Pub Date : 2023-07-27DOI: 10.1109/JMASS.2023.3299330
Lifan Zhou;Wenjie Xing;Jie Zhu;Yu Xia;Shan Zhong;Shengrong Gong
High-resolution pixel-level classification of the roads and rivers in the remote sensing system has extremely important application value and has been a research focus which is received extensive attention from the remote sensing society. In recent years, deep convolutional neural networks (DCNNs) have been used in the pixel-level classification of remote sensing images, which has shown extraordinary performance. However, the traditional DCNNs mostly produce discontinuous and incomplete pixel-level classification results when dealing with thin-stripped roads and rivers. To solve the above problem, we put forward a high-resolution strong fusion network (abbreviated as HRSF-Net) which can keep the feature map at high resolution and minimize the texture information loss of the thin-stripped target caused by multiple downsampling operations. In addition, a pixel relationship enhancement and dual-channel attention (PRE-DCA) module is proposed to fully explore the strong correlation between the thin-stripped target pixels, and a hetero-resolution fusion (HRF) module is also proposed to better fuse the feature maps with different resolutions. The proposed HRSF-Net is examined on the two public remote sensing datasets. The ablation experimental result verifies the effectiveness of each module of the HRSF-Net. The comparative experimental result shows that the HRSF-Net has achieved mIoU of 79.05% and 64.46% on the two datasets, respectively, which both outperform some advanced pixel-level classification methods.
{"title":"HRSF-Net: A High-Resolution Strong Fusion Network for Pixel-Level Classification of the Thin-Stripped Target for Remote Sensing System","authors":"Lifan Zhou;Wenjie Xing;Jie Zhu;Yu Xia;Shan Zhong;Shengrong Gong","doi":"10.1109/JMASS.2023.3299330","DOIUrl":"10.1109/JMASS.2023.3299330","url":null,"abstract":"High-resolution pixel-level classification of the roads and rivers in the remote sensing system has extremely important application value and has been a research focus which is received extensive attention from the remote sensing society. In recent years, deep convolutional neural networks (DCNNs) have been used in the pixel-level classification of remote sensing images, which has shown extraordinary performance. However, the traditional DCNNs mostly produce discontinuous and incomplete pixel-level classification results when dealing with thin-stripped roads and rivers. To solve the above problem, we put forward a high-resolution strong fusion network (abbreviated as HRSF-Net) which can keep the feature map at high resolution and minimize the texture information loss of the thin-stripped target caused by multiple downsampling operations. In addition, a pixel relationship enhancement and dual-channel attention (PRE-DCA) module is proposed to fully explore the strong correlation between the thin-stripped target pixels, and a hetero-resolution fusion (HRF) module is also proposed to better fuse the feature maps with different resolutions. The proposed HRSF-Net is examined on the two public remote sensing datasets. The ablation experimental result verifies the effectiveness of each module of the HRSF-Net. The comparative experimental result shows that the HRSF-Net has achieved mIoU of 79.05% and 64.46% on the two datasets, respectively, which both outperform some advanced pixel-level classification methods.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"368-375"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74977486","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}
Objectives: To improve the recognition accuracy of radar signals under a low signal-to-noise ratio (SNR). Technology or Method: We propose a novel radar signal recognition method based on a dual-channel model with the histogram of oriented gradients (HOG) feature extraction. Specifically, multisynchrosqueezing transform (MSST) and Choi–Williams distribution (CWD) transform are adopted individually to obtain the time–frequency distribution images of radar signals, and HOG feature extraction is performed on the preprocessed time–frequency images of each channel, respectively. Then, the features of the two channels are fused and dimensionally reduced by the principal component analysis (PCA). Finally, the compact feature parameters are fed to the support vector machine (SVM) classifier to identify radar signals. Clinical or Biological Impact: The experimental results demonstrate that the proposed model achieves a high recognition performance with a small computational complexity, especially in low SNR. When the SNR is −12 dB, the recognition accuracy can reach more than 92%, which is over 6% higher than that of single-channel models and related convolutional neural network-based models.
