检索结果-南通市图书馆

Chinese Journal of Aeronautics 2024年第5期37卷 349-362页

作者： Zhengjie LI Yujie WEI Junwei XIE Haowei ZHANG Yu TIAN air and missile Defense College Air Force Engineering UniversityXi’an 710051China air force Xi’an Flying College Xi’an 710300China air and missile Defense institute Air Force Research InstituteBeijing 100085China School of Economics and Management Beihang UniversityBeijing 100191China

This paper investigates the problem of Joint Radar Node Selection and Power Allocation(JRNSPA)in the Multiple Radar System(MRS)in the blanket jamming *** radar node independently tracks moving target and subsequently transmits the raw observation data to the fusion center,which formulates a centralized tracking network *** order to establish a practical blanket jamming environment,we suppose that each target carries the self-defense jammer which automatically implements blanket jamming to the radar nodes that exceed the preset interception ***,the Predicted Conditional Cramer-Rao Lower Bound(PC-CRLB)is derived and utilized as the tracking accuracy *** at ensuring both the tracking performance and the Low Probability of Intercept(LPI)performance,the resource-saving scheduling model is formulated to minimize the transmit power consumption while meeting the requirements of tracking ***,the Modified Zoutendijk Method Of Feasible Directions(MZMFD)-based two-stage solution technique is adopted to solve the formulated non-convex optimization *** results show the effectiveness of the proposed JRNSPA scheme.

关键词： Multiple Radar System(MRS) Centralized target tracking Resource scheduling Blanket jamming Low Probability of Intercept(LPI)

MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge

在线全文

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第9期140卷 2559-2586页

作者： Tengda Li Gang Wang Qiang Fu College of air and missile Defense Air Force Engineering UniversityXi’an710051China

Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinforce-ment learning(DRL)theory and an improved Multi-Agent Deep Deterministic Policy Gradient(MADDPG-D2)algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of *** algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm,and considers the introduction of a double noise mechanism to increase the action exploration space in the early stage of the algorithm,and the introduction of a double experience pool to improve the data utilization rate;at the same time,in order to accelerate the training speed and efficiency of the agents,and to solve the cold-start problem of the training,the a priori knowledge technology is applied to the training of the ***,the MADDPG-D2 algorithm is compared and analyzed based on the digital battlefield of ground and air *** experimental results show that the agents trained by the MADDPG-D2 algorithm have higher win rates and average rewards,can utilize the resources more reasonably,and better solve the problem of the traditional single agent algorithms facing the difficulty of solving the problem in the high-dimensional decision *** MADDPG-D2 algorithm based on multi-agent architecture proposed in this paper has certain superiority and rationality in DTA.

关键词： Deep reinforcement learning dynamic task allocation intelligent decision-making multi-agent system MADDPG-D2 algorithm

Simplistic framework of single-pixelprogrammable metasurfaces integrated with a capsuled LED array

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Photonics Research 2024年第5期12卷 884-894页

作者： YUXI LI JIAFU WANG SAI SUI RUICHAO ZHU YAJUAN HAN HONGYA CHEN XINMIN FU SHAOJIE WANG CUNQIAN FENG SHAOBO QU Shaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices Air Force Engineering UniversityXi’an 710051China air and missile Defense College Air Force Engineering UniversityXi’an 710051China

Coding metasurfaces can manipulate electromagnetic wave in real time with high degree of freedom,the fascinating properties of which enrich the metasurface design with a wide range of application ***,most of the coding metasurfaces are designed based on external excitation framework with the wired electrical or wireless light control devices,thus inevitably causing the interference with electromagnetic wave transmission and increasing the complexity of the metasurface *** this work,a simplistic framework of single-pixel-programmable metasurfaces integrated with a capsuled LED array is proposed to dynamically control electromagnetic *** framework fully embeds the photoresistor in the meta-atom,controlling the LED array to directly illuminate the photoresistor to modulate the phase *** this manner,the complex biasing network is transformed to the universal LED array,which means the physical control framework can be transformed to a software framework,and thus the functions of the metasurface can be freely manipulated by encoding the capsuled LED array avoiding mutual coupling of adjacent meta-atoms in real *** the results verify that the far-field scattering pattern can be customized with this singlepixel-programmable ***,this work provides a universal framework for coding metasurface design,which lays the foundation for metasurface intelligent perception and adaptive modulation.

