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International Conference on Control, Automation and Information sciences (ICCAIS)

作者： Xinwei Wei Linxiu Chen Chenyu Zhang Yiru Lin Linao Zhang Xiaokai Liu Jixiang Jiang Wei Yi School of Information and Communication Engineering University of Electronic Science and Technology of China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Research Institute of Electronic Science and Technology University of Electronic Science and Technology of China

Multi-target Tracking (MTT) is the process of processing received measurements to maintain estimates of the current status of multiple targets, with important applications to autonomous driving, aerial reconnaissance, underwater operations, and others. In the model-based setting, Bayesian filtering can provide the theoretical optimal estimate in a single target scenario. However, in complex situations, uncertain factors such as changes in the number of targets will cause the amount of calculation to increase exponentially, resulting in a decline in tracking accuracy. To solve that problem, model-free methods based on deep-learning provide an attractive alternative, especially the state-of-the-art architecture Transformer based encoder-decoder prediction model, which outperforms the Bayesian filters in the single frame prediction tasks. However, when switching to continuous tracking, these algorithms need to be trained separately frame by frame to adapt to the new tasks. Still, there is no correlation between their predictions from different frames, which prevents them from fully utilizing all the measurements. In this paper, we propose an end-to-end Transformer based MTT method with state autoregression, which allows the model to have the capability of online continuous tracking and make total use of the entire trajectory. The results show that the proposed model is a great extension from single-frame prediction to online continuous tracking.

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A Multi-Modal Multi-Objective Evolutionary Algorithm Based on Dual Decomposition and Subset Selection

SSRN

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SSRN 2023年

作者： Xiong, Minghui Xiong, Wei Liu, Zheng Liu, Yali Han, Chi Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101416 China

Multi-modal multi-objective problems (MMOPs) arise frequently in real world applications, in which multiple Pareto-optimal solution sets (PSs) correspond to the same point on the Pareto front (PF). Diversity preservation is a crucial issue in multi-modal multi-objective evolutionary algorithms (MMEAs), which aims at evolving the population toward the PF and equivalent PSs with a uniform distribution and good spread. In spite of many diversity preservation approaches in MMEAs, most of them guarantee the diversity in decision space with a degraded performance in objective space. To address the above issues, this article proposes a MMEA based on dual decomposition and subset selection, which employes the weight vectors and grids to decompose both the objective and decision spaces. On the basis of dual decomposition, the mating selection are adaptively performed based on estimated optimization stages of current subproblem. Moreover, a subset with good diversity in both the objective and decision spaces are selected from the promising solutions ever collected during the evolution process. Experimental results on 22 benchmark problems demonstrate our advantages to maintain a more balanced diversity in both objective and decision spaces. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Modelling and Analyses of the Range Gate Pull-off (RGPO) Effect on Radar Tracking

Modelling and Analyses of the Range Gate Pull-off (RGPO) Eff...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yanfeng Wang Qihua Wu Tiehua Zhao Xiaobin Liu Feng Zhao Shunping Xiao State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics and Information System College of Electronic Science and Technology National University of Defense Technology China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

Range gate pull-off (RGPO) Jamming disrupts tracking capability of radar system by strategically increasing the latency of radar signal. Hence this kind of active deception jamming is widely adopted. The quantitative assessment and evaluation of RGPO Jamming effects on radar systems is of great significance for enhancing radar anti-jamming capabilities. However, difficulties exist in establishing a mathematical model for analyzing the jamming effects through theoretical analyses. Both the complexity of different radar systems and randomness of radar tracking processes add to this challenge. These factors result in substantial workload to analyze jamming effects. Considering these challenges, a modelling method for RGPO effects on radar based on digital simulation results is introduced in this study. Firstly, the radar range tracking model was established. Through the qualitative analyses of the process of RGPO jamming effect and the principle of affecting the tracking loop, it was found that the rate of a successful pulling off would show a precipitous drop after the pulling speed exceeds a certain threshold. This conclusion was verified in the simulation results of the RGPO effect simulation system. On this basis, to build the relationship between the maximum pull-off speed and the Jamming-to-Signal Ratio (JSR), a modelling method based on spline interpolation and least squares fitting was proposed in this study. The work contributes to a deeper understanding of the impact of RGPO Jamming on radar tracking, and provides valuable insights for bolstering radar countermeasures.

关键词： Interpolation Analytical models Tracking loops Fitting Digital simulation Radar Logic gates

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Construction and Application of Knowledge Graph of Weapon and Equipment system of systems

Construction and Application of Knowledge Graph of Weapon an...

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2021 International Conference on electronic Information Engineering and Computer science, EIECS 2021

作者： Li, Xiao Liu, Desheng Space Engineering University Science and Technology on Complex Electronic System Simulation Laboratory Beijing China

ISBN: (纸本)9781665416740

In the context of diversified types of weapon and equipment and massive data quantification, how to model and store, manage and apply the data of various types of weapon and equipment is a problem that needs to be solved. The article analyzes the characteristics of the weapon and equipment system and proposes a method to construct the knowledge graph of the weapon and equipment system based on this method, which first constructs the ontology in the top-down order, then defines the edge nodes and fills in the structured data. Finally, the application is carried out according to the constructed knowledge graph, and the future development direction is proposed. © 2021 IEEE.

关键词： Information management

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WebGL-based Network Multi-view Visualization Interactive Analysis Method 4

WebGL-based Network Multi-view Visualization Interactive Ana...

