IEEE Journal on Miniaturization for Air and Space Systems最新文献_第4页

Wide-Band Wide-Angle Beamsteerable Meta-Lens Antenna for Terrestrial/Nonterrestrial 5G Communication Systems 用于地面/非地面 5G 通信系统的宽带宽角度可转向元透镜天线

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2024-04-04 DOI: 10.1109/JMASS.2024.3385699

Suchitra Tiwari;Amit K. Singh;Ankit Dubey

{"title":"Wide-Band Wide-Angle Beamsteerable Meta-Lens Antenna for Terrestrial/Nonterrestrial 5G Communication Systems","authors":"Suchitra Tiwari;Amit K. Singh;Ankit Dubey","doi":"10.1109/JMASS.2024.3385699","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3385699","url":null,"abstract":"A highly efficient low-profile binary metasurface lens (BMSL) antenna is designed and developed to achieve wide-angle beamsteering at the millimeter-wave band of fifth-generation (5G) aerospace communication systems. First, a subwavelength-sized phase-shift element (meta-element) with a crossed-arrow geometry having two-line symmetry structure is designed possessing special characteristics of insensitivity to polarization as well as the oblique angle of incidence, wide-band transmission, and compactness. Further, 1-bit quantized radial phase-graded metasurface lens is designed by arranging the proposed elements in \u0000<inline-formula> <tex-math>$19times19$ </tex-math></inline-formula>\u0000 array resulting in an aperture area of \u0000<inline-formula> <tex-math>$33.6~lambda _{0}^{2}$ </tex-math></inline-formula>\u0000. To realize beamsteering along 0°, ±15°, ±30°, ±45°, and ±60°, BMSLs with distinct phase-quantization are designed and spatially fed through antipodal Vivaldi antenna (AVA) which acts as a primary feed source positioned at optimum focal point thereby radiating highly concentrated beams in the intended directions. The complete BMSL antenna system is then fabricated and characterized in an ideal free-space environment achieving a measured peak gain of up to 20.8 dBi in broadside direction and 1.6 dB of maximum scan loss for ±60° steering. The proposed BMSL antenna achieves an aperture efficiency of 28.4 % at 28 GHz and a −3-dB gain bandwidth of 16.5 %. Thus, the proposed BMSL antenna is a promising contender for facilitating terrestrial (air) as well as nonterrestrial (space) communication links between low-Earth orbit satellites and 5G base stations.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 2","pages":"117-124"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091131","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

Multibaseline Phase Unwrapping With a Refined Parametric Pure Integer Programming for Noise Suppression 利用精炼参数纯整数编程进行多基线相位解包以抑制噪声

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2024-04-04 DOI: 10.1109/JMASS.2024.3385026

Jiawei Yue;Qihuan Huang;Hui Liu;Ziqi He;Hanwen Zhang

引用次数: 0

Enhanced Low-Rank Matrix Decomposition for High-Resolution UAV-SAR Imagery 用于高分辨率无人机-合成孔径雷达成像的增强型低阶矩阵分解

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2024-03-29 DOI: 10.1109/JMASS.2024.3406783

Bin Gao;Anna Song;Hanwen Xu;Zenan Zhang;Wenhui Lian;Lei Yang

{"title":"Enhanced Low-Rank Matrix Decomposition for High-Resolution UAV-SAR Imagery","authors":"Bin Gao;Anna Song;Hanwen Xu;Zenan Zhang;Wenhui Lian;Lei Yang","doi":"10.1109/JMASS.2024.3406783","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3406783","url":null,"abstract":"Low-rank matrix decomposition is effective for sparse recovery. However, the conventions are limited in accuracy for high-resolution synthetic aperture radar (SAR) imagery due to the shrinkage effect in the cost function, which leads to biased estimates. To this end, an enhanced-low rank matrix decomposition (E-LRMD) SAR imaging algorithm is proposed, which employs a factor group-sparse regularization (FGSR) to approximate the intended cost function, so that the low-rank features can be represented. Since, the constructed regularization function is factorized, the singular value decomposition is avoided, and the computational burden can be reduced accordingly. Furthermore, \u0000<inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>\u0000-norm is incorporated to encode the sparse feature. To incorporate with the enhancement of multiple features, the alternating direction method of multipliers (ADMM) framework is utilized. Therefore, both the low-rank and sparse features can be accurately recovered and enhanced, cooperatively, where the error propagation between the enhancement of multiple features is minimized. In the experiments, the effectiveness and robustness of the algorithm are verified by the simulated data and practical UAV-SAR data, respectively. Also, a phase transition diagram (PTD) experiment is carried out to analyse the advantages of the proposed algorithm in terms of quantitative aspects compared with the conventional methods.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 3","pages":"187-199"},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041442","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 Adaptive Nonlinear Phase Error Estimation and Compensation Method for Terahertz Radar Imaging System 太赫兹雷达成像系统的自适应非线性相位误差估计与补偿方法

