零記憶增量學(xué)習(xí)的復(fù)合有源干擾識別

吳振華; 崔金鑫; 曹宜策; 張強; 張磊; 楊利霞

doi:10.11999/JEIT240521

零記憶增量學(xué)習(xí)的復(fù)合有源干擾識別

doi: 10.11999/JEIT240521

吳振華^{1, 2},
崔金鑫¹,
曹宜策^1, ,,
張強³,
張磊⁴,
楊利霞¹

1.
安徽大學(xué)信息材料與智能感知安徽省實驗室合肥 230601
2.
中國電子科技集團(tuán)公司第三十八研究所合肥 230088
3.
天基綜合信息系統(tǒng)全國重點實驗室北京 100094
4.
中山大學(xué)電子與通信工程學(xué)院深圳 518107

基金項目: 國家自然科學(xué)基金(62201007, 62401007)，中國博士后科學(xué)基金(2020M681992)，安徽省自然科學(xué)基金(2308085QF199)

詳細(xì)信息

作者簡介:
吳振華：男，副教授，研究方向為雷達(dá)成像、雷達(dá)信號處理、雷達(dá)智能干擾對抗

崔金鑫：男，碩士生，研究方向為雷達(dá)干擾識別、雷達(dá)信號處理

曹宜策：女，講師，研究方向為遙感圖像的智能解譯、雷達(dá)信號處理、計算機(jī)視覺

張強：男，研究員，研究方向為天基信號處理

張磊：男，教授，研究方向為雷達(dá)信號處理

楊利霞：男，教授，研究方向為電磁散射與逆散射、電波傳播及天線理論與設(shè)計、計算電磁學(xué)

通訊作者:
曹宜策　yccao@ahu.edu.cn

中圖分類號: TN974
計量
- 文章訪問數(shù): 202
- HTML全文瀏覽量: 92
- PDF下載量: 35
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-06-25
- 修回日期: 2024-11-07
- 網(wǎng)絡(luò)出版日期: 2024-11-13
- 刊出日期: 2025-01-31

Compound Active Jamming Recognition for Zero-memory Incremental Learning

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)

摘要

摘要: 非完備、高動態(tài)有源干擾對抗作戰(zhàn)環(huán)境下，現(xiàn)階段針對庫內(nèi)多類型單一有源干擾樣本所優(yōu)化訓(xùn)練的靜態(tài)模型，在面對庫外類型多樣、參數(shù)多變、組合方式多元的復(fù)合干擾時，模型無法快速更新且難以應(yīng)對測試樣本數(shù)非均衡問題。針對此問題，該文提出一種基于零記憶增量學(xué)習(xí)的雷達(dá)復(fù)合有源干擾識別方法。首先，利用元學(xué)習(xí)訓(xùn)練模式對庫內(nèi)單一干擾進(jìn)行原型學(xué)習(xí)，訓(xùn)練出高效的特征提取器，使其具備對庫外復(fù)合干擾特征有效提取能力。進(jìn)而，基于超維空間和余弦相似度計算，構(gòu)建零記憶增量學(xué)習(xí)網(wǎng)絡(luò)(ZMILN)，將復(fù)合干擾原型向量映射到超維空間并存儲，從而實現(xiàn)識別模型動態(tài)更新。此外，為解決樣本數(shù)非均衡下復(fù)合干擾識別問題，設(shè)計直推式信息最大化(TIM)測試模塊，通過在互信息損失函數(shù)中加入散度約束，對識別模型進(jìn)一步強化訓(xùn)練以應(yīng)對非均衡測試樣本。實驗結(jié)果表明，該文所提方法在非均衡測試條件下對4種單一干擾和7種復(fù)合干擾進(jìn)行增量學(xué)習(xí)后，平均識別準(zhǔn)確率達(dá)到了93.62%。該方法通過對庫內(nèi)多類型單一干擾知識充分提取，實現(xiàn)對多種組合條件下庫外復(fù)合干擾的快速動態(tài)識別。
- 雷達(dá)有源干擾 /
- 零記憶增量學(xué)習(xí) /
- 非均衡 /
- 直推式信息最大化 /
- 復(fù)合干擾識別
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

HTML全文

圖 1 雷達(dá)回波及干擾信號的時頻譜圖

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圖 2 零記憶增量學(xué)習(xí)網(wǎng)絡(luò)

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圖 3 原型學(xué)習(xí)網(wǎng)絡(luò)詳細(xì)結(jié)構(gòu)圖

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圖 4 原型空間更新示意圖

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圖 5 不同方法下的干擾識別準(zhǔn)確率曲線

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圖 6 1-way 5-shot設(shè)置下的t-SNE可視化圖

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圖 7 不同基礎(chǔ)設(shè)置下的干擾識別性能曲線

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表 1 雷達(dá)干擾參數(shù)

信號	參數(shù)	數(shù)值范圍
LFM	信號寬度帶寬采樣頻率	10 μs 50 μs 125 MHz
SMSP	干擾個數(shù)	3～7
SNJ	轉(zhuǎn)發(fā)個數(shù) 切片長度占空比	3～5 10 μs 0.5～0.8
DFTJ	假目標(biāo)個數(shù) 假目標(biāo)時延	3～7 1～10 μs
MISRJ	轉(zhuǎn)發(fā)個數(shù) 切片長度占空比	3～5 10 μs 0.5～0.8

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表 2 增量干擾數(shù)據(jù)集配置

階段	序號	干擾名稱	訓(xùn)練樣本個數(shù)	單次測試樣本個數(shù)
基礎(chǔ)	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

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表 3 TIM模塊對模型的影響(%)

測試樣本分布	TIM	基礎(chǔ)階段準(zhǔn)確率	增量階段準(zhǔn)確率							平均準(zhǔn)確率	性能下降率
測試樣本分布	TIM	1	2	3	4	5	6	7	8	平均準(zhǔn)確率	性能下降率
均衡	√	100.00	100.00	97.95	97.60	96.61	95.72	94.88	94.62	97.17	5.38
均衡	$ \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
非均衡	$ \times $	99.85	95.22	93.71	87.83	86.44	84.72	72.51	80.66	87.61	19.19

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表 4 在1-way 5-shot設(shè)置下不同方法的干擾識別結(jié)果

方法	基礎(chǔ)階段準(zhǔn)確率(%)	增量階段準(zhǔn)確率(%)							平均準(zhǔn)確率 (%)	性能下降率 (%)	訓(xùn)練平均時間 (min)	測試平均時間 (s)
方法	1	2	3	4	5	6	7	8	平均準(zhǔn)確率 (%)	性能下降率 (%)	訓(xùn)練平均時間 (min)	測試平均時間 (s)
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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