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检索条件"机构=Key Laboratory of Technology on Data link"

共 86 条记录，以下是21-30 订阅

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Semantic Consensus Model and Behavioural Control Model for Visual data link

Semantic Consensus Model and Behavioural Control Model for V...

引用

IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Pengju Guo Junfeng Hu Guobao Hui CETC Key Laboratory of Data Link Technology Xi'an China

The Visual data link is a new type of data link based on vision and artificial intelligence. To address the problems of information interaction, information sharing and behavioural control faced by Visual data link, this thesis proposes a semantic consensus model for Visual data link that can achieve semantic consensus among unmanned platforms and realize the information interaction and information sharing capability of Visual data link in the “spectral domain”; the semantic consensus model and behavioural control model for Visual data link proposed in this study can realize the common sense cognitive understanding and collaborative control of related information resources based on vision. Semantic consensus and behavioural control models can effectively support the development and application of Visual data link.

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Adaptive Topology Control Strategy for Heterogeneous Multichannel data linkNetworks

Adaptive Topology Control Strategy for Heterogeneous Multich...

引用

IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Kai Chi Fuqiang Li Chaoqi Zhang CETC Key Laboratory of Data Link Technology Xi'an Shaanxi China

This paper considers a topology control strategy in the multichannel heterogeneous datalink network, which can meet the connectivity optimization requirements of the network by deploying multimode nodes reasonably and adapting different task priorities. This strategy can improve the performance of data link networks in dynamic scenarios. Finally, simulation results are presented to demonstrate the effectiveness of our proposed algorithm.

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BEBLID-SLAM: An Efficient Feature-Based Monocular SLAM System 37

BEBLID-SLAM: An Efficient Feature-Based Monocular SLAM Syste...

引用

37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Yang, Feng Song, Hongxuan Li, Haotian Jie, Baibing Northwestern Polytechnical University Key Laboratory of Information Fusion Technology Ministry of Education Xi'an710129 China Northwestern Polytechnical University CETC Key Laboratory of Data Link Technology Xi'an China

ISBN: (纸本)9781665465366

The feature matching quality plays an important role in the robustness and location accuracy of feature-based Simultaneous Localization and Mapping (SLAM) system, in which descriptor is significant of tracking and re-localization. In this paper, we propose an efficient feature-based Monocular SLAM system, BEBLID-SLAM, which use BEBLID descriptor for feature matching in ORB-SLAM pipeline. In the proposed system, adopting histogram equalization to preprocess input images and offline training Bag-of-Words for BEBLID is respectively adopted to preprocess input images and realize re-localization and loop closure. Moreover, we valided that our algorithm has outstanding performance in robustness and accuracy than the popular algorithms in the public dataset EuRoC © 2022 IEEE.

关键词： Equalizers

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Particle Swarm Based Reinforcement Learning 7th

Particle Swarm Based Reinforcement Learning

引用

7th International Conference on data Mining and Big data, DMBD 2022

作者： Duan, Jianyu Guo, Yanxiao Wang, Zhigang Ke, Liangjun State Key Laboratory for Manufacturing Systems Engineering School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China CETC Key Laboratory of Data Link Technology Xi’an Xi’an China

ISBN: (纸本)9789811992964

With the vigorous development of computer-related technology, the "perception + decision" paradigm of the combination of deep learning and reinforcement learning has become a research hotspot. Nowadays, deep reinforcement learning algorithms have been successfully applied to the fields of games, industry and commerce. However, deep reinforcement learning algorithms often fall into the dilemma of "exploration" and "exploitation", and the effect of these algorithms is easily affected by the quality of hyperparameters. In order to make up for the defects mentioned above, this paper introduces the particle swarm based reinforcement learning framework (PRL). Compared with the standard reinforcement learning algorithms, this framework greatly improves the exploration ability and obtains better scores in a series of gym experimental tests. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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A Deep Reinforcement Learning Approach for Cooperative Target Defense 7th

A Deep Reinforcement Learning Approach for Cooperative Targ...

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7th International Conference on data Mining and Big data, DMBD 2022

作者： Xiong, Yanxue Wang, Zhigang Ke, Liangjun State Key Laboratory for Manufacturing Systems Engineering School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China CETC Key Laboratory of Data Link Technology Xi’an Xi’an China

ISBN: (纸本)9789811992964

This paper considers a new variant of the pursuit-evasion problem, called the cooperative target defense problem with three agents (attacker, targeter, and defender) in a 3D space. The targeter tries to fly as quickly as possible from a starting point to the terminal, while the defender seeks to protect it from the attacker. The problem is difficult to solve under traditional game theory methods, while deep reinforcement learning (DRL) has shown strong adaptability in these complex and higher-dimensional tasks. Inspired by the successful applications of Proximal Policy Optimization (PPO), this paper proposes a PPO-based algorithm for the problem, intending to derive the optimal behavioral policies for both sides. We design the corresponding state space, action space, and rewards of the agents. Three kinds of reward functions are proposed for the attacker and compared by experimental results. Our study provides a good foundation for the cooperative target defense problem. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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A Novel Distributed Adaptive Cubature Kalman Filter with Variational Bayesian Approach 10

A Novel Distributed Adaptive Cubature Kalman Filter with Var...

引用

10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021

作者： Yang, Feng Ying, Yuwei Zheng, Litao Northwestern Polytechnical University CETC Key Laboratory of Data Link Technology Xi'an710072 China

ISBN: (纸本)9781665440295

A distributed cubature Kalman filter (CKF) is proposed for multi-sensor systems with uncertain measurement noise statistics. Firstly, the CKF is exploited to handle nonlinear state estimation. By choosing inverse Wishart priors, the joint posterior distribution of the state vector and measurement noise covariance matrix is approximated to a free-form variational distribution based on the variational Bayesian approach. By communicating with neighbors, all the sensor nodes can approximate global estimation via performing arithmetic average fusion within the framework of consensus filtering. Simulations of a target tracking problem are performed to illustrate the effectiveness of the proposed algorithm. © 2021 IEEE.