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2024-03-28 DOI: 10.1109/JMASS.2024.3382942

Mengyang Zhan;Jiawei Wu;Yinwei Li;Gang Xu;Yiming Zhu

{"title":"An Adaptive Nonlinear Phase Error Estimation and Compensation Method for Terahertz Radar Imaging System","authors":"Mengyang Zhan;Jiawei Wu;Yinwei Li;Gang Xu;Yiming Zhu","doi":"10.1109/JMASS.2024.3382942","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3382942","url":null,"abstract":"Terahertz (THz) radar imaging has been getting a lot more attention in recent years because it has a faster frame rate and better resolution. However, nonlinear phase errors resulting from the immaturity and instability of THz devices inevitably affect the transmitted signal of THz radar imaging systems, causing the range image to blur. In this work, we present an adaptive correction approach for improving the imaging quality of THz radar by the elimination of nonlinear phase error. First, the nonparametric model is created with high accuracy; this model accounts for nonlinear phase errors introduced by the signal source and other broadband hardware devices like the frequency multiplier. After that, the suggested technique employs nonlinear phase error estimates and compensation by iterative optimization, with the picture contrast of multiple pulse compression serving as the evaluation criterion. The proposed method has been validated through the use of both synthetic data and field data gathered with a 0.22-THz airborne synthetic aperture radar equipment. The experimental results further highlight the suggested method’s high robustness, low computational cost, and several potential uses.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 2","pages":"108-116"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091213","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

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2024-03-27 DOI: 10.1109/JMASS.2024.3381788

Peifeng Ma;Chang Yu;Zherong Wu;Zhanze Wang;Jiehong Chen

{"title":"Mining-Related Subsidence Measurements Using a Robust Multitemporal InSAR Method and Logistic Model","authors":"Peifeng Ma;Chang Yu;Zherong Wu;Zhanze Wang;Jiehong Chen","doi":"10.1109/JMASS.2024.3381788","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3381788","url":null,"abstract":"Ground subsidence is a representative geohazard in mining areas that threatens human safety and infrastructure. Interferometric synthetic aperture radar (InSAR) was used to measure ground subsidence related to mining activities. However, mining areas are often subjected to severe temporal and geometric decorrelation problems, resulting in sparse persistent scatterers (PSs) and lower measurement accuracy. To improve deformation measurements, a robust multitemporal InSAR (MT-InSAR) method that jointly detects PS and distributed scatterers (DSs) in a two-tier network was utilized here. To solve the mismatch in the traditional linear velocity model, a logistic model was introduced for MT-InSAR processing. Forty-four Sentinel-1A SAR images acquired between 1 January 2020 and 30 June 2021 were used to measure ground subsidence in Zhoutaizi Village, Chengde City, Hebei Province, China, which endured geohazards induced and exacerbated by mining activities. We observed that more measurement points were produced using the logistic model (11 607) compared with the constant velocity model (10 980) in the mining areas with an increase of 5.7%, while the mean value of the standard deviation of the estimated residuals reduced from 1.45 to 1.13 with a decrease of 22%. Results are beneficial for geohazard assessment and management in mining areas.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 3","pages":"149-155"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041370","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

A Complex-Valued PolSAR Image Segmentation Network With Lovász-Softmax Loss Optimization 具有 Lovász-Softmax 损失优化功能的复值 PolSAR 图像分割网络

