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2021 CIE International Conference on Radar, Radar 2021

作者： Peng, Cong Chai, Lei Yi, Wei Chen, Linxiu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665498142

This paper addresses the problem of maneuvering multi-target tracking by a network of sensors having different and limited fields of view (FoV s). Each local sensor runs the Gaussian Mixture Probability Hypothetical Density (GMPHD) filter. Due to the target maneuver, based on a single motion model, severe tracking performance degradation can be observed. We propose to use multi-model (MM) method to realize the adaptation to motion characteristic so as to overcome maneuverability of targets. Then considering FoV s of sensors in the network are different and limited, the standard weighted arithmetic average (WAA) fusion is no longer applicable and leads to an underestimation of the target number. Therefore, we use state-dependent WAA (SD-WAA) fusion rule, which performs the WAA in a more robust way by calculating a set of state-related fusion weights. Numerical experiment is designed to demonstrate the efficacy of the proposed method. © 2021 IEEE.

关键词： fields of view gaussian mixture maneuvering multi-target tracking multi-model method probability hypothesis density filter state-dependent weighted arithmetic average

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An error analysis method for attitude estimation system based on epipolar geometry

An error analysis method for attitude estimation system base...

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2021 IEEE International Conference on Power Electronics, Computer Applications, ICPECA 2021

作者： Luo, Shilin Xu, Cheng Zhang, Lundong Dong, Jing Fu, Wenxing Han, Songlai Central South University School of Aeronautics and Astronautics Changsha China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratary Beijing China

ISBN: (纸本)9781728190037

This paper proposes an error analysis method based on epipolar geometry which is analyzed for improving the accuracy of attitude estimation. The influence of equivalent focal length and principal point coordinates on modeling error was analyzed by simulation and experiment based on a rotary table. The result shows that the influence of the equivalent focal length will increase as the rotation angle increases, the different motion states will lead to different degrees of impact of the principal point coordinates. Besides, we derived that pose measuring accuracy is unrelated to the magnitude of rotation angle and there is no accumulative error under simulation conditions. This proposed method can enhance the measuring accuracy and provide some meaningful advice for engineering applications. © 2021 IEEE.

关键词： Computer vision

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An Adaptive Power Allocation Scheme for Mini-UAV Tracking with Distributed Radar in Dense Urban Environment 10

An Adaptive Power Allocation Scheme for Mini-UAV Tracking wi...

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10th International Conference on control, Automation and Information sciences, ICCAIS 2021

作者： Cao, Ziyi Yuan, Ye Yi, Wei Chen, Linxiu University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665440295

In this paper, a new power allocation scheme of the distributed radar system is proposed for mini-UAV tracking tasks in urban environments, considering the influence of the building occlusion on the probability of detection. Firstly, the probability of detection of each radar node is derived according to the estimated position of the target and the visibility of each radar node. Then, according to the probability of detection, the adaptive cost function concerning the transmit power and tracking accuracy is designed and used to the formulation of the optimization problem. Simulation results show that the proposed power allocation scheme can improve the target tracking accuracy in urban environments compared to the conventional method. © 2021 IEEE.

关键词： Cost functions

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Simulation and analysis of far-field single-photon response model in Geiger avalanche mode 7

Simulation and analysis of far-field single-photon response ...

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7th Global intelligent Industry Conference, GIIC 2024

作者： Ou, Hengchang Ma, Le Jiang, Peng Wang, Pengfei Lu, Jie Zhou, Xin Sun, Jianfeng National Key Laboratory of Laser Spatial Information 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 Institute of Remote Sensing Beijing100854 China 44th Research Institute China Electronics Technology Group Corporation Chongqing400060 China Research Center for Space Optical Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute of Harbin Institute of Technology Zhengzhou Henan450046 China

ISBN: (纸本)9781510682986

Poisson distribution model is the basis of data analysis for GM-APD lidar, but it is only applicable to the mirror reflected target under ideal conditions, and cannot accurately describe the photon triggering process of actual GM-APD lidar detection. For the actual target with rough surface, the negative binomial distribution with M as the parameter can describe the photon distribution model more accurately. In order to solve this problem, this paper compares and analyzes Poisson distribution triggering model and the negative binomial distribution triggering model that conforms to the triggering situation of the echo signal of the target with rough surface, considering the differences in the triggering probability under different noise and signal levels. The results show that the trigger probability curve corresponding to the trigger model based on negative binomial distribution is lower in peak value, wider in bottom value and fatter overall than that of the trigger model based on Poisson distribution, and the difference between the two is more prominent under the conditions of low noise level and high signal level. This study has guiding significance for the signal extraction research based on different surface echoes. © 2024 SPIE.

