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13th International Conference on Information Optics and Photonics, CIOP 2022

作者： Zhang, Yinbo Li, Sining Sun, Jianfeng Jiang, Peng Zhou, Shuaijun Zhang, Xin Hou, Yue Zhang, Hailong National Key Laboratory of Science and Technology on Tunable Laser Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9781510660632

The non-uniform distribution of smoke and laser spot seriously limits the imaging ability of single-photon lidar through smoke. To this end, based on the collision theory between photons and smoke particles, this paper establishes the time-domain distribution model of scattered photons (Gamma), which considers the lidar system parameters and the characteristics of smoke particles. The shape parameter and inverse scale parameter are defined as the maximum scattering number K and the average scattering number β, respectively. The indoor test effectively illustrates the estimation ability of the model for smoke. The parameter estimation results show that the average scattering number β and the maximum scattering number K increase linearly with the increase of attenuation coefficient. This study is of great significance for the suppression of smoke backscattering and is expected to improve the weather adaptability of single-photon lidar. © 2022 SPIE.

关键词： Backscattering

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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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An Approach for Upper Limb Movement Intention Recognition Using EEG and sEMG Fusion based on the MCPSA-CIIM 5

An Approach for Upper Limb Movement Intention Recognition Us...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhang, Weiming Zhang, Xiaodong Xu, Cheng Zhou, Guchuan Zhang, Teng Wang, Yu Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robot School of Mechanical Engineering Shaanxi Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China

ISBN: (纸本)9798350307320

The upper limb motion intention recognition method based on electroencephalography (EEG) and surface electromyography (sEMG) fusion has achieved significant results in fields such as prosthetic control. However, most existing fusion methods use manual means to select features, which cannot capture temporal and spatial features at different scales, and ignore the correlation features between the two types of signals. To address these issues, this article proposes a fusion recognition method for upper limb motion intention EEG and sEMG based on Multi-scale Convolution, Polarized Self-Attention, and Cross Intelligence Integration Module. This article extracts multidimensional temporal and spatial features of EEG and sEMG through multi-scale convolution, and introduce polarized self-attention mechanism to filter the extracted multi-scale features. Simultaneously using cross enhancement strategy to extract correlation features between EEG and sEMG. Finally, the features are input into the classification network for recognition. This method was validated on the Jeong database, and the results showed that compared with CNN-LSTM and EEGNet, the recognition accuracy of this method increased by 2.63% and 3.15%, respectively. © 2023 IEEE.

关键词： Electroencephalography

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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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UAV Typical Index Evaluation Method Based on Improving Evidence Theory

UAV Typical Index Evaluation Method Based on Improving Evide...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Geng, Huapin Suo, Bin Tong, Jiahui Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9789811694912

It is an effective way to get more accurate results of UAV performance evaluation by fusing the data of infield test and outfield test. The common Bayes multi-source data fusion method can make comprehensive use of multiple information types while ensuring the decision risk as small as possible, but there are some risks, such as insufficient data utilization and small sample data being "submerged" by large sample data in the fusion. Due to the different scale and credibility of multi-source data, a non-uniform distribution probability sampling method is proposed based on evidence theory for the same sample space to avoid the risk of small sample data being "drowned" by large sample data;meanwhile, for the situation that the fusion weight mainly depends on the evaluation of experts is not objective enough, a comprehensive weight confirmation method of multi-source test data is proposed based on credibility measurement so as to realize the comprehensive evaluation of performance indicators based on multi-source data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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An Iterative Closest Points Optimization Algorithm for Localization of Mecanum-wheeled Robots

An Iterative Closest Points Optimization Algorithm for Local...

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第三十八届中国控制会议

作者： Ning Hao Rui Xing Qi Zhao Fenghua He Hang Zhang Harbin Institute of Technology Complex System Control and Intelligent Agent Cooperation Laboratory

Over the last years, optimization based estimation methods have been applied with great success to a variety of state estimation problems. In this paper, we propose an optimization algorithm to solve the localization problem of Mecanumwheeled robots in a structured environment based on combination of odometer and laser lidar. First, provide the configuration of the mobile robot and the expression of localization problem based on probability form. Secondly, the optimization cost function is derived from the perspective of maximum posterior estimation. And then an iterative closest points optimization algorithm is proposed to address the combination optimization problem. Finally, simulation experiments are designed and implemented to validate the proposed algorithm.

关键词： localization state estimation iterative closest points optimization Mecanum-wheeled robot

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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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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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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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A Parallel Mapping and Planning Strategy for Micro Aerial Vehicle

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Guidance, Navigation and control 2022年第2期2卷 125-145页

作者： Zhuo Wang Qun Zong Hongming Shen Bailing Tian Hanchen Lu School of Electrical and Information Engineering Tianjin UniversityTianjin 300072P.R.China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing 100083P.R.China

Autonomous fight is a fundamental problem for various saplications of miaro aerial vehicles(MAVs).Thanks to the dewvelopment of *** and control(GNC)techndogle,the rsearch on this problem i abo becoming ***,safe fight in unknown,dut tered enviouments remains an open question,epeaially with real-time reguirement on onboard *** paper propoes a framnework including prlelly mapping and planning and implements it on Graphics Pmocessing Unit(GPU).First,a spherical coomlinate proijection is used in the ocupency grid map to avold menory *** that,in the planning phase,a method baed on latice state space sampling is applied to obtain mutiple rajectorfe ***,we design a series of aoft constraints to ensure that the MAV is in a safe known space with optimal *** solving the cost for each trajectory and comparison,the optimal trajectory can be *** efectivenes of the propeed strategy is demonstrated through simulation testa.

关键词： Motion planning lattice planner parallel processing

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