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Deep radio signal clustering with interpretability analysis based on saliency map

Digital Communications and Networks 2024年第5期10卷 1448-1458页

作者： Huaji Zhou Jing Bai Yiran Wang Junjie Ren Xiaoniu Yang Licheng Jiao School of Artificial Intelligence Xidian UniversityXi’an710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033China

With the development of information technology,radio communication technology has made rapid *** radio signals that have appeared in space are difficult to classify without manually *** radio signal clustering methods have recently become an urgent need for this ***,the high complexity of deep learning makes it difficult to understand the decision results of the clustering models,making it essential to conduct interpretable *** paper proposed a combined loss function for unsupervised clustering based on *** combined loss function includes reconstruction loss and deep clustering *** clustering loss is added based on reconstruction loss,which makes similar deep features converge more in feature *** addition,a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency *** experiments have been conducted on a modulated signal dataset,and the results indicate the superior performance of our proposed method over other clustering *** particular,for the simulated dataset containing six modulation modes,when the SNR is 20dB,the clustering accuracy of the proposed method is greater than 78%.The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.

关键词： Unsupervised radio signal clustering Autoencoder Clustering features visualization Deep learning interpretability

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Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

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Computer Modeling in Engineering & sciences 2024年第4期139卷 279-296页

作者： Hui Zhang Huaji Zhou Li Wang Feng Zhou The Ministry Key Laboratory of Electronic Information Countermeasure and Simulation Xidian UniversityXi’an710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033China

This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open *** spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center *** designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during ***,this method allows unknown classes to occupy a larger space in the feature *** reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more ***,the feature comparator threshold can be used to reject unknown *** signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world *** experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open ***,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

关键词： Electromagnetic signal recognition deep learning feature extraction open set recognition

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Teachers cooperation:team-knowledge distillation for multiple cross-domain few-shot learning

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Frontiers of Computer science 2023年第2期17卷 91-99页

作者： Zhong JI Jingwei NI Xiyao LIU Yanwei PANG School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308China Tianjin Key Laboratory of Brain-Inspired Intelligence Technology Tianjin 300072China

Although few-shot learning(FSL)has achieved great progress,it is still an enormous challenge especially when the source and target set are from different domains,which is also known as cross-domain few-shot learning(CD-FSL).Utilizing more source domain data is an effective way to improve the performance of ***,knowledge from different source domains may entangle and confuse with each other,which hurts the performance on the target ***,we propose team-knowledge distllation networks(TKD-Net)to tackle this problem,which explores a strategy to help the cooperation of multiple ***,we distill knowledge from the cooperation of teacher networks to a single student network in a meta-learning *** incorporates task-oriented knowledge distillation and multiple cooperation among teachers to train an efficient student with better generalization ability on unseen ***,our TKD-Net employs both response-based knowledge and relation-based knowledge to transfer more comprehensive and effective *** experimental results on four fine-grained datasets have demonstrated the effectiveness and superiority of our proposed TKD-Net approach.

关键词： cross-domain few-shot learning meta-learning knowledge distillation multiple teachers

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Open World Recognition of Communication Jamming Signals

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China Communications 2023年第6期20卷 199-214页

作者： Yan Tang Zhijin Zhao Jie Chen Shilian Zheng Xueyi Ye Caiyi Lou Xiaoniu Yang School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China

To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is *** algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new *** network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known *** the one hand,the network is required to have the ability to distinguish whether two samples are from the same *** the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set *** the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known *** simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.

关键词： communication jamming signals Siamese Neural Network Open World Recognition unsupervised clustering of new jamming type Gaussian probability density function

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A new multiple targets tracking algorithm based on improved MHT algorithm

A new multiple targets tracking algorithm based on improved ...

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2022 International Conference on Computer Engineering and Artificial Intelligence, ICCEAI 2022

作者： Sun, Wei Han, Yu Science and Technology on Commuication Information Security Control Laboratory No.36 Research Institute of CETC Jiaxing314033 China

ISBN: (数字)9781665468039

ISBN: (纸本)9781665468039

In this paper, a bearing pretreatment method suitable for passive sonar tracking is proposed, when the ocean background noise fluctuation is large, the weak target can also be detected and tracked through the adaptive adjustment of the threshold. The principle and characteristics of the conventional multi-hypothesis tracking algorithm is explored in this paper, we propose a bearing information pre-processing method suitable for passive sonar tracing and an improved MHT method based on Kalman prediction framework. Finally, automatic tracking problem with multi-Targets based on bearing crossing and ocean background fluctuations can be achieved. The simulation data proves that this method is reliable and practical. © 2022 IEEE.

