IEEE Journal on Miniaturization for Air and Space Systems最新文献_第9页

Fragility-Free Prescribed Performance Control Without Approximation Applied to Waverider Aerocraft 无逼近的无脆弱性能控制在乘波飞行器上的应用

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-02-06 DOI: 10.1109/JMASS.2023.3242304

Xiangwei Bu;Baoxu Jiang

引用次数: 5

Fault-Tolerant Attitude Control for Hypersonic Flight Vehicle Subject to Actuators Constraint: A Model Predictive Static Programming Approach 基于作动器约束的高超声速飞行器容错姿态控制:一种模型预测静态规划方法

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-02-01 DOI: 10.1109/JMASS.2023.3241566

Ao Li;Shuaizheng Liu;Xiaoxiang Hu;Rui Guo

{"title":"Fault-Tolerant Attitude Control for Hypersonic Flight Vehicle Subject to Actuators Constraint: A Model Predictive Static Programming Approach","authors":"Ao Li;Shuaizheng Liu;Xiaoxiang Hu;Rui Guo","doi":"10.1109/JMASS.2023.3241566","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3241566","url":null,"abstract":"In this article, an improved model predictive static programming (MPSP)-based fault-tolerant control (FTC) scheme is proposed to solve the attitude tracking control problem of the hypersonic vehicle (HSV). In the field of HSV, the MPSP technique has been applied successfully to solve guidance problems of its high computational efficiency. While we try to address the attitude control problem directly using it. The attitude model of HSV with uncertainty and disturbance, together with the fault model of aircraft body injury, is constructed first. The actuator of HSV is suffering from input constraints. Then, a feasible attitude control trajectory is generated by the improved MPSP method. The methodological innovation in this article extends the MPSP technique to the direct control of the attitude of HSV both in the fixed and flexible final time. By utilizing the improved MPSP technique, the complexity of processing multiple constraints and the computation is reduced. The effectiveness of the designed FTC scheme is demonstrated through simulation under different cases with actuator constraints.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"136-145"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

High-Resolution Mobile Mapping Platform Using 15-mm Accuracy LiDAR and SPAN/TerraStar C-PRO Technologies 使用15毫米精度激光雷达和SPAN/TerraStar C-PRO技术的高分辨率移动地图平台

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-01-30 DOI: 10.1109/JMASS.2023.3240892

Fraj Hariz;Yassine Bouslimani;Mohsen Ghribi

{"title":"High-Resolution Mobile Mapping Platform Using 15-mm Accuracy LiDAR and SPAN/TerraStar C-PRO Technologies","authors":"Fraj Hariz;Yassine Bouslimani;Mohsen Ghribi","doi":"10.1109/JMASS.2023.3240892","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3240892","url":null,"abstract":"Nowadays, most of the mobile mapping systems (MMSs) use global navigation satellite system (GNSS)/inertial navigation system positioning technology and 2-D sensors to construct maps, self-localize, and gather environmental information, as well. Several problems can arise with traditional architectures of these systems, especially in situations where the GNSS signal is unavailable or multiple paths are involved, such as reliability issues and poor accuracy. Moreover, their cost of up to U.S. \u0000<inline-formula> <tex-math>$$ $ </tex-math></inline-formula>\u00002 million still poses a significant challenge for the development of new geographical information system applications. This article proposes a new design of an MMS that incorporates a 1.5-cm accurate 3-D light detection and ranging sensor and a high-accuracy positioning system based on synchronous position attitude and navigation (SPAN)/TerraStar C-PRO technologies. The extended Kalman filter was used in this research to reduce the impact of GNSS signal loss by combining the simultaneous localization and mapping (SLAM) method with SPAN/TerraStar C-PRO technologies. In the experiments, the concept of our mobile mapping platform was validated using the simulation environment Gazebo. So as to evaluate the proposed platform, a real dataset was collected from a complex environment where the GNSS signal is rarely available, exactly, from the campus of Moncton—Université de Moncton. The obtained results disclosed that the proposed platform proves its performance in terms of accuracy and reliability. Due to the integration of the SLAM algorithm with SPAN/TerraStarC-PRO technologies, the generated 3-D point cloud map includes a number of 285 million points with a mean accuracy 0.28 m even in the case of GNSS signal loss.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"122-135"},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Distributed Gradient Descent Method for Node Localization on Large-Scale Wireless Sensor Network 大规模无线传感器网络节点定位的分布式梯度下降方法

