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Fast sparse adversarial attack for synthetic aperture radar target recognition

Journal of Applied Remote Sensing 2025年第1期19卷

作者： Wan, Xuanshen Liu, Wei Niu, Chaoyang Lu, Wanjie Li, Yuanli PLA Information Engineering University Institute of Data and Target Engineering Zhengzhou China

With the rapid development of artificial intelligence technology, deep learning has achieved significant advantages in synthetic aperture radar automatic target recognition (SAR-ATR). However, previous research showed that the addition of small perturbations not easily detected by the human eye can lead to SAR-ATR model recognition errors;that is, they are affected by adversarial attacks. To solve the problem of long computation time in existing SAR sparse adversarial attack algorithms, we propose a SAR fast sparse adversarial attack (FSAA) algorithm. First, an end-to-end sparse adversarial attack framework is developed based on the lightweight generator ResNet model using two different upsampling modules to control the amplitude and position of the adversarial perturbation. A loss function for the generator is then constructed, which mainly consists of the linear addition of the attack loss, the amplitude distortion loss, and the sparsity loss. Finally, the SAR image is mapped through the trained generator model in a one-step process to generate sparse adversarial perturbations quickly and effectively. Compared with the existing SAR sparse adversarial attack algorithm, the experimental results show that the generation speed of the proposed method is at least 30 times higher when the perturbation is less than 0.05% of the pixels in the entire image, and the recognition rate of the model is >13%. © The Authors.

关键词： Deep learning

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Distributed Optical Fiber Intrusion Identification Algorithm Based on Deep Learning 2

Distributed Optical Fiber Intrusion Identification Algorithm...

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2nd International Conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2023

作者： Yang, Nachuan Zhao, Yongjun Pla Strategic Support Force Information Engineering University Data and Target Engineering Institute Zhengzhou450001 China

ISBN: (纸本)9798350324358

Optical fiber intrusion presents new features of being more subtle, advanced and frequent, which puts forward higher requirements for optical fiber intrusion detection. Distributed optical fiber intrusion location detection technology has the advantages of long detection distance, anti-electromagnetic interference and easy maintenance, and has become one of the most promising technologies in the field of long-distance security detection. In this paper, the distributed optical fiber intrusion identification algorithm based on DL(Deep learning) is studied. With the help of DL technology, the spatial and temporal characteristics of traffic data are considered comprehensively. Firstly, CNN (Convolutional Neural Network) is used to extract the spatial features of traffic data, and then BPNN (BP neural network) is used to extract the temporal features of traffic data. By stacking CNN+BPNN modules and gradually increasing the learning granularity, the purpose of effectively extracting both spatial features and temporal features is achieved. The research results show that the distributed optical fiber intrusion identification algorithm based on CNN+BPNN can identify common intrusion events, and the recognition rate is as high as 93%. The algorithm in this paper is suitable for all three kinds of optical fiber artificial signals, and the feature extraction time is reduced by about 25% compared with the original algorithm. The validity and superiority of the distributed optical fiber intrusion identification algorithm based on CNN+BPNN proposed in this paper are verified. © 2023 IEEE.

关键词： Feature extraction

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A knowledge-based approach for estimating the distribution of urban mixed land use

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International Journal of Digital Earth 2023年第1期16卷 965-987页

作者： Jing Li Haiyan Liu Jia Li Xiaohui Chen Zekun Tao Institute of Data and Target Engineering PLA Strategic Support Force Information Engineering UniversityZhengzhouPeople's Republic of China Piesat Information Technology Co. LtdHangzhouPeople's Republic of China

Estimating the proportion of land-use types in different regions is essential to promote the organization of a compact city and reduce energy ***,existing research in this area has a few limitations:(1)lack of consideration of land-use distribution-related factors other than POIs;(2)inability to extract complex relations from heterogeneous information;and(3)overlooking the correlation between land-use *** overcome these limitations,we propose a knowledge-based approach for estimating land-use *** designed a knowledge graph to display POIs and other related heterogeneous data and then utilized a knowledge embedding model to directly obtain the region embedding vectors by learning the complex and implicit relations present in the knowledge *** embedding vectors were mapped to land-use distributions using a label distribution learning method integrating the correlation between land-use *** prove the reliability and validity of our approach,we conducted a case study in Jinhua,*** results indicated that the proposed model outperformed other algorithms in all evaluation indices,thus illustrating the potential of this method to achieve higher accuracy land-use distribution estimates.

