IEEE Journal on Miniaturization for Air and Space Systems最新文献_第5页

Design Methodology for Single-Feed Circularly Polarized X-Band Antenna Arrays for CubeSats Using Multilevel Sequential Rotation 利用多级顺序旋转为立方体卫星设计单馈电圆极化 X 波段天线阵列的方法学

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-11-17 DOI: 10.1109/JMASS.2023.3333833

Daylon Hester;Seokhee Han;Mark Adams

{"title":"Design Methodology for Single-Feed Circularly Polarized X-Band Antenna Arrays for CubeSats Using Multilevel Sequential Rotation","authors":"Daylon Hester;Seokhee Han;Mark Adams","doi":"10.1109/JMASS.2023.3333833","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3333833","url":null,"abstract":"This article presents a streamlined design methodology for single-feed circularly polarized antenna arrays for CubeSats. The presented method was created with student-led teams in mind and employs a geometrically simple approach, opting for circular patches and ring-shaped feed networks instead of complex geometries. High- and low-impedance radiating elements are designed, and design restrictions are introduced such that all other geometries may be solved through a set of simple cascading equations. These deliberate choices minimize the number of design parameters and simplify the design process. Circular polarization is achieved through a multilevel implementation of sequentially arranged linearly polarized circular patches fed in a series-parallel fashion by ring-shaped feed lines of constant impedance. This article also demonstrates a \u0000<inline-formula> <tex-math>$4times 4$ </tex-math></inline-formula>\u0000 right-hand circularly polarized (RHCP) CubeSat downlink array antenna designed for operation in the 8025–8400-MHz Earth exploration satellite band which was developed using the proposed methodology. The antenna comprises four sequentially rotated RHCP subarrays, each consisting of four sequentially rotated linearly polarized circular patches. The antenna’s boresight RHCP gain exceeds 16.19 dBic at 8.389 GHz with a simulated 27.9% 3-dB axial ratio bandwidth, a 20° half-power beamwidth, and an aperture efficiency of 53%. The antenna has a sub-2 VSWR bandwidth of 26.6%, and its radiation efficiency ranges from 60% to 82% across the target band. Its compact size of 9 cm \u0000<inline-formula> <tex-math>$times $ </tex-math></inline-formula>\u0000 9 cm enables it to fit on one face of a 10 cm \u0000<inline-formula> <tex-math>$times $ </tex-math></inline-formula>\u0000 10 cm CubeSat unit.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 1","pages":"42-50"},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139942845","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

Hybrid CNN and Transformer Network for Semantic Segmentation of UAV Remote Sensing Images 用于无人机遥感图像语义分割的混合 CNN 和变压器网络

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-11-15 DOI: 10.1109/JMASS.2023.3332948

Xuanyu Zhou;Lifan Zhou;Shengrong Gong;Haizhen Zhang;Shan Zhong;Yu Xia;Yizhou Huang

{"title":"Hybrid CNN and Transformer Network for Semantic Segmentation of UAV Remote Sensing Images","authors":"Xuanyu Zhou;Lifan Zhou;Shengrong Gong;Haizhen Zhang;Shan Zhong;Yu Xia;Yizhou Huang","doi":"10.1109/JMASS.2023.3332948","DOIUrl":"10.1109/JMASS.2023.3332948","url":null,"abstract":"Semantic segmentation of unmanned aerial vehicle (UAV) remote sensing images is a recent research hotspot, offering technical support for diverse types of UAV remote sensing missions. However, unlike general scene images, UAV remote sensing images present inherent challenges. These challenges include the complexity of backgrounds, substantial variations in target scales, and dense arrangements of small targets, which severely hinder the accuracy of semantic segmentation. To address these issues, we propose a convolutional neural network (CNN) and transformer hybrid network for semantic segmentation of UAV remote sensing images. The proposed network follows an encoder–decoder architecture that merges a transformer-based encoder with a CNN-based decoder. First, we incorporate the Swin transformer as the encoder to address the limitations of CNN in global modeling, mitigating the interference caused by complex background information. Second, to effectively handle the significant changes in target scales, we design the multiscale feature integration module (MFIM) that enhances the multiscale feature representation capability of the network. Finally, the semantic feature fusion module (SFFM) is designed to filter the redundant noise during the feature fusion process, which improves the recognition of small targets and edges. Experimental results demonstrate that the proposed method outperforms other popular methods on the UAVid and Aeroscapes datasets.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 1","pages":"33-41"},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135759026","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

Dim and Small Target Detection Method via Gradient Features Guided Local Contrast 通过梯度特征引导局部对比度的微小目标检测方法

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-11-03 DOI: 10.1109/JMASS.2023.3330014

Wei Shi;Mingliang Chen;Junchao Zhang

引用次数: 0

Hardware/Software Co-Design of a Feature-Based Satellite Pose Estimation System 基于特征的卫星姿态估计系统的硬件/软件协同设计

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-10-31 DOI: 10.1109/JMASS.2023.3328879