关键词： surface. transformed mutual

Ballistic target recognition based on multiple data representations and deep-learning algorithms

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2024年第6期37卷 167-181页

作者： Lixun HAN Cunqian FENG Xiaowei HU Sisan HE Xuguang XU air and missile Defense College Air Force Engineering UniversityXi’an 710051China College of Information and Communication National University of Defense TechnologyWuhan 430010China

Target recognition is a significant part of a Ballistic missile Defense System(BMDS).However,most existing ballistic target recognition methods overlook the impact of data representation on recognition *** paper focuses on systematically investigating the influences of three novel data representations in the Range-Doppler(RD)***,the Radar Cross Section(RCS)and micro-Doppler(m-D)characteristics of a cone-shaped ballistic target are ***,three different data representations are proposed:RD data,RD sequence tensor data,and RD trajectory *** accommodate various data inputs,deep-learning models are designed,including a two-Dimensional Residual Dense Network(2D RDN),a three-Dimensional Residual Dense Network-Gated Recurrent Unit(3D RDN-GRU),and a Dynamic Trajectory Recognition Network(DTRN).Finally,an Electromagnetic(EM)computation dataset is collected to verify the performances of the networks.A broad range of experimental results demonstrates the effectiveness of the proposed ***,several key parameters of the proposed networks and datasets are extensively studied in this research.

关键词： Ballistic target Micro-Doppler Deep learning Range-Doppler Radar target recognition

Reduced-complexity multiple parameters estimation via toeplitz matrix triple iteration reconstruction with bistatic MIMO radar

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2024年第7期37卷 482-495页

作者： Chenghong ZHAN Guoping HU Junpeng SHI Fangzheng ZHAO Hao ZHOU Graduate College Air Force Engineering UniversityXi’an 710051China air and missile Defense College Air Force Engineering UniversityXi’an 710051China College of Electronic Countermeasure National University of Defense TechnologyHefei 230037China Beijing Aeronautical Technology Research Center Beijing 100076China

In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output(MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovatively addresses the joint estimation of the Direction of Departure(DOD), Direction of Arrival(DOA), and Doppler frequency for incoherent targets. We propose a novel approach that significantly reduces computational complexity by utilizing the TemporalSpatial Nested Sampling Model(TSNSM). Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom(DOFs) and reorganize the remaining ones. We then employ the Toeplitz matrix triple iteration reconstruction method, surpassing the traditional Temporal-Spatial Smoothing Window(TSSW) approach, to mitigate the single snapshot effect and reduce computational demands. We further refine the highdimensional ESPRIT algorithm for joint estimation of DOD, DOA, and Doppler frequency, eliminating the need for additional parameter pairing. Moreover, we meticulously derive the Cramér-Rao Bound(CRB) for the TSNSM. This signal model allows for a second expansion of DOFs in time and space domains, achieving high precision in target angle and Doppler frequency estimation with low computational complexity. Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures, ensuring higher precision in parameter estimation with less complexity burden.

关键词： MIMO Radar Multipleparameters estimation Temporal-spatial Nested Sampling Multi-linear mapping mechanism Toeplitz matrix triple iteration reconstruction Reduce computational complexity

Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2024年第1期35卷 43-54页

作者： JIAN Tao HE Jia WANG Bencai LIU Yu XU Congan XIE Zikeng Research institute of Information Fusion Naval Aviation UniversityYantai 264001China air force Early Warning Academy Wuhan 430019China

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance *** target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown *** on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,*** of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power *** results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.

关键词： adaptive detection subspace interference constant false alarm rate Gradient test partially homogeneous environment

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第5期79卷 2375-2398页

作者： Zhijun Guo Yun Sun YingWang Chaoqi Fu Jilong Zhong air Traffic Control and Navigation College Air Force Engineering UniversityXi’an710000China institute of Systems Engineering Academy of Military ScienceBeijing100101China Equipment Management and UAV Engineering College Air Force Engineering UniversityXi’an710038China Defense Innovation institute Academy of Military ScienceBeijing100071China

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh *** paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention *** mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network *** with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid *** results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing *** results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art ***,by the ablation study,the contributions of each different improvement are *** proposed model can be used to support resilient network design for real-time mission execution.