关键词： Target tracking

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Incremental Learning of Radio Modulation Classification Based on Sample Recall

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China Communications 2023年第7期20卷 258-272页

作者： Yan Zhao Shichuan Chen Tao Chen Weiguo Shen Shilian Zheng Zhijin Zhao Xiaoniu Yang School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China Institute of Cyberspace Security Zhejiang University of TechnologyHangzhou 310023China

Radio modulation classification has always been an important technology in the field of *** difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old *** this paper,we propose a sample memory and recall framework for incremental learning of radio modulation *** data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for *** compare the performance of our proposed method with three baselines through a large number of simulation *** show that our method achieves far higher classification accuracy than finetuning method and feature extraction ***,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.

关键词： radio modulation classification incremen-tal learning deep learning convolutional neural net-work.

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Modulation technology in Cross-Domain Magnetic Induction Communication 14

Modulation Technology in Cross-Domain Magnetic Induction Com...

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14th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2024

作者： Cheng, Ruoxue Zhang, Xin Qi, Xiaoming Zhang, Xiaoji Ge, Yizhou School of Marine Science and Technology Northwestern Polytechnical University Xi'an China Science and Technology on Communication Information Security Control Laboratory Jiangxi China

ISBN: (纸本)9798350366556

The research on technology of cross-domain communication, which spans air and sea domains, has been widely concerned. As an emerging cross-domain communication technology, magnetic induction (MI) communication boasts unique advantages such as stable channels, minimal susceptibility to the dynamic ocean environment, and low propagation delay. However, it also has limitations in terms of transmission distance×data rate. In this paper, the modulation technology in cross-domain magnetic induction communication is studied, and the very minimum shift keying (VMSK/2) coding modulation scheme based on direct sequence spread spectrum (DSSS) technology is proposed. VMSK modulation is used to adapt to the severely bandwidth-limited underwater magnetic induction channel, and DSSS technology is used to enhance the detection ability of weak magnetic induction signal, thereby increasing the water entry depth of cross-domain communication. In this paper, the scheme of VMSK/2-DSSS code modulation is given, and the simulation analysis and field trial are carried out. The simulation and lake test results show that compared to magnetic induction communication without DSSS, the VMSK/2-DSSS coding modulation scheme can achieve a greater water entry depth, enabling error-free cross-domain magnetic induction communication at a depth of 40 meters and a data rate of 100bps. © 2024 IEEE.

关键词： Signal modulation

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Contrastive Clustering for Unsupervised Recognition of Interference Signals

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Computer Systems science & Engineering 2023年第8期46卷 1385-1400页

作者： Xiangwei Chen Zhijin Zhao Xueyi Ye Shilian Zheng Caiyi Lou Xiaoniu Yang The School of Communication Engineering Hangzhou Dianzi UniversityHangzhou310018China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314001China

Interference signals recognition plays an important role in anti-jamming *** the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition ***,there is no unsupervised interference signals recognition algorithm at *** this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is ***,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive *** the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive *** addition,a dynamic entropy parameter strategy is *** simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC *** unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other *** particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 *** addition,our method is close to the supervised learning algorithm.

关键词： Interference signals recognition unsupervised clustering contrastive learning deep learning k-nearest neighbor

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Model-based deep learning for fiber bundle infrared image restoration

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Defence technology（防务技术） 2023年第9期27卷 38-45页

作者： Bo-wen Wang Le Li Hai-bo Yang Jia-xin Chen Yu-hai Li Qian Chen Chao Zuo Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing University of Science and TechnologyNanjingJiangsu Province210094China Smart Computational Imaging(SCI)Laboratory Nanjing University of Science and TechnologyNanjingJiangsu Province210094China Science and Technology on Electro-Optical Information Security Control Lab Tianjin300308China

As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual *** to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is *** the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep *** building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated *** results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated *** the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging *** proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.

关键词： Fiber bundle Deep learning Infrared imaging Image restoration

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Phishing detection on Ethereum via transaction subgraphs embedding

IET Blockchain

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IET Blockchain 2023年第4期3卷 194-203页

作者： Lv, Haifeng Ding, Yong Guangxi Key Laboratory of Machine Vision and Intelligent Control WuZhou University Wuzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology WuZhou University Wuzhou China

With the rapid development of blockchain technology in the financial sector, the security of blockchain is being put to the test due to an increase in phishing fraud. Therefore, it is essential to study more effective measures and better solutions. Graph models have been proven to provide abundant information for downstream assignments. In this study, a graph-based embedding classification method is proposed for phishing detection on Ethereum by modeling its transaction records using subgraphs. Initially, the transaction data of normal addresses and an equal number of confirmed phishing addresses are collected through web crawling. Multiple subgraphs using the collected transaction records are constructed, with each subgraph containing a target address and its nearby transaction network. To extract features of the addresses, a modified Graph2Vec model called imgraph2vec is designed, which considers block height, timestamp, and amount of transactions. Finally, the Extreme Gradient Boosting (XGBoost) algorithm is employed to detect phishing and normal addresses. The experimental results show that the proposed method achieves good performance in phishing detection, indicating the effectiveness of imgraph2vec in feature acquisition of transaction networks compared to existing models. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Graphic methods

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