Yunjie Liu;Anne Bettens;Xiaofeng Wu

{"title":"Hardware/Software Co-Design of a Feature-Based Satellite Pose Estimation System","authors":"Yunjie Liu;Anne Bettens;Xiaofeng Wu","doi":"10.1109/JMASS.2023.3328879","DOIUrl":"10.1109/JMASS.2023.3328879","url":null,"abstract":"Vision-based pose estimation is fundamental for close proximity satellite operations, especially for on-orbit service missions. While neural network methods for pose estimation are becoming more widespread, traditional computer vision techniques still offer unique benefits in terms of efficiency and reliability. This article presents an algorithm that uses feature point detection and random sample consensus (RANSAC) as a solution for satellite pose estimation. The proposed algorithm requires no initialization, previous pose, or motion state information, which significantly reduces processing time. A comparison was conducted between the proposed algorithm and neural-network-based approaches. It was found that the proposed method only needs minimal training samples and memory to produce high-precision pose estimation, making it appropriate for use on small satellite platforms, such as CubeSats. Moreover, the satellite pose estimation implementation was achieved through hardware/software (HW/SW) co-design, by implementing the feature point detection module on a field-programmable gate array (FPGA). This approach takes full advantage of an FPGA’s pipeline structure and the ability for parallel operation of software and hardware. Consequently, it offers an efficient solution for satellite pose estimation with improved operational efficiency, resource utilization, and low power consumption.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 1","pages":"16-26"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135262846","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

An Improved Chaotic Self-Adapting Monkey Algorithm for Multi-UAV Task Assignment 用于多无人机任务分配的改进型混沌自适应猴算法

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-10-26 DOI: 10.1109/JMASS.2023.3327721

Yujuan Cui

引用次数: 0

Integrated Convolution Network for ISAR Imaging and Target Recognition 集成卷积网络用于ISAR成像和目标识别

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-10-18 DOI: 10.1109/JMASS.2023.3325526

Haoze Du;Peishuang Ni;Jianlai Chen;Shuai Ma;Hui Zhang;Gang Xu

引用次数: 0

Reinforcement Learning-Based 3-D Sliding Mode Interception Guidance via Proximal Policy Optimization 基于近端策略优化的强化学习三维滑模拦截制导

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-10-17 DOI: 10.1109/JMASS.2023.3325054

Jianguo Guo;Mengxuan Li;Zongyi Guo;Zhiyong She

引用次数: 0

Pan Evaporation Prediction Using LSTM Models Based on PCA Factor Reduction and Firefly Optimization Algorithm 基于PCA因子约简和萤火虫优化算法的LSTM蒸发皿蒸发量预测

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-09-26 DOI: 10.1109/JMASS.2023.3319579

Chuanli Wang;Tianyu Li;Dongjun Xin;Qian Wang;Ran Chen;Chaoyi Cao

{"title":"Pan Evaporation Prediction Using LSTM Models Based on PCA Factor Reduction and Firefly Optimization Algorithm","authors":"Chuanli Wang;Tianyu Li;Dongjun Xin;Qian Wang;Ran Chen;Chaoyi Cao","doi":"10.1109/JMASS.2023.3319579","DOIUrl":"10.1109/JMASS.2023.3319579","url":null,"abstract":"Evaporation is an important part of the moisture exchange between the earth and the air. Understanding the trend of pan evaporation can help to reveal the status of actual evaporation, which is very useful for the allocation of regional water resources. However, long short-term memory (LSTM) has become a mainstream algorithm for predicting pan evaporation, there are two issues worth considering. One of the issues is how to automatically find the optimal hyperparameters, the other is how to eliminate the correlation between prediction factors to improve prediction performance. To address the two issues, this article proposes LSTM models based on principal component analysis (PCA) factor reduction and firefly optimization algorithm. In the proposed model, fire-fly algorithm can find the optimal hyperparameters, and PCA can eliminate the correlation between prediction factors. Xiangjiang River Basin, an important Basin for China’s water resource management, is selected as a study area, the experimental results are evaluated by root mean square error (RMSE) and the coefficient of determination (\u0000<inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula>\u0000). The results show that the proposed models can successfully predict daily pan evaporation of the study area.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"416-422"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10263773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135754786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-Length and On-Wafer Probe-Based Broadband and Rapid Characterization of Substrate Dielectric Constant for Aerospace Applications 航空航天应用中基于单长度和片上探针的衬底介电常数宽带和快速表征

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-09-13 DOI: 10.1109/JMASS.2023.3314982

Longzhu Cai;Xin Xu;Gang Xu

{"title":"Single-Length and On-Wafer Probe-Based Broadband and Rapid Characterization of Substrate Dielectric Constant for Aerospace Applications","authors":"Longzhu Cai;Xin Xu;Gang Xu","doi":"10.1109/JMASS.2023.3314982","DOIUrl":"10.1109/JMASS.2023.3314982","url":null,"abstract":"The space environment can exert an influence on the dielectric properties of dielectric substrates, and potential alterations in substrate dielectric constant could significantly impact the performance and reliability of spaceborne devices and systems, which might lead to mission failure. This work presents a technique for rapid and broadband characterization of substrate dielectric constant by performing only a single measurement of a single transmission line based on the ground–signal–ground (GSG) on-wafer probe for aerospace applications. Another extraction technique by the use of welding microwave connector is also discussed for comparison. Unlike previously reported techniques that require two or more transmission lines and welding connectors, our method owns the merits of avoiding connector repeatability and additional parasitic elements, easy and fast to implement without prior knowledge of substrate dielectric constant, low analysis complexity, less fabrication efforts, and being applicable to most dielectric substrates. This study offers valuable insights for airborne and spaceborne platforms with limited space, simultaneously mitigating costs and complexity, rendering it an appealing proposition for aerospace applications.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"408-415"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135402796","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

The Journal of Miniaturized Air and Space Systems 小型化航空航天系统杂志

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-08-22 DOI: 10.1109/JMASS.2023.3295110

引用次数: 0