关键词： Resilience cooperative mission FANET spatio-temporal node pooling multi-head attention graph network

FedDAA:a robust federated learning framework to protect privacy and defend against adversarial attack

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Computer Science 2024年第2期18卷 107-122页

作者： Shiwei LU Ruihu LI Wenbin LIU Fundamentals Department Air Force Engineering UniversityXi’an 710051China institute of Advanced Computational Science and Technology Guangzhou UniversityGuangzhou 510006China

Federated learning(FL)has emerged to break data-silo and protect clients’privacy in the field of artificial ***,deep leakage from gradient(DLG)attack can fully reconstruct clients’data from the submitted gradient,which threatens the fundamental privacy of *** cryptology and differential privacy prevent privacy leakage from gradient,they bring negative effect on communication overhead or model ***,the original distribution of local gradient has been changed in these schemes,which makes it difficult to defend against adversarial *** this paper,we propose a novel federated learning framework with model decomposition,aggregation and assembling(FedDAA),along with a training algorithm,to train federated model,where local gradient is decomposed into multiple blocks and sent to different proxy servers to complete *** bring better privacy protection performance to FedDAA,an indicator is designed based on image structural similarity to measure privacy leakage under DLG attack and an optimization method is given to protect privacy with the least proxy *** addition,we give defense schemes against adversarial attack in FedDAA and design an algorithm to verify the correctness of aggregated *** results demonstrate that FedDAA can reduce the structural similarity between the reconstructed image and the original image to 0.014 and remain model convergence accuracy as 0.952,thus having the best privacy protection performance and model training *** importantly,defense schemes against adversarial attack are compatible with privacy protection in FedDAA and the defense effects are not weaker than those in the traditional ***,verification algorithm of aggregation results brings about negligible overhead to FedDAA.

关键词： federated learning privacy protection adversarial attacks aggregated rule correctness verification

MUS Model:A Deep Learning-Based Architecture for IoT Intrusion Detection

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第7期80卷 875-896页

作者： Yu Yan Yu Yang Shen Fang Minna Gao Yiding Chen College of Information Engineering University of Engineering of the Chinese People’s Armed Police Force(PAP)Xi’an710000China College of missile Engineering Rocket Force Engineering UniversityXi’an710000China

In the face of the effective popularity of the Internet of Things(IoT),but the frequent occurrence of cybersecurity incidents,various cybersecurity protection means have been proposed and *** them,Intrusion Detection System(IDS)has been proven to be stable and ***,traditional intrusion detection methods have shortcomings such as lowdetection accuracy and inability to effectively identifymalicious *** address the above problems,this paper fully considers the superiority of deep learning models in processing highdimensional data,and reasonable data type conversion methods can extract deep features and detect classification using advanced computer vision techniques to improve classification *** TransformField(MTF)method is used to convert 1Dnetwork traffic data into 2D images,and then the converted 2D images are filtered by UnsharpMasking to enhance the image details by sharpening;to further improve the accuracy of data classification and detection,unlike using the existing high-performance baseline image classification models,a soft-voting integrated model,which integrates three deep learning models,MobileNet,VGGNet and ResNet,to finally obtain an effective IoT intrusion detection architecture:the MUS *** types of experiments are conducted on the publicly available intrusion detection dataset CICIDS2018 and the IoT network traffic dataset N_BaIoT,and the results demonstrate that the accuracy of attack traffic detection is greatly improved,which is not only applicable to the IoT intrusion detection environment,but also to different types of attacks and different network environments,which confirms the effectiveness of the work done.

关键词： Cyberspace security intrusion detection deep learning Markov Transition Fields(MTF) soft voting integration

A unified intelligent control strategy synthesizing multi-constrained guidance and avoidance penetration

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2024年第7期67卷 329-330页

作者： Sibo ZHAO Jianwen ZHU Weimin BAO Xiaoping LI School of Aerospace Science and Technology Xidian University College of missile Engineering Rocket Force University of Engineering China Aerospace Science and Technology Corporation

Satisfying various terminal constraints and achieving evasive flight in no-flying-zones (NFZs) are two important flight missions undertaken by high velocity vehicles. We studied an intelligent maneuvering guidance strategy using deep reinforcement learning (DRL) methods to achieve the coordination and unification of evasive flight and multi-constrained guidance with minimal energy loss.

关键词： Energy dissipation