关键词： Sensor nodes

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Learning-based Gaussian Mixture Reduction for Distributed Bayesian Filter 10

Learning-based Gaussian Mixture Reduction for Distributed Ba...

引用

10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021

作者： Yang, Feng Tang, Xinyi Li, Tiancheng Zheng, Litao Northwestern Polytechnical University CETC Key Laboratory of Data Link Technology Xi'an710072 China

ISBN: (纸本)9781665440295

Based on the Wiener approximation theorem, any distribution can be expressed or approximated by the finite sum of the known Gaussian distribution, so it has been widely used in many scenes. When one applies Gaussian mixture distribution (GMD) to state estimation, an urgent problem to be solved is that the number of Gaussian components will increase exponentially over time, which brings difficulties to the practical application. Therefore, it is particularly important to design appropriate methods to reduce the number of Gaussian components in the application of state estimation and keep the computational complexity at a feasible level. This paper proposes a learning-based improved Expectation Maximization (EM) Gaussian Mixture Reduction (GMR) method. This method is a multi-step optimization method, which can reduce the number of Gaussian components at the expense of a small amount of computational complexity. Finally, the proposed improved EM GMR method is applied to the target tracking scene of the distributed Bayesian filter. Simulation results show that compared with traditional algorithms, the proposed learning-based method can obtain a high estimation accuracy with a low computational cost. © 2021 IEEE.

关键词： State estimation

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Membership data Privacy Protection and Poisoning Detection Scheme for Federated Learning 1st

Membership Data Privacy Protection and Poisoning Detection ...

引用

1st Conference on Blockchain and Web3.0 technology Innovation and Application, BWTAC 2024

作者： Li, Yafeng Sun, Zhijun Ma, Lichuan China CETC Key Laboratory of Technology on Data Link Xi’an710068 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China

ISBN: (纸本)9789819794119

In the era of big data, data privacy and security are highly valued, leading to the phenomenon of "data islands", which hinders the effective utilization of data. Federated learning, as a privacy-preserving distributed machine learning method, allows model training without sharing raw data, addressing the privacy concerns in cross-organizational data cooperation. Although federated learning protects data privacy, participants’ gradient parameters can still potentially leak sensitive information. As a result, extensive research has focused on conducting federated learning while protecting the privacy of gradient parameters and reducing the communication and computational overhead for participants. These efforts assume that each participant adheres honestly to the protocol;however, malicious submissions of incorrect gradient parameters can significantly bias the federated learning model. This paper proposes a federated learning scheme for privacy-protected gradient aggregation and poison detection that defends against malicious poisoning attacks while safeguarding data privacy, and optimizes computational and communication overheads. By employing inner product triples, the computation process is further optimized, enhancing system performance. The security of the scheme is demonstrated, and its efficiency is validated through practical tests, showing that the optimized solution significantly outperforms the basic scheme in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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AoI Optimal Trajectory Planning for Cooperative UAVs: A Multi-Agent Deep Reinforcement Learning Approach 5

AoI Optimal Trajectory Planning for Cooperative UAVs: A Mult...

引用

5th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2022

作者： Chi, Kai Li, Fuqiang Zhang, Fan Wu, Mengjie Xu, Chao Xidian University State Key Laboratory of Integrated Service Networks Shaanxi Xi'an China Cetc Key Laboratory of Data Link Technology Shaanxi Xi'an China School of Information Engineering Northwest A&f University Shaanxi Yangling China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

This paper considers a multiple Unmanned Aerial Vehicles (UAVs)-assisted IoT network, where the UAVs cooperatively collect data packets generated by the IoT devices (IDs) and transmit them to the Base Station (BS) continuously to improve the information freshness, in terms of the age of information (AoI). Particularly, we first formulate multi- UAV distributed cooperative dynamic trajectory planning problem as a decentralized partially observable Markov decision process (Dec-POMDP), where the update arrivals at IDs are stochastic and are unknown to the UAVs. Furthermore, in order to address the challenges arising from unknown environmental dynamics and conflict collision constraints, we devise a multi-agent deep rein-forcement learning (MADRL) based dynamic trajectory planning algorithm. The algorithm leverages the advantages of both the QMIX and Gated Recurrent Unit (GRU) techniques. Finally, simulation results are presented to demonstrate the effectiveness of our proposed algorithm. © 2022 IEEE.

关键词： Reinforcement learning

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Modeling and Simulation of link 16 Message Distribution in Operational Applications

Modeling and Simulation of Link 16 Message Distribution in O...

引用

IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Wanyong Tian Fuqiang Li Chilian Chen Key Laboratory of Technology on Data link China Electronics Technology Group Corporation (CETC) 20th Institute Xi'an China

data link is an information system conforming to specified message formats and communication protocols that links combat units such as sensors, weapons, and command and control (C2) platforms. It could automatically share formatted tactical data, for instance, battlefield situation, command and guidance, tactical coordination and weapon control instructions among combat participants in real time. As a typical data link, link 16 has been the major means of information dissemination in joint tactical operations of the US military, and has played an important role in all previous wars over the past decades. link 16 is capable of achieving interoperability and realizing various tactical functions for all combat participants based on unified information distribution and processing mechanisms. This paper investigates message exchanging procedure and applies discrete event system specification (DEVS) method in modeling and simulation (M&S) of information distribution in air interception operations. The experimental results show that the method can be widely applied to modeling information distribution with good scalability. It is able to provide technical reference for subsequent information processing optimization.

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