Compound Active Jamming Recognition for Zero-memory Incremental Learning

WU Zhenhua; CUI Jinxin; CAO Yice; ZHANG Qiang; ZHANG Lei; YANG Lixia

doi:10.11999/JEIT240521

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WU Zhenhua, CUI Jinxin, CAO Yice, ZHANG Qiang, ZHANG Lei, YANG Lixia. Compound Active Jamming Recognition for Zero-memory Incremental Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240521

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WU Zhenhua, CUI Jinxin, CAO Yice, ZHANG Qiang, ZHANG Lei, YANG Lixia. Compound Active Jamming Recognition for Zero-memory Incremental Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240521

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Compound Active Jamming Recognition for Zero-memory Incremental Learning

doi: 10.11999/JEIT240521

WU Zhenhua^{1, 2},
CUI Jinxin¹,
CAO Yice^{1
,
,},
ZHANG Qiang³,
ZHANG Lei⁴,
YANG Lixia¹

1.
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China
2.
Thirty-eighth Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China
3.
National Key Laboratory of Space Integrated Information System, Beijing 100094, China
4.
School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Funds: The National Natural Science Foundation of China (62201007, 62401007), China Postdoctoral Science Foundation (2020M681992), The Natural Science Foundation of Anhui (2308085QF199)

Received Date: 2024-06-25
Rev Recd Date: 2024-11-08

Available Online: 2024-11-13

Abstract

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.
- Radar active jamming,
- Zero-memory incremental learning,
- Imbalance,
- Transductive Information Maximization(TIM),
- Compound jamming recognition

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References(26)

References

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