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2024 China Automation Congress, CAC 2024

作者： Li, Wei Xu, Hongfeng Li, Yuan Beijing Institute of Technology Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9798350368604

In complex environments with vast and diverse information sources, target recognition serves as a crucial precursor to situational analysis, providing the foundation for further decision-making and analysis. Traditional algorithms for target recognition often rely on human experience and rules, necessitating expert knowledge and subjective judgments, which introduces uncertainties. This paper introduces a method for target recognition inference, leveraging Situation Knowledge Graph Convolutional Neural Network (SKGCN). By employing active and passive radar sensor data in complex scenarios, the algorithm processes inputs within a self-constructed situational knowledge graph. It derives vector representations of target entities through aggregation operations among neighboring nodes and extracts perceptual data vectors using the embedding layer. The algorithm calculates the alignment between sensor data and target entities, facilitating target recognition and inference in complex scenarios. Simulation results confirm the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Knowledge graph

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Refined recognition and intelligent smart interference of radar signal

Refined recognition and intelligent smart interference of ra...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Yuan, Jianquan Bu, Yunong Yang, Shuyuan Chi, Qingxi Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China Xidian University Xi'an China

ISBN: (纸本)9781728137926

Facing the complex electromagnetic environment on the battlefield, how to effectively identify important radar signals by jammers and how to implement strong and effective electromagnetic interference to the enemy's huge radar detection system under the condition of limited jamming resources will be the difficult problems that need to be solved continuously in the electronic warfare field. The solution of these problems will be the premise of effective interference for wartime jammers. In this paper, the technical gap of traditional jammers is analyzed. For radar signal perception, a refined perception and recognition method is proposed, forming the algorithm idea of multi-scale feature extraction and recognition based on radar fingerprint and multi-channel integrated multi-label recognition and classification based on radar radiation source. For interference implementation, the idea of smart interference based on deep GAN is put forward, laying the ground for further research on cognitive electronic warfare. © 2019 IEEE.

关键词： III-V semiconductors

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Vehicle detection based on point cloud intensity and distance clustering 5

Vehicle detection based on point cloud intensity and distanc...

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2020 5th International Seminar on Computer technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Wang, Zhao Wang, Xing Fang, Bin Yu, Kun Ma, Jie School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Multispectral Information Processing Technology Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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Prediction of Hybrid Asynchronous Frequency Hopping Pattern based on Deep Learning in Wideband Spectrum

Prediction of Hybrid Asynchronous Frequency Hopping Pattern ...

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2023 China Automation Congress, CAC 2023

作者： Wang, Ya Wang, Jiaqi Yuan, Shuai Liu, Naijin Lu, Yuanming Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Unit 63893 of Pla Luoyang China

ISBN: (纸本)9798350303759

In non-cooperative scenarios, wideband frequency hopping (FH) communication reconnaissance including FH signal detection, parameter estimation, and network sorting under single-channel reception is challenging. The FH pattern contains the most information about FH signals. So it is the core of FH parameter estimation. Based on the task analysis, this paper proposes a blind prediction framework combining deep learning (DL) and clustering methods to improve the accuracy and efficiency of FH pattern estimation for mixed signals in the wideband spectrum. The unique advantage of this framework is that it requires no prior signal information and anchors but exploits the inherent time-frequency (TF) properties of asynchronous FH signals. It has a strong generalization ability and can adapt to signals of any shape. Moreover, a new comprehensive evaluation metric, normalized root mean square error including missed detection (NRMSE-MD), suitable for sequence data prediction is proposed to evaluate the level of missed detection and false detection in signal monitoring. Experimental results demonstrate the superiority of the proposed framework and the effectiveness of the proposed evaluation metric. © 2023 IEEE.

关键词： Forecasting

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The Research on Simulation technology for multi-aircraft cooperative guidance

The Research on Simulation Technology for multi-aircraft coo...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Li Jing Shen Chao Tong JiaHui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

In this paper, the composition and operation mechanism of a multi-aircraft cooperative simulation system is analyzed, with its formal architecture identified as a discrete event system & differential equation system specifications (DEV&DESS). Then, the operation and control problem of the cooperative simulation system is converted into the simulation control problem of a specific DEV&DESS system. A coordinator-based test method for cooperative simulation is proposed and validated by tests of real-time mathematical simulation.