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2024-03-26 DOI: 10.1109/JMASS.2024.3381974

Rui Guo;Xiaopeng Zhao;Liang Guo;Ruiqi Xu;Yi Liang

{"title":"A Complex-Valued PolSAR Image Segmentation Network With Lovász-Softmax Loss Optimization","authors":"Rui Guo;Xiaopeng Zhao;Liang Guo;Ruiqi Xu;Yi Liang","doi":"10.1109/JMASS.2024.3381974","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3381974","url":null,"abstract":"In recent years, complex-valued convolutional neural networks (CNNs) have emerged as a promising approach for polarimetric synthetic aperture radar (PolSAR) image segmentation by utilizing both amplitude and phase information in PolSAR data. This article introduces a complex-valued network for PolSAR image segmentation termed as complex-valued Lovász-softmax loss optimization synthetic aperture radar network (CV-LoSARNet), which is in fact a complex-valued Lovász-softmax loss optimization framework. The bilateral structure of CV-LoSARNet provides efficient feature extraction, while the complex-valued network adapting to PolSAR data can improve feature extraction capabilities. The introduced loss function combines both the Lovász-softmax loss and cross-entropy loss, which can improve the optimization objective of the segmentation. Comparative experiments conducted on E-SAR data and AIRSAR data demonstrate the superiority of the proposed network over the classical full CNN and the classic bilateral networks. Compared with the classic bilateral network, the CV-LoSARNet has improved the mean intersection over union and mean pixel accuracy of E-SAR data sets by 2.37% and 2.29%, for AIRSAR data sets, the improvement is 12.95% and 6.70%. Moreover, the segmentation performance of the proposed network on different polarimetric modes is discussed.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 2","pages":"100-107"},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091153","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

IEEE Journal on Miniaturization for Air and Space Systems Special Issue on Network Intelligence for Unmanned Aerial Vehicles 电气和电子工程师学会《航空航天系统微型化期刊》无人驾驶飞行器网络智能特刊

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2024-03-23 DOI: 10.1109/JMASS.2024.3397068

引用次数: 0

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

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2024-03-23 DOI: 10.1109/JMASS.2024.3397026

引用次数: 0

Attitude Determination and Control in Small Satellites: A Review 小型卫星的姿态确定与控制：综述

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2024-03-20 DOI: 10.1109/JMASS.2024.3402984

Mariana Londoño Orozco;Belarmino Segura Giraldo

引用次数: 0

Decision-Making Method of Multi-UAV Cooperate Air Combat Under Uncertain Environment 不确定环境下多无人机协同空战的决策方法

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2024-03-18 DOI: 10.1109/JMASS.2024.3378726

Jialong Jian;Yong Chen;Qiuni Li;Hongbo Li;Xiaokang Zheng;Chongchong Han

{"title":"Decision-Making Method of Multi-UAV Cooperate Air Combat Under Uncertain Environment","authors":"Jialong Jian;Yong Chen;Qiuni Li;Hongbo Li;Xiaokang Zheng;Chongchong Han","doi":"10.1109/JMASS.2024.3378726","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3378726","url":null,"abstract":"Multi-UAV cooperative air combat has attracted wide attention from relative scholars. However, the decision-making problem of UAV swarm confrontation under uncertain conditions makes it more difficult. In this article, a two-layer decision-making method, containing dynamic target assignment and distributed Monte Carlo tree search (MCTS), is proposed to address this issue. Additionally, the possibility degree function method of interval gray number is combined with a genetic algorithm to deal with uncertain information in an air combat environment. Specifically, considering the actual air combat scene, the target value factor is introduced in the target allocation process, and the dynamic target allocation mechanism is established to adjust the cluster combat strategy in real time. The experiments show that the proposed two-level decision-making method can effectively deal with the swarm air combat problem under uncertain environments. First, the improved genetic algorithm can solve the problem of target allocation in an uncertain environment and give the target allocation scheme in the current state. Moreover, the establishment of the dynamic target allocation mechanism makes the cooperative behavior of UAVs emerge in the swarm, which fully reflects the adversarial air combat.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 3","pages":"138-148"},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041440","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