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: 2024-11-07

Available Online: 2024-11-13

Abstract

Abstract

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 capture the features of out-of-database compound jamming signals. Subsequently, a Zero-Memory Incremental Learning Network (ZMILN) is developed, which incorporates hyperdimensional space and cosine similarity techniques. This network facilitates the mapping and storage of prototype vectors for compound jamming signals, thereby enabling the dynamic updating of the recognition model. To address the challenges associated with imbalanced test sample distributions, a Transductive Information Maximization (TIM) testing module is introduced. This module integrates divergence constraints into the mutual information loss function, refining the recognition model to optimize its performance across imbalanced datasets. The implementation begins with a comprehensive modeling of radar active jamming signals. Linear Frequency Modulation (LFM) signals, frequently utilized in contemporary radar systems, are chosen as the foundation for the transmitted radar signals. The received signals are modeled as a blend of target echo signals, jamming signals, and noise. Various categories of radar active jamming, including suppression jamming and deceptive jamming, are classified, and their composite forms are examined. For feature extraction, a five-layer Convolutional Neural Network (CNN) is employed. This CNN is specifically designed to transform input radar jamming time-frequency image samples into a hyperdimensional feature space, generating 512-dimensional prototype vectors. These vectors are then stored within the prototype space, with each jamming category corresponding to a distinct prototype vector. To enhance classification accuracy and efficiency, a quasi-orthogonal optimization strategy is utilized to improve the spatial arrangement of these prototype vectors, thereby minimizing overlap and confusion between different categories and increasing the precision of jamming signal recognition. The ZMILN framework addresses two primary challenges in recognizing compound jamming signals: the scarcity of new-category samples and the limitations inherent in existing models when it comes to identifying novel categories. By integrating prototypical learning with hyperdimensional space techniques, the ZMILN enables generalized recognition from in-database single jamming signals to out-of-database compound jamming. To further enhance model performance in the face of imbalanced sample conditions, the TIM module maximizes information gain by partitioning the test set into supervised support and unsupervised query sets. The ZMILN model is subsequently fine-tuned using the support set, followed by unsupervised testing on the query set. During the testing phase, the model computes the cosine similarity between the test samples and the prototype vectors, ultimately yielding the final recognition results. Results and Discussions: The proposed method exhibits notable effectiveness in the recognition of radar compound active jamming signals. Experimental results indicate an average recognition accuracy of 93.62% across four single jamming signals and seven compound jamming signals under imbalanced test conditions. This performance significantly exceeds various baseline incremental learning methods, highlighting the superior capabilities of the proposed approach in the radar jamming recognition task. Additionally, t-distributed Stochastic Neighbor Embedding (t-SNE) visualization experiments present the distribution of jamming features at different stages of incremental learning, further confirming the method’s effectiveness and robustness. The experiments simulate a realistic radar jamming recognition scenario by categorizing “in-database” jamming as single types included in the base training set, and “out-of-database” jamming as novel compound types that emerge during the incremental training phase. This configuration closely resembles real-world operational conditions, where radar systems routinely encounter new and evolving jamming techniques. Quantitative performance metrics, including accuracy and performance degradation rates, are utilized to assess the model’s capacity to retain knowledge of previously learned categories while adapting to new jamming types. Accuracy is computed at each incremental learning stage to evaluate the model’s performance on both old and new categories. Furthermore, the performance degradation rate is calculated to measure the extent of knowledge retention, with lower degradation rates indicative of stronger retention of prior knowledge throughout the learning process. Conclusions: In conclusion, the proposed Zero-Memory Incremental Learning method for recognizing radar compound active jamming is highly effective in addressing the challenges posed by rapidly evolving and complex radar jamming techniques. By leveraging a comprehensive understanding of individual jamming signals, this method facilitates swift and dynamic recognition of out-of-database compound jamming across diverse and high-dynamic conditions. This approach not only enhances the radar system’s capabilities in recognizing novel compound jamming but also effectively mitigates performance degradation resulting from imbalanced sample distributions. Such advancements are essential for improving the adaptability and reliability of radar systems in complex electronic warfare environments, where the nature of jamming signals is in constant flux. Additionally, the proposed method holds significant implications for other fields facing incremental learning challenges, particularly those involving imbalanced data and rapidly emerging categories. Future research will focus on exploring open-set recognition models, further enhancing the cognitive recognition capabilities of radar systems in fully open and highly dynamic adversarial environments. This work lays the groundwork for developing more agile cognitive closed-loop recognition systems, ultimately contributing to more resilient and adaptable radar systems capable of effectively managing complex electronic warfare scenarios.
- Radar active jamming,
- Zero-memory incremental learning,
- Imbalance,
- Transductive Information Maximization(TIM),
- Compound jamming recognition

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References(26)

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