关键词： aircraft control control system synthesis differential equations discrete event simulation discrete event systems Aircraft control discrete event systems control system synthesis discrete event simulation Differential equations Simulation systems Differentiation Test Method

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Optimal Trajectory Generation of UAV Considering Attitude Constraints

Optimal Trajectory Generation of UAV Considering Attitude Co...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Liu, Chunjing Zhou, Hao Dong, Xiaofei Wang, Dongbin School of Astronautics Beihang University Beijing102206 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China School of Automation Science and Electrical Engineering Beihang University Beijing100083 China

ISBN: (纸本)9789811581540

The six degrees of freedom (6-DOF) trajectory planning of quadrotors with attitude constraints is studied. Based on active disturbance rejection control (ADRC) technology, a controller for quadrotors that can track both position and attitude is designed. Firstly, the nonlinear dynamic model of quadrotors is established. Next, the optimal trajectory with attitude constraints is solved under different performance indexes. Finally, based on ADRC method, a 6-DOF tracking control algorithm for quadrotors is designed. The control effect is simulated and compared with the traditional linear control method. The results indicate that ADRC method has superior control accuracy and stability for quadrotors with large attitude maneuvers. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Heterogeneous Unmanned Aerial Vehicles Cooperative Search Approach for Complex Environments

arXiv

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arXiv 2023年

作者： Liu, Zhenchang Hao, Mingrui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

This paper studies a heterogeneous Unmanned Aerial Vehicles (UAVs) cooperative search approach suitable for complex environments. In the application, a fixed-wing UAV drops rotor UAVs to deploy the cluster rapidly. Meanwhile, the fixed-wing UAV works as a communication relay node to improve the search performance of the cluster further. The distributed model predictive control and genetic algorithms are adopted to make online intelligent decisions on UAVs’ search directions. On this basis, a jump grid decision method is proposed to satisfy the maneuverability constraints of UAVs, a parameter dynamic selection method is developed to make search decisions more responsive to task requirements, and a search information transmission method with low bandwidth is designed. This approach can enable UAVs to discover targets quickly, cope with various constraints and unexpected situations, and make adaptive decisions, significantly improving the robustness of search tasks in complex, dynamic, and unknown environments. The proposed approach is tested with several search scenarios, and simulation results show that the cooperative search performance of heterogeneous UAVs is significantly improved compared to homogeneous UAVs. © 2023, CC BY.

关键词： Genetic algorithms

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Anti-bias Target Association Method Based on Laplacian Spectrum and Kuhn-Munkres Algorithm 1

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3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Bai, Xiaomeng Xu, Cheng Fu, Wenxing Guan, Xujun Zhang, Hang Fu, Wenxing Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Electro-Mechanical Engineering Institute Beijing100074 China Unmanned System Research Institute Northwestern Polytechnical University Xi’an710072 China

ISBN: (数字)9789819710911

ISBN: (纸本)9789819710904

In the problem of collaborative situational awareness, the single sensor inevitably has random errors, systematic errors, false alarms, and missed alarms. To improve the accuracy of target association in the above cases, this paper proposes an anti-bias target association method based on the Laplacian spectrum and Kuhn-Munkres (KM) algorithm. The main contributions of the paper include addressing the coupling problem between system error calibration and target association through two stages, focusing on the structural characteristics of the target formation in the first stage and the absolute position of the target itself in the second stage. The algorithm takes into account both factors and achieves accurate association under complex errors. The test results demonstrate that the proposed method effectively improves the accuracy of anti-bias target association. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： systematic errors

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Visual Explanation of Object Detectors via Saliency Maps 18

Visual Explanation of Object Detectors via Saliency Maps

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Xiao, Jin Liu, Wenrui Hu, Xiaoguang Jiang, Hao Wang, Weipeng Yahang, Li Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Beihang University Beijing China The Third Academy of Casic Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350312201

In recent years, rapid progress of deep learning has resulted in object detection algorithms based on deep neural networks nearly completely replacing traditional methods. This shift has led to significant improvements in both accuracy and speed of object detection. However, deep neural networks that support decision-making inobject detection are complex black boxes with hidden internal logic and operations. This lack of transparency results in deep learning models being unexplainable, which limits their practical deployment. In this paper, we analyze the current state and future trends of interpretable technology in object detection and propose an effective object detection interpretable algorithm. This algorithm aims to explain the underlying logic of AI decision-making and provide support for the high performance and credibility of object detection algorithms based on deep neural networks through quantitative evaluation indicators. © 2023 IEEE.

关键词： Object detection

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A Cooperative Position Method of Two Aircraft Under the Condition of Giant Inertial Dispersion 11th

A Cooperative Position Method of Two Aircraft Under the Cond...

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11th China Conference on Command and control, C2 2023

作者： Peng, Siting Li, Lei Ren, Jun Zhu, Hongbo Yang, Jiabin Beijing Electro-Mechanical Engineering Institute Beijing100074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China School of Electronics and Information Engineering Beihang University Beijing China

ISBN: (纸本)9789819990207

In this paper, based on the condition that the inertial navigation system loaded on the aircraft has giant dispersion, and the aircraft need to detect, track and attack a target, thus the relative position relation needs to be calculated precisely. How to estimate the problem becomes a difficult obstacle. This article describe the demand faced in the weapon engineer field and provide some solutions feasibly in the practical engineering, three mathematical method are modelled and the readers can choose the suitable scheme according to the detector performance. The un-linear filter method like unscented and cubature kalman filter are used in combination with the state and observation equation. The simulation results show that the modelling methods are observable and the filter method is adaptive to the motion method, therefore the algorithm elaborated in the thesis is of practical significance to the engineer project. © Chinese Institute of Command and control 2024.

关键词： Aircraft

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