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检索条件"机构=CETC Key Laboratory of Data Link Technology"

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data link Effectiveness Evaluation Method Based on Improved ADC Model 3

Data Link Effectiveness Evaluation Method Based on Improved ...

引用

2023 3rd International Conference on Mechatronics, Automation and Intelligent Control, MAIC 2023

作者： Guo, Pengju Chen, Liang Key Laboratory of Technology on Data Link China Electronics Technology Group Corporation 20th Institute Xi'an China

Aiming at the problem of innumerable data link effectiveness evaluation bases, the battlefield environment adaptability is introduced into the ADC model to improve the accuracy of the ADC evaluation method based on the analysis of the ADC model. According to the mission and using mode of data link equipment, the evaluation index system and calculation method of data link equipment effectiveness are proposed, and the quantitative evaluation of data link system effectiveness is realized. The validity and scientificity of the model are verified by the example analysis, which provides an effective method for the effectiveness evaluation of data link equipment. © Published under licence by IOP Publishing Ltd.

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Particle Swarm Based Reinforcement Learning 7th

Particle Swarm Based Reinforcement Learning

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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...

引用

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...

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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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Tactical data Chain link Effectiveness Assessment Based on Cloud Theory 6

Tactical Data Chain Link Effectiveness Assessment Based on C...

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6th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2023

作者： Chen, Liang Li, FuQiang Zhao, Lulu Zhang, Qian China Electronics Technology Group Corporation 20th Institute Xi'an China Key Laboratory of Technology on Data Link Xi'an China

ISBN: (纸本)9798350399059

To address the complexity of tactical data chain system effectiveness assessment, a tactical data chain system effectiveness assessment index system is established by drawing on the principles of cloud theory, and a tactical data chain system effectiveness assessment method based on cloud theory is proposed. Based on the description of the hierarchical analysis method and the evaluation steps of cloud theory, the evaluation of the effectiveness of the tactical data chain system is realized by calculating the cloud model of each indicator and the weight of each indicator parameter in combination with a specific example. The example proves that this method can effectively integrate the uncertainty and ambiguity of quantitative and qualitative indicators, is highly operable, and can provide a scientific and objective method for the comprehensive effectiveness assessment of tactical data chain systems based on network information systems. © 2023 IEEE.

关键词： Hierarchical systems

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A Complex Time Management Method Driven by Composite Event in Cloud Simulation Platform

A Complex Time Management Method Driven by Composite Event i...

引用

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

作者： Sule Wang Chilian Chen Tao Zhang Key Laboratory of Technology on Data link China Electronics Technology Group Corporation (CETC) 20th Institute Xi'an China

At present, cloud computing related technologies have developed rapidly. Among them, cloud simulation platforms play a vital role in complex military projects. The complex time management under the cloud simulation platform can ensure the normal operation of the relevant test system, thereby promoting the development and progress of defense-related research. In actual engineering projects, each subsystem in the system adopts different time advancing mechanisms. Based on this, this paper studies the complex time management method driven by compound events under the cloud simulation platform. The research content and results are as follows: Based on the characteristics of physical subsystem and logical subsystem, two advancing mechanisms, real-time advancing and logical time advancing are designed; A complex event-driven complex time management method under the cloud simulation platform is designed; Verify the effectiveness of the compound event-driven complex time management method proposed in this paper under the cloud simulation platform.

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Mining Frequent Closed Itemsets and Generators Over Uncertain data

Mining Frequent Closed Itemsets and Generators Over Uncertai...

引用

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

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

The rapid development of computer technology and the widespread use of Internet products have already led us into the era of big data, and researchers are increasingly focused on developing advanced data mining techniques to extract valuable insights from large datasets. Frequent itemset mining refers to mining the frequently occurring itemsets in data, which is a reliable method to get useful information from a large amount of data and is used on the Internet, finance, medical and other fields. But as a rule, the number of frequent itemsets extracted from data is exponential, which reduces the usefulness of mining results. To achieve the compressed representation of frequent itemsets, researchers have proposed the concept of frequent closed itemsets, which are greatly reduced in number compared to frequent itemsets and retain the integrity of the information contained in frequent itemsets. In this paper, we propose an algorithm for mining closed itemsets and generators in uncertain data, which can be divided into two steps. First, all probabilistic frequent itemsets are mined, and then, based on the probabilistic frequent itemsets, the closed itemsets and generators among them are identified. And the closed itemsets and generators are associated based on the concept of equivalence classes. The algorithm is proved to be correct and efficient after evaluation on several datasets.

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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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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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