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

Deep-Learning-Based Mask-Cut Method for InSAR Phase Unwrapping 基于深度学习的掩模切割InSAR相位展开方法

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-03-16 DOI: 10.1109/JMASS.2023.3258379

Kai Yang;Zhihui Yuan;Xuemin Xing;Lifu Chen

{"title":"Deep-Learning-Based Mask-Cut Method for InSAR Phase Unwrapping","authors":"Kai Yang;Zhihui Yuan;Xuemin Xing;Lifu Chen","doi":"10.1109/JMASS.2023.3258379","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3258379","url":null,"abstract":"Two-dimensional phase unwrapping (2D-PU) is a key processing step for interferometric synthetic aperture radar (InSAR) and it plays an important role in InSAR data processing. For the phase unwrapping (PU) problem, many scholars began to consider using the deep learning (DL) technology in the field of artificial intelligence. By accumulating InSAR PU processing experience through DL, the learning-based PU method can surpass the traditional PU algorithm sometimes. Therefore, this article designs a mask-cuts (MCs) deployment network based on DL, which is named MCNet, and the PU method based on this network is also known as MCNet-PU. First, the residues images and its corresponding MCs images are obtained by using the traditional MC method as the training data and testing data. Second, the relationship between residues and MCs is learned through the training of the self-built MCNet. Then, the trained MCNet is used to obtain the MCs corresponding to the interferogram to be unwrapped. Finally, the unwrapped result is obtained by phase integration using the traditional flood fill method. Compared with the traditional MC method, MCNet does not need to use the quality map to guide the deployment of the MCs, nor does it need to refine the MCs, and it can make the deployment of the MCs more accurate. Experiments on simulated and real InSAR data show that the MCNet-PU method can improve the phase unwrapping success ratio (PUSR) by about 4%–15%, which shows the effectiveness of the method.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"221-230"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964255","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: 3

Precision Terrain Modeling Approach in Complex Mountainous Areas Based on Compact UAV Ka-InSAR Data 基于Ka-InSAR数据的复杂山区精确地形建模方法

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-03-16 DOI: 10.1109/JMASS.2023.3276949

Fei Liu;Shuang Li;Yaoquan Jing;Jia Liu;Han Hu;Quan Gan;Tingting Zhao;Yuling Ding;Xing Pan;Shuo Deng;Qing Zhu

{"title":"Precision Terrain Modeling Approach in Complex Mountainous Areas Based on Compact UAV Ka-InSAR Data","authors":"Fei Liu;Shuang Li;Yaoquan Jing;Jia Liu;Han Hu;Quan Gan;Tingting Zhao;Yuling Ding;Xing Pan;Shuo Deng;Qing Zhu","doi":"10.1109/JMASS.2023.3276949","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3276949","url":null,"abstract":"A high-precision digital elevation model (DEM) is useful for disaster investigation and evaluation in cloudy, rainy, and complex mountainous areas. However, clouds and rain make the optical images and laser point-cloud data acquisition difficult, while noise prohibits obtaining accurate surface information. Additionally, the complex elevation difference in mountainous areas increases the data processing difficulty, such as phase unwrapping (PU) and filtering. To overcome these problems, first, we introduce a new airborne multibaseline Ka-interferometric synthetic aperture radar (InSAR) system developed by the Beijing Institute of Radio Measurement. The system affords high resolution and small volume, is lightweight, has a good top-view angle, and is flexible. Thus, it reduces the flight platform’s dependence and improves the aircraft’s adaptability and universality. Moreover, a multibaseline PU method of a two-stage programming approach (TSPA) is selected to overcome the influence of severe noise and the phase continuity assumption limitation. Additionally, an adaptive filtering method for InSAR point clouds considering coherence and optimal bending energy is proposed. This method’s validity is verified using stereo satellite images, ground observation point precision checks, and geomorphic texture analysis against existing DEM results. The experimental results demonstrate that the proposed scheme has a good filtering effect on noise, vegetation, residential building areas, and bridges, significantly reducing manual intervention. Moreover, the results highlight that our method is well integrated with stereo images and has more texture details than conventional stereo mapping results, with a mean square error of elevation of 1.938 m.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 3","pages":"257-266"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49966673","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

Saturated Control With Variable Prescribed Performance Applied to the Manipulator of UAV 无人机机械臂的变规定性能饱和控制

