJ 5 9 8 SMSP+DFTJ 5 5 9 SNJ+DFTJ 5 12 10 SNJ+MISRJ 5 7 11 SNJ+SMSP+DFTJ 5 14 表 3 TIM模块对模型的影响(%)
测试样本分布 TIM 基础阶段准确率 增量阶段准确率 平均准确率 性能下降率 1 2 3 4 5 6 7 8 均衡 √ 100.00 100.00 97.95 97.60 96.61 95.72 94.88 94.62 97.17 5.38 $ \times $ 100.00 97.42 92.94 91.70 90.42 89.21 87.61 85.72 91.87 14.28 非均衡 √ 100.00 100.00 98.95 97.60 96.61 94.72 93.88 93.62 96.92 6.38 $ \times $ 99.85 95.22 93.71 87.83 86.44 84.72 72.51 80.66 87.61 19.19 表 4 在1-way 5-shot设置下不同方法的干扰识别结果
方法 基础阶段准确率(%) 增量阶段准确率(%) 平均准确率
(%)性能下降率
(%)训练平均时间
(min)测试平均时间
(s)1 2 3 4 5 6 7 8 Ft-CNN[8] 100.00 92.22 83.33 74.16 67.77 61.33 55.55 51.55 73.23 48.45 50.59 15.73 iCaRL[24] 100.00 91.14 85.72 85.83 83.51 82.66 80.75 78.47 86.01 21.53 185.15 20.79 TOPIC[16] 99.31 89.77 83.54 84.91 84.67 81.03 78.75 75.98 84.74 23.33 141.96 19.86 FACT[26] 97.55 97.77 93.80 92.57 91.29 88.74 85.45 83.12 91.28 14.43 98.15 12.02 CEC[23] 98.44 96.74 93.94 92.70 90.61 88.72 85.88 84.62 91.45 13.82 169.64 16.95 F2M[25] 100.00 95.71 93.44 87.70 86.41 84.72 82.45 80.74 88.89 19.26 78.96 9.93 本文(ZMILN) 100.00 100.00 98.95 97.60 96.61 94.72 93.88 93.62 96.92 6.38 62.45 36.95 -
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