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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Ye, Pengpeng Wei, Jianing Lu, Yu Li, Feifei Lin, Tao Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Electro-Mechanical Engineering Institute Beijing100074 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789811694912

The salvo attack of multi-missile is investigated, where the communication topology is randomly switching and unsustainably connected due to the unreliable links, and the probability of the packet loss is unavailable. Based on feedback linearization, a cooperative guidance protocol is developed in a fully distributed manner, in which gains can be designed incongruently for all missiles, and only the local topology information is essential. Multiple missiles disconnect with each other once the consensus of impact time is reached, and are guided to the target under the proportional navigation guidance (PNG) law individually. Sufficient conditions are established to guarantee the consensus of impact time. A simulation experiment is delivered to validate the effectiveness of the obtained results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feedback linearization

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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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Multi-Source Dynamic Time Series Data Processing Method Based on Comprehensive Weighted Fusion 12

Multi-Source Dynamic Time Series Data Processing Method Base...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022

作者： Geng, Huapin Yuan, Feimeng Suo, Bin Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China Beijing Electro-mechanical Engineering Institute Beijing China School of Mathematics Southwest Jiaotong University Chengdu China School of Information Engineering Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9781839538360

Aiming at the fusion problem of multi-source heterogeneous dynamic data sources, based on the subjective and objective comprehensive weighting idea, an efficient multi-source dynamic data fusion algorithm is proposed. First, the outlier detection method based on linear regression model is used to preprocess the multi-source dynamic time series data. Then, determine the weight of the dynamic data. The prior weight of the data source is determined by the AHP method, cyclic scoring method, etc., and the posterior weight is determined according to the similarity between the time series data. Based on the Euclidean distance, a similarity measure between different data sources and a similarity measure between different dynamic data samples are constructed respectively. The former is used for the fusion of dynamic data standard deviation expectations, and the latter is used for the fusion of mean expectations. Finally, the comprehensive weight of each dynamic time series of each data source is obtained, and the comprehensive weighted fusion method is used to obtain the expected value and standard deviation of the dynamic response. The analysis of the calculation example shows that the proposed algorithm has higher computational efficiency than the method that treats dynamic data as several discrete static data. © 2022 IEEE.

关键词： Data fusion

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

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

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

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

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.

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Scalable Distributed Organizational Structure and Self-Organization Mechanisms for Unmanned system Cluster 9

Scalable Distributed Organizational Structure and Self-Organ...

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9th IEEE International Conference on Computer science and Network technology, ICCSNT 2021

作者： Liu, Haibo Lin, Yang Shen, Jing Wang, Xing Shi, Changting Zhang, Jiayu College of Computer Science And Technology Harbin Engineering University Harbin150001 China Science And Technology On Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781665443647

Reasonable control organizational structure can help an unmanned system cluster cooperate more effectively to complete tasks. Previous research of existing organizational structures has problems implementing the task in a natural environment, such as the cluster's lack of scalability. This paper proposes a three-layer scalable distributed organizational structure and dynamic self-organization mechanisms, which overcomes the lack of scalability and ensures that the cluster can still perform stably after the cluster size changes. The simulation results show that the organizational structure realizes information sharing and cross-layer control, which enhance the flexibility and stability of unmanned system clusters. Comparison results show that our achievements can make a good performance, contributing to this area of future research. © 2021 IEEE.

关键词： Scalability

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Distributed Multi-Sensor Multi-View Fusion of PHD Filter for Maneuvering Targets

Distributed Multi-Sensor Multi-View Fusion of PHD Filter for...

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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 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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Lightweight hybrid visual-inertial odometry with closed-form zero velocity update

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Chinese Journal of Aeronautics 2020年第12期33卷 3344-3359页

作者： QIU Xiaochen ZHANG Hai FU Wenxing School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Aircraft Control Laboratory Beijing 100191China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing 100074China

Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual *** VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature *** modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.

关键词： Inverse depth parametrization Kalman filter Online calibration Visual-inertial odometry Zero velocity update

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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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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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