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-01-13 DOI: 10.1109/JMASS.2023.3236765

Mou Ma;Shasha Xu;Junzheng Jiang

{"title":"A Distributed Gradient Descent Method for Node Localization on Large-Scale Wireless Sensor Network","authors":"Mou Ma;Shasha Xu;Junzheng Jiang","doi":"10.1109/JMASS.2023.3236765","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3236765","url":null,"abstract":"A distributed iterative method is proposed to solve the problem of node (sensor) localization for large-scale wireless sensor network (WSN), by leveraging the graph topology decomposition and gradient descent method. First, the undirected graph representing the WSN is divided into several overlapping subgraphs. Based on the decomposition subgraphs, the localization problem is splitting into a series of subproblems each of which resides on one subgraph. The iterative procedure is proceeded on the subgraphs and each iteration consists of two operators. The first operator is solving the subproblem in every subgraph by using the gradient descent method which possesses light computational cost, and the second operator is to fuse and average the local positions of nodes in the overlapping region of adjacent subgraphs. In order to enrich the available information of localization, the positions of the target nodes with high localization accuracy are used as the (pseudo) anchor nodes for the subsequent iteration. Owing to that the operators are accomplished on subgraphs with small sizes, the proposed distributed iterative method possesses low computational cost, making it suitable for large-scale WSN. Numerical results are included to demonstrate the effectiveness of the proposed localization method.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"114-121"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Matrix Completion Method Based on TNNR and Total Row Difference for Recovering Optical Image 基于TNNR和全行差的鲁棒矩阵补全方法恢复光学图像

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-01-12 DOI: 10.1109/JMASS.2023.3236302

Xinrun Tian;Shuisheng Zhou;Tiantian Meng

{"title":"Robust Matrix Completion Method Based on TNNR and Total Row Difference for Recovering Optical Image","authors":"Xinrun Tian;Shuisheng Zhou;Tiantian Meng","doi":"10.1109/JMASS.2023.3236302","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3236302","url":null,"abstract":"Matrix completion aims to recover a matrix from an incomplete matrix with many unknown elements and has wide applications in optical image recovery and machine learning, in which the popular method is to formulate it as a general low-rank matrix approximation problem. However, the traditional optimization model for matrix completion is less robust. This article proposes a robust matrix completion method in which the truncated nuclear norm regularization (TNNR) is used as the approximation of the rank function and the sum of absolute values of the row difference, which is called the total row difference, is used to constrain the oscillations of the missing matrix. By minimizing the value of the total row difference in the objective, the proposed model controls the oscillation and reduces the impact of missing parts in the process of matrix completion continuously. Furthermore, we propose a two-step iterative algorithm framework and design an ADMM algorithm for the subproblem model that includes minimizing the total row difference. Experiments show that the proposed algorithm has more stable performance and better recovery effect and obviously reduces the sensitivity of the traditional TNNR models to the truncated rank parameter.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"105-113"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight Tracking of Satellite Video Object Based on Saliency Enhancement Mechanism 基于显著性增强机制的卫星视频目标轻量化跟踪

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-01-04 DOI: 10.1109/JMASS.2023.3234099

Jianhu Liang;Jiayi Sun;Xumei Zhang;Mingming Bian;Fukun Bi

{"title":"Lightweight Tracking of Satellite Video Object Based on Saliency Enhancement Mechanism","authors":"Jianhu Liang;Jiayi Sun;Xumei Zhang;Mingming Bian;Fukun Bi","doi":"10.1109/JMASS.2023.3234099","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3234099","url":null,"abstract":"Target tracking based on satellite platforms in remote sensing images plays a critical role in military and civilian fields. However, most of the traditional algorithms are still aimed at natural scenes and are difficult to be directly applied to complex satellite images with large fields of view and weak contrast. Therefore, the method of tracking satellite videos based on a multidimensional enhancement mechanism is proposed. For the problem that the complex background in satellite images, which makes the target difficult to be correctly captured and identified, the triplet attention module is introduced to enhance the significance of the target in an efficient way, thereby improving the performance of the tracking network; because of the large computational complexity of a deep convolution network, the network structure with the ghost feature is adopted, and some traditional convolution operations are replaced by simple linear operations, which improves the speed of the network. Finally, with the support of satellite remote sensing datasets, the effectiveness of this method is verified through qualitative and quantitative experiments.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"100-104"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Spatial Feature Transformation for Oriented Aerial Object Detection 面向空中目标检测的深度空间特征变换