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-03-14 DOI: 10.1109/JMASS.2023.3257177

Xiangwei Bu

引用次数: 2

Processing of Airborne Microwave Photonic SAR Raw Data With Inaccurate RSF RSF不准确的机载微波光子SAR原始数据处理

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-03-13 DOI: 10.1109/JMASS.2022.3226183

Jianlai Chen;Mengliang Li;Mengdao Xing;Gang Xu;Yucan Zhu;Ruoming Li;Wangzhe Li

引用次数: 2

Deep Neural Network-Based Dynamical Object Recognition and Robust Multiobject Tracking Technique for Onboard Unmanned Aerial Vehicle’s Computer Vision-Based Systems 基于深度神经网络的机载无人机动态目标识别与鲁棒多目标跟踪技术

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-03-10 DOI: 10.1109/JMASS.2023.3274929

Ivan V. Saetchnikov;Victor V. Skakun;Elina A. Tcherniavskaia

{"title":"Deep Neural Network-Based Dynamical Object Recognition and Robust Multiobject Tracking Technique for Onboard Unmanned Aerial Vehicle’s Computer Vision-Based Systems","authors":"Ivan V. Saetchnikov;Victor V. Skakun;Elina A. Tcherniavskaia","doi":"10.1109/JMASS.2023.3274929","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3274929","url":null,"abstract":"Computer vision-based systems seem highly perspective for semantic analysis of the dynamical objects. However, considering dynamical object recognition and tracking from the unmanned aerial vehicle (UAV) the task to design a robust model for data association is highly challenging due to additional issues, e.g., image degradation, nonfixed object camera distance and shooting focus, and real-time issues. Thus, we propose an accurate deep neural network-based dynamical object recognition and robust multiobject tracking technique based on bidirectional LSTM with the optimized motion and appearance gates as a multiobject tracking backbone, supported by an advanced single-shot detector network improved with residual prediction model and implemented a DenseNet network as well as a YOLOv4eff network as feature extraction. The technique has been trained on VisDrone 2022 and UAVDT datasets with the side-shoot dynamical objects at a height of up to 50 m. The performance analysis on the test stage performed on seven metrics demonstrate that the proposed technique surpasses, by accuracy and robustness ability, other state-of-the-art techniques based on two cumulative MOTA and MOTP, as well as MT and IDsw. In particular, we have dramatically decreased the number of IDsw which implies a better capability to handle several occlusions, which is a desirable property in real-time multiple object tracking. We have pointed out the sensitivity of the tracking performance of our technique on the number of utilizing different sequence lengths and have defined an optimum. Finally, the applicability and reliability of the proposed technique for onboard UAV computer-based systems have been discussed.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 3","pages":"250-256"},"PeriodicalIF":0.0,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49966672","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

Ship Target Detection Algorithm Based on Decision-Level Fusion of Visible and SAR Images 基于决策级可见光和SAR图像融合的舰船目标检测算法

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-03-01 DOI: 10.1109/JMASS.2023.3269434

Jianlai Chen;Xiaoqing Xu;Junchao Zhang;Gang Xu;Yucan Zhu;Buge Liang;Degui Yang

引用次数: 1

On-Board Computer and Testing Platform for CubeSat Development 面向立方体卫星开发的星载计算机与测试平台

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-02-28 DOI: 10.1109/JMASS.2023.3250581

Koffi V. C. K. de Souza;Yassine Bouslimani;Mohsen Ghribi;Tobie Boutot

{"title":"On-Board Computer and Testing Platform for CubeSat Development","authors":"Koffi V. C. K. de Souza;Yassine Bouslimani;Mohsen Ghribi;Tobie Boutot","doi":"10.1109/JMASS.2023.3250581","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3250581","url":null,"abstract":"The design and development of a CubeSat testing platform built from scratch is the focus of this work. The investigation was conducted as part of the Canadian CubeSat Project (CCP), an initiative conducted by the Canadian Space Agency (CSA) to support the development of 15 CubeSats across Canada. In this article, a particular emphasis is placed on three key subsystems: 1) an on-board computer (OBC); 2) a global navigation satellite system (GNSS)-based payload; and 3) a communication board, all connected together through a FlatSat board. The mission software running on an STM32-microcontroller (MCU)-based OBC is responsible for managing all CubeSat activities. The OBC was designed to meet a range of requirements, including mechanical, electrical, and thermal requirements. Indeed, due to the intense heat and radiation that the CubeSat will be exposed to in low-Earth orbit (LEO), the CubeSat may experience many difficulties, potentially leading to mission failure. The risk-reduction techniques used in the design of the OBC will be discussed in detail. The tests performed on the developed OBC were successful, including an initial power test and a vacuum test, where the MCU entered low-power mode for a total of 10 s, consuming only 0.0528 W of power.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"199-211"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964257","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