关键词： Knowledge graph land-use distribution region embedding label distribution learning label correlation

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Fast Recognition of Distributed Fiber Optic Vibration Sensing Signal based on Machine Vision in High-speed Railway Security 23

Fast Recognition of Distributed Fiber Optic Vibration Sensin...

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8th International Conference on Multimedia and Image Processing, ICMIP 2023

作者： Yang, Nachuan Zhao, Yongjun Wang, Fuqiang Data and Target Engineering Institute Pla Strategic Support Force Information Engineering University Zhengzhou450001 China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou450001 China

ISBN: (纸本)9781450399586

Accurate and effective identification of multi-vibration events detected based on the phase-sensitive optical time-domain reflectometer (φ-OTDR) is an effective method to achieve precise alarm. This study proposes a real-time classification method of φ-OTDR multi-vibration events based on the combination of convolutional neural network (CNN), bi-directional long short-term memory network (Bi-LSTM) and connectionist temporal classification (CTC), which can quickly and effectively identify the type and number of vibrations contained in the data image when multiple vibration signals are present in a single image, and manual alignment is not required for model training. Noncoherent integration and pulse cancellers are used for raw signal processing to generate spatio-temporal images. CNN is used to extract spatial dimensional features in spatio-temporal images, Bi-LSTM extracts temporal dimensional correlation features, and the hybrid features are automatically aligned with the labels by CTC. A dataset of 8,000 vibration images containing 17,589 abnormal vibration events is collected for model training, validation and testing. Experiments show that the recognition model C3B3 trained with this method can achieve 210 FPS and 99.62% F1 score on the test set. The system can achieve the real-time classification of multiple vibration targets at the perimeter of high-speed railway and effectively reduce the false alarm rate of the system. © 2023 ACM.

关键词： Long short-term memory

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GeoVoronoi: A Voronoi Diagram Generation System for Large-scale Geographical Point data via Spatial Attribute Association

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IEEE Transactions on Big data 2024年

作者： Zhou, Zhiguang Wang, Haoxuan Yang, Zhendong Chen, Yuanyuan Chen, Xiaohui Lai, Ying Big Data Visualization and Human Computer Collaborative Intelligent Laboratory China Zhejiang University of Technology China Information Engineering University Institute of Data and Target Engineering China Zhejiang Academy of Marine Sciences China

Voronoi diagram is commonly used to visualize geographical point dataset with a collection of plane-partitioned facets. As the size of the geographical point dataset increases, facets are densely distributed, and present different sizes and irregular shapes, leading to overdrawing and confusion problems, and hampering the visual perception of Voronoi diagram and insightful exploration of geographical point data. In this paper, we propose a novel Voronoi diagram generation framework to visualize and explore large-scale geographical point datasets. Firstly, an attribute-based blue noise sampling model is designed to select a subset of points to generate the simplified Voronoi diagram, retaining both the spatial distribution and attribute relationship of the original large-scale geographical points. Then a couple of optimization schemes are integrated into the sampling model to replace the representative points, aiming to enhance the visual perception of Voronoi diagram, such as shape balance and color characterization. Furthermore, we implement an interactive online Voronoi diagram generation tool, GeoVoronoi, enabling users to generate meaningful facets according to their requirements. Quantitative comparisons, case studies and user studies based on real-world datasets have demonstrated the effectiveness of our proposed method in the generation of credible Voronoi diagram and in-depth exploration of geographical point datasets. © 2024 IEEE.

关键词： data visualization

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Distributed Optical Fiber Intrusion Identification Algorithm Based on Deep Learning

Distributed Optical Fiber Intrusion Identification Algorithm...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), International Conference on

作者： Nachuan Yang Yongjun Zhao Data and Target Engineering Institute PLA Strategic Support Force Information Engineering University Zhengzhou China

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Visualization and visual analysis of vessel trajectory data: A survey

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Visual Informatics 2021年第4期5卷 1-10页

作者： Haiyan Liu Xiaohui Chen Yidi Wang Bing Zhang Yunpeng Chen Ying Zhao Fangfang Zhou Institute of Data and Target Engineering Information Engineering UniversityZhengzhouChina School of Computer Science and Engineering Central South UniversityChangshaChina Rail Data Research and Application Key Laboratory of Hunan Province ChangshaChina

Maritime transports play a critical role in international trade and *** vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial–temporal patterns of vessel *** and understanding these patterns are valuable for maritime traffic surveillance and *** essential techniques in complex data analysis and understanding,visualization and visual analysis have been widely used in vessel trajectory data *** paper presents a literature review on the visualization and visual analysis of vessel trajectory ***,we introduce commonly used vessel trajectory data sets and summarize main operations in vessel trajectory data ***,we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in ***,we expound on the prospects of the remaining challenges and directions for future research.