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-01-04 DOI: 10.1109/JMASS.2023.3234076

Yangte Gao;Zhihao Che;Lin Li;Jianfeng Gao;Fukun Bi

{"title":"Deep Spatial Feature Transformation for Oriented Aerial Object Detection","authors":"Yangte Gao;Zhihao Che;Lin Li;Jianfeng Gao;Fukun Bi","doi":"10.1109/JMASS.2023.3234076","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3234076","url":null,"abstract":"Object detection in aerial images has received extensive attention in the field of computer vision. Different from natural images, the aerial objects are usually distributed in any direction. Therefore, the existing detector usually needs more parameters to encode the direction information, resulting in a large number of redundant calculations. In addition, because an ordinary convolution neural network (CNN) does not effectively model the direction change, a large amount of the rotated data is required for the aerial detector. To solve these problems, we propose a deep spatial feature transformation network (DSFT-Net), which includes a spatial feature extraction module and a feature selection module. Specifically, we add the rotation convolution kernel to the detector to extract the directional feature of the rotated target to accurately predict the direction of the model. Then, we build a dual pyramid to separate the features in the classification and regression tasks. Finally, the polarization function is proposed to construct the critical features that are suitable for their respective tasks, achieving feature selection and more refined detection. Experiments on public remote sensing benchmarks (e.g., DOTA, HRSC2016, and UCAS-AOD) have proved the effectiveness of our detector.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"93-99"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual Tracking With Reinforced Template Updating and Redetection Discriminator 基于增强模板更新和重检鉴别器的视觉跟踪

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2022-12-21 DOI: 10.1109/JMASS.2022.3228339

Shan Zhong;Yuya Sun;Shengrong Gong;Lifan Zhou;Gengsheng Xie

{"title":"Visual Tracking With Reinforced Template Updating and Redetection Discriminator","authors":"Shan Zhong;Yuya Sun;Shengrong Gong;Lifan Zhou;Gengsheng Xie","doi":"10.1109/JMASS.2022.3228339","DOIUrl":"https://doi.org/10.1109/JMASS.2022.3228339","url":null,"abstract":"Though many deep-learning-based trackers for visual object tracking have achieved state-of-the-art performance on multiple benchmarks, they still suffer from significant variations in object appearance and loss of the object. To capture variations of the object appearance, this article proposes a template matching network for object tracking, where deep reinforcement learning is introduced to learn how to update the template. Specifically, the template updating problem is modeled to a Markov decision process where the proximal policy optimization (PPO) algorithm is applied to learn the policy of updating the current template. The resultant template updating policy not only considers the variations of the object but also estimates the influence of current updating for the following frames. To further handle the sudden loss of the object, a two-class redetection discriminator is proposed to conclude whether the object is lost or not. If the object is believed to be lost, a global redetection will be launched to locate the target. Experimentally, the proposed method is compared with some representative methods on dataset OTB2015, and experimental results show that our method can get competitive performance on both accuracy and frame speed.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 1","pages":"70-75"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49953275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interference Countermeasure System Based on Time–Frequency Domain Characteristics 基于时频域特性的干扰对抗系统

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2022-12-20 DOI: 10.1109/JMASS.2022.3229499

Lining Duan;Siyu Du;Yinghui Quan;Qinzhe Lv;Shuai Li;Mengdao Xing

引用次数: 2

High-Resolution mmWave SAR Imagery for Automotive Parking Assistance 用于汽车泊车辅助的高分辨率毫米波SAR图像

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2022-12-06 DOI: 10.1109/JMASS.2022.3226771

Gang Xu;Hao Pei;Mengjie Jiang;Jianlai Chen;Hui Wang;Hui Zhang;Yanyang Liu

引用次数: 0