FlatSat Platforms for Small Satellites: A Systematic Mapping and Classification 小卫星平面卫星平台:系统制图与分类

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-02-24 DOI: 10.1109/JMASS.2023.3249044

João Cláudio Elsen Barcellos;Anderson Wedderhoff Spengler;Laio Oriel Seman;Raphael Diego Comesanha e Silva;Héctor Pettenghi Roldán;Eduardo Augusto Bezerra

{"title":"FlatSat Platforms for Small Satellites: A Systematic Mapping and Classification","authors":"João Cláudio Elsen Barcellos;Anderson Wedderhoff Spengler;Laio Oriel Seman;Raphael Diego Comesanha e Silva;Héctor Pettenghi Roldán;Eduardo Augusto Bezerra","doi":"10.1109/JMASS.2023.3249044","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3249044","url":null,"abstract":"Recent trends indicate an increase in the number of small satellite missions, which can be developed more quickly and at a lower cost than traditional satellites. This has led to a growing interest in university-based satellite development, despite a lack of expertise in the space field, which has resulted in a high failure rate for such missions. To address this issue, the implementation of robust and reliable verification and validation (V&V) methods has become essential, and it has been demonstrated that the use of a FlatSat during the V&V campaign increases reliability. Despite the significance of FlatSat, there is a dearth of information on the platforms used to implement it, as well as a classification scheme for locating them. This article contributes to bridging this gap by conducting a systematic mapping of 65 works that were selected based on specific criteria and subsequently analyzed. The primary characteristics of the platforms are enumerated, and a new classification for FlatSat platforms into Raw, Bridge, Dock, and Modular is proposed. In order to provide a comprehensive understanding of the topic, the principal tests conducted on these platforms were also covered.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"186-198"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964258","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 : 2023-02-22 DOI: 10.1109/JMASS.2023.3235675

引用次数: 0

A Robust Complex-Valued Deep Neural Network for Target Recognition of UAV SAR Imagery 基于鲁棒复值深度神经网络的无人机SAR图像目标识别

IEEE Journal on Miniaturization for Air and Space Systems Pub Date : 2023-02-22 DOI: 10.1109/JMASS.2023.3247586

Cheng Fang;Yumeng Song;Fangheng Guan;Feifei Liang;Lei Yang

{"title":"A Robust Complex-Valued Deep Neural Network for Target Recognition of UAV SAR Imagery","authors":"Cheng Fang;Yumeng Song;Fangheng Guan;Feifei Liang;Lei Yang","doi":"10.1109/JMASS.2023.3247586","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3247586","url":null,"abstract":"Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) plays an important role in modern remote sensing for its characteristics of all weather, all day-and-night, zero casualty, flying flexibility, and low cost. However, the atmospheric turbulence will cause motion errors to UAV SAR, resulting in unmodeled phase errors. The phase errors will degrade the focusing quality of the image and bring difficulties to the recognition task. Meanwhile, it is difficult for a convolution neural network (CNN) to extract and utilize the back-scattering information for target recognition. To this end, a novel defocusing adaptive complex CNN (DA-CCNN) is proposed, which can realize the overall computation of the network in the complex-valued data domain and effectively extract the phase history information of the complex-valued data. Furthermore, it is the first time that the image entropy metric is introduced into the fully complex deep neural network to improve the focusing quality of the image and the interpretability of the network. The experiment is carried out using the benchmark dataset of MSTAR 10. In order to simulate the defocused images generated by UAV SAR and certify the robustness to phase errors, datasets with the contamination are also applied. The results show that on the benchmark data, the recognition accuracy of DA-CCNN is comparable to that of the existing methods. On the data with phase errors, DA-CCNN shows stronger robustness and higher accuracy in terms of recognition than the reported networks.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"175-185"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964259","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