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4th International Conference on Computer Information science and Artificial Intelligence, CISAI 2021

作者： Yu, Ronghuan An, Shenhao Space Engineering University Science and Technology on Complex Electronic System Simulation Laboratory Beijing China

ISBN: (纸本)9781665406925

The space-ground integrated network is one of the important information infrastructures of the country in the future. In order to display its network structure in all directions and from multiple angles, a multi-view visualization interactive analysis method is proposed. First introduces the relevant basic technology, and then constructs the network dynamic model, which is mainly divided into three parts: link visibility analysis, relative motion analysis and visualization model construction. Finally, the digital earth technology is based on a unified coordinate system and data source. Establish a multi-dimensional view on it and provide a variety of interactive operations. Experimental results show that this method can more comprehensively show the structure of the space-ground integrated network, and help users understand its evolutionary law. © 2021 IEEE.

关键词： complex networks

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Collaborative Filtering Recommendation Algorithm of Weaponry Based on Knowledge Graph 2

Collaborative Filtering Recommendation Algorithm of Weaponry...

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2nd IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Li, Xiao Liu, Desheng Chang, Qing Space Engineering University Science and Technology on Complex Electronic System Simulation Laboratory Beijing China

ISBN: (纸本)9781665428767

Nowadays, modern warfare has changed from weapon-centric operations to network-centric system operations, which has led to the phenomenon of sea-level quantification of weaponry. It is an important research topic for combat commanders to select suitable weaponry from a large number of available resources when facing combat tasks. Recommendation technology is a solution to this problem, which can effectively solve the problem of information overload, so that combat commanders do not produce the phenomenon of 'information loss', in the shortest possible time to make the right decision. Therefore, this paper proposes a collaborative filtering recommendation algorithm for weaponry based on the knowledge graph and the idea of collaborative filtering. In this algorithm, the knowledge graph provides a clear data model with various association relationships, and the collaborative filtering algorithm can realize it without relying on professional experience, which can realize the recommendation process more actively, faster and more intelligently. © 2021 IEEE.

关键词： Collaborative filtering

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ETM fusion algorithm based on log classification 2

ETM fusion algorithm based on log classification

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2nd IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Lin, Wenxiang Liu, Desheng Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781665428767

With the improvement of computer computing power, the shortcomings of the computational inefficiency of the genetic mining algorithm are compensated, and the genetic mining become a research hotspot. In this paper, for the Evolutionary Tree Miner (ETM) algorithm in noise processing can not find the low-frequency effective path and remove the shortage of noise, an ETM fusion algorithm based on log classification is proposed. The algorithm performs process model mining for high-frequency and low-frequency logs separately, and get model fusion based on alignment ideas, Eventually mining the complete process model. Through the simulation of event log data verification, the improved algorithm achieves more complete process model mining than the original ETM algorithm. © 2021 IEEE.

关键词： Trees (mathematics)

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Research on Integration Framework and Service Adaptation technology of Space system Simulation Test-bed

Research on Integration Framework and Service Adaptation Tec...

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2021 International Conference on Advanced Materials and Mechatronics, ICAMM 2021

作者： Liu, Desheng Chang, Qing Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

Aiming at the requirements of space system simulation test-bed in heterogeneous integration and co-simulation, the support capability of test-bed integration framework is analysed. A space system simulation test-bed integration framework based on bridging adaptation service is constructed, and adaptation services such as protocol adaptation, information publication, information subscription and service control for service oriented architecture are designed and implemented, which provide integration capability and co-simulation management capability for heterogeneous systems, models and components of space system simulation test-bed. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.

关键词： Information services

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Analysis on the complex Characteristics of the Space-ground Integrated Network Structure 2

Analysis on the Complex Characteristics of the Space-ground ...

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2nd International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2021

作者： An, Shenhao Yu, Ronghuan Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing China

ISBN: (纸本)9781665412964

The space-ground integrated network is one of the important information infrastructures of the country in the future. In order to further promote its visualization research, the structural characteristics of the space-ground integrated network are analyzed from the perspective of network complexity. Firstly, it introduces the development status of the space-ground integrated network, and then describes its overall structure and composition, focusing on the analysis of the complex characteristics of the space-ground integrated network structure from four aspects: heterogeneity, multi-layeredness, dynamics and distribution. Finally, Prospects for the next research direction. © 2021 IEEE.

关键词： complex networks

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Status and analysis of process mining algorithm classification 5

Status and analysis of process mining algorithm classificati...

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5th IEEE Information technology, Networking, electronic and Automation Control Conference, ITNEC 2021

作者： Lin, Wenxiang Liu, Desheng Space Engineering University Science and Technology on Complex Electronic System Simulation Laboratory Beijing China

ISBN: (纸本)9781665415989

In view of the rapid development of process mining, the rapid update of related algorithms, and the urgent need to sort out a reasonable classification method, the research on the current status of process mining algorithm classification is carried out. First, conduct statistical visualization analysis of process mining algorithms from different data sources, then analyze and summarize the specific algorithm classification literature at home and abroad in chronological order, and discuss its classification methods and advantages and disadvantages. From the research results, there are four main classification methods of algorithms, among which, based on the characteristics of the algorithm, it has the advantages of small crossover and obvious algorithm characteristics. © 2021 IEEE.

关键词： Visualization

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