{"title":"Radar Signal Recognition Based on Dual-Channel Model With HOG Feature Extraction","authors":"Zeyu Tang;Daying Quan;Xiaofeng Wang;Ning Jin;Dongping Zhang","doi":"10.1109/JMASS.2023.3299159","DOIUrl":"10.1109/JMASS.2023.3299159","url":null,"abstract":"Objectives: To improve the recognition accuracy of radar signals under a low signal-to-noise ratio (SNR). Technology or Method: We propose a novel radar signal recognition method based on a dual-channel model with the histogram of oriented gradients (HOG) feature extraction. Specifically, multisynchrosqueezing transform (MSST) and Choi–Williams distribution (CWD) transform are adopted individually to obtain the time–frequency distribution images of radar signals, and HOG feature extraction is performed on the preprocessed time–frequency images of each channel, respectively. Then, the features of the two channels are fused and dimensionally reduced by the principal component analysis (PCA). Finally, the compact feature parameters are fed to the support vector machine (SVM) classifier to identify radar signals. Clinical or Biological Impact: The experimental results demonstrate that the proposed model achieves a high recognition performance with a small computational complexity, especially in low SNR. When the SNR is −12 dB, the recognition accuracy can reach more than 92%, which is over 6% higher than that of single-channel models and related convolutional neural network-based models.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"358-367"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10195159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76093604","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}
Pub Date : 2023-07-18DOI: 10.1109/JMASS.2023.3296433
Guilherme F. Carvalho;Fabio A. A. Andrade;Gabryel S. Ramos;Alessandro R. L. Zachi;Ana L. F. de Barros;Milena F. Pinto
Unmanned aerial vehicles (UAVs) have been used in different applications due to their flexibility in maneuvering and performing missions. However, they can face external disturbances, such as wind, which can cause physical instability of the platform. Usually, UAVs commonly use a classical PID controller due to their simple structure and less dependence on the model. However, this classical controller requires expertise from the operator to adjust the parameters when dealing with nonlinearities. Therefore, this work proposes the integration of a slide mode control (SMC) controller into a PX4 flight control unit (FCU) and combining it with computer vision techniques and sensor data fusion to enable autonomous UAV offshore cargo tasks for the Oil & Gas sector. The controller was evaluated in a software in the loop (SITL) simulation performed in the robot operating system (ROS), demonstrating its robustness and potential for small marine cargo transportation using UAVs.
{"title":"Sliding Mode Controller Applied to Autonomous UAV Operation in Marine Small Cargo Transport","authors":"Guilherme F. Carvalho;Fabio A. A. Andrade;Gabryel S. Ramos;Alessandro R. L. Zachi;Ana L. F. de Barros;Milena F. Pinto","doi":"10.1109/JMASS.2023.3296433","DOIUrl":"10.1109/JMASS.2023.3296433","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have been used in different applications due to their flexibility in maneuvering and performing missions. However, they can face external disturbances, such as wind, which can cause physical instability of the platform. Usually, UAVs commonly use a classical PID controller due to their simple structure and less dependence on the model. However, this classical controller requires expertise from the operator to adjust the parameters when dealing with nonlinearities. Therefore, this work proposes the integration of a slide mode control (SMC) controller into a PX4 flight control unit (FCU) and combining it with computer vision techniques and sensor data fusion to enable autonomous UAV offshore cargo tasks for the Oil & Gas sector. The controller was evaluated in a software in the loop (SITL) simulation performed in the robot operating system (ROS), demonstrating its robustness and potential for small marine cargo transportation using UAVs.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"345-357"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10185965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82649124","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}
Pub Date : 2023-07-11DOI: 10.1109/JMASS.2023.3293861
Bo Wen;Guoyao Xiao;Zongzheng Sun;Guisheng Liao;Fei Xie;Yinghui Quan
The miniaturization of memory systems is of great significance to the miniaturization of aerospace electronic systems, and double data rate (DDR) memory is prone to serious signal integrity (SI) problems due to its high-frequency and high-speed characteristics. Eye simulation analysis is often time-consuming and does not provide insightful guidance for link optimization and requires further circuit modeling and mathematical analysis. Based on a multichip DDR microsystem design, this article proposes a circuit model of links under different topologies by taking a representative multilevel bonding interconnection structure as an example and establishes a mathematical model of DDR received signal through theoretical calculation. At the same time, we summarize the quantitative relationship between the bonding wire parameters and the related SI problems by substituting the actual circuit parameters into the mathematical model formula. Finally, the theoretical analysis results and simulation results are compared and verified through circuit simulation, and the error is analyzed. The results show that the circuit model and theoretical analysis method can quantitatively analyze the SI problem from a mathematical perspective within a certain error range, and the method and conclusion can be used to guide the early design and later optimization of the DDR memory microsystem.
{"title":"A Modeling and Computational Analysis Method for Multichip DDR Microsystem","authors":"Bo Wen;Guoyao Xiao;Zongzheng Sun;Guisheng Liao;Fei Xie;Yinghui Quan","doi":"10.1109/JMASS.2023.3293861","DOIUrl":"10.1