关键词： Maritime traffic Vessel trajectory data Automatic identification system Visualization and visual analysis

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Moving target localization for multistatic passive radar using delay, Doppler and Doppler rate measurements

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Journal of Systems engineering and Electronics 2020年第5期31卷 939-949页

作者： ZHAO Yongsheng HU Dexiu ZHAO Yongjun LIU Zhixin Data and Target Engineering Institute PLA Strategic Support Force Information Engineering UniversityZhengzhou 450001China

Time delay and Doppler shift between the echo signal and the reference signal are two most commonly used measurements in target localization for the passive radar. Doppler rate, which can be obtained from the extended cross ambiguity function, offers an opportunity to further enhance the localization accuracy. This paper considers using the measurement Doppler rate in addition to measurements of time delay and Doppler shift to locate a moving target. A closed-form solution is developed to accurately and efficiently estimate the target position and *** proposed solution establishes a pseudolinear set of equations by introducing some additional variables, imposes weighted least squares formulation to yield a rough estimate, and utilizes the function relation among the target location parameters and additional variables to improve the estimation accuracy. Theoretical covariance and Cramer-Rao lower bound(CRLB) are derived and compared, analytically indicating that the proposed solution attains the CRLB. Numerical simulations corroborate this analysis and demonstrate that the proposed solution outperforms existing methods.

关键词： target localization multistatic passive radar time delay Doppler shift Doppler rate

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Ontology knowledge reasoning method for multi-source intelligent road selection

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Cehui Xuebao/Acta Geodaetica et Cartographica Sinica 2022年第2期51卷 279-289页

作者： Guo, Xuan Qian, Haizhong Wang, Xiao Liu, Junnan Ren, Yan Zhao, Yuzhe Chen, Guoqing Institute of Geospatial Information Information Engineering University Zhengzhou450000 China Institute of Data and Target Engineering Information Engineering University Zhengzhou450000 China

In the era of big data, multi-source data is increasing. However, there is a semantic inconsistency among multi-source data, we propose an intelligent road selection method based on ontology knowledge reasoning. In this paper, we use basic scale map as basic case and use navigation data and OSM data as experimental data. Features such as grade, length, degree, closeness and betweenness are calculated based on road stroke, and their concepts are extracted to construct a road selection ontology. In order to correlate basic case with experimental data, conceptual similarity is calculated from semantic feature and numerical feature. Then, ontology and semantic web rule language are used to define road selection rules and reason the process knowledge of cartographic generalization, which realize the automatic selection of multi-source road data. The experiments indicate that our method can effectively eliminate the semantic inconsistency among multi-source data to realize the road intelligent selection in similar areas. © 2022, Surveying and Mapping Press. All right reserved.

关键词： Ontology

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Multipath number estimation based on QR decomposition 2

Multipath number estimation based on QR decomposition

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2020 2nd International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2020

作者： Gao, Xiangying Liu, Zhixin Zhao, Yongjun Liu, Chengcheng School of Data and Target Engineering PLA Information Engineering University Zhengzhou Henan450001 China Institute of Systems Engineering Academy of Military SciencesPLA Beijing100141 China

Accurate multipath number estimation is the prerequisite for MUSIC algorithm to achieve excellent superresolution performance. To solve the problem that the existing multipath number estimation algorithms based on eigenvalue distribution usually require a large amount of computation, this paper proposed a fast multipath number estimation algorithm based on QR decomposition under the OFDM signal model. This algorithm only needs few times QR iteration decomposition to obtain the pseudo-eigenvalues of the matrix, and uses them to estimate the multipath number through two-step comparison. Due to the avoidance of accurate eigenvalues solution, compared with the traditional algorithms based on eigenvalue distribution, proposed algorithm's computation is smaller. Simulation results showed that the proposed algorithm has better detection performance in low SNR and few snapshots than existing algorithms. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Multiple signal classification

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