关键词： Photons

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Meta-ZSDETR: Zero-shot DETR with Meta-learning

Meta-ZSDETR: Zero-shot DETR with Meta-learning

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International Conference on Computer Vision (ICCV)

作者： Lu Zhang Chenbo Zhang Jiajia Zhao Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with background. In this paper, we present the first method that combines DETR and meta-learning to perform zero-shot object detection, named Meta-ZSDETR, where model training is formalized as an individual episode based meta-learning task. Different from Faster R-CNN based methods that firstly generate class-agnostic proposals, and then classify them with visual-semantic alignment module, Meta-ZSDETR directly predict class-specific boxes with class-specific queries and further filter them with the predicted accuracy from classification head. The model is optimized with meta-contrastive learning, which contains a regression head to generate the coordinates of class-specific boxes, a classification head to predict the accuracy of generated boxes, and a contrastive head that utilizes the proposed contrastive-reconstruction loss to further separate different classes in visual space. We conduct extensive experiments on two benchmark datasets MS COCO and PASCAL VOC. Experimental results show that our method outperforms the existing ZSD methods by a large margin.

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Research on Simulation technology of Cooperative Guidance for Aircraft Formation

Research on Simulation Technology of Cooperative Guidance fo...

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Chinese intelligent systems Conference, CISC 2020

作者： Tong, Jiahui Liu, Jin Li, Jing Zhan, Tianming Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China TAIJI Computer Corporation Limited Beijing China

ISBN: (纸本)9789811584572

This paper analyzed the structure and characteristics of cooperative guidance system of aircraft formation, puts forward a multi-level simulation system architecture of centralized/distributed driving to solve the cooperative guidance for aircraft formation, realized the modeling of aircraft formation members and interaction by using multi-agent method, discussed the simulation methods of data link networking transmission and positioning, space-time registration of simulation system, and so on. The simulation is carried out based on a certain combat scenario, and the results show that the simulation system constructed in this paper can simulate the whole process of cooperative guidance of aircraft formation. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer architecture

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Transformer based Online Continuous Multi-Target Tracking with State Regression

Transformer based Online Continuous Multi-Target Tracking wi...

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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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An Optimized One-Stage Detector for Autonomous PV Defect Detection

An Optimized One-Stage Detector for Autonomous PV Defect Det...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Huidi, Xiao Jiajia, Zhao Lihui, Wang Qiucheng, Shen Nongjie, Ma Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education School of Instrument Science and Engineering Southeast University Nanjing210096 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China School of Mechanical and Electronic Control Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9789811694912

To realize the defect detection of Photovoltaic (PV) modules based on infrared images, a one-stage detector based on FPT and loss function optimization is proposed. Firstly, ResNet50 is selected as the backbone network for feature extraction, combining with the idea of migration learning to strengthen its feature extraction ability. Besides, replacing the traditional feature pyramid with FPT, using Transformer structure help realize the interaction of the same position between different pyramid layers as well as different positions of the same layer, improving the feature fusion capability of the model. Finally, to alleviate the problem of suppression of non-optimal predictions by NMS strategy, a bipartite graph matching algorithm based on loss function minimization is designed to enhance the learning of the distribution of ground truth targets by the network. The dataset is expanded by Mosaic data enhancement method for model training, so as to achieve infrared image-based PV defect detection. Results show that the improved one-stage target detection network has a promising detection effect on PV module defects under infrared images, with the average accuracy improving by 2.20% compared with the original algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Defects

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Meta-ZSDETR: Zero-shot DETR with Meta-learning

arXiv

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arXiv 2023年

作者： Zhang, Lu Zhang, Chenbo Zhao, Jiajia Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

关键词： Object detection

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Rudder Health Monitoring and Data Visualization Based on Feature Extraction 10

Rudder Health Monitoring and Data Visualization Based on Fea...

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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Zhou, Yajing Sun, Mingwei Hao, Mingrui Chen, Zengqiang College of Artificial Intelligence Nankai University Tianjin300350 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781665424233

In order to monitor the health status of the rudder, this paper proposes a health monitoring method based on deep neural network through feature extraction. First, sufficient samples are obtained by data preprocessing. Then the feature dimension is reduced by using autoencoder, and the health monitoring can be carried out by combining with Softmax classifier. After proper tuning and comparison, proper network structure parameters can be obtained. Two kinds of images can be plotted by using the feature extraction of encoder to provide explicit contrast between the normal and abnormal rudders. The effectiveness of the proposed method can be demonstrated by numerical simulations. © 2021 IEEE.

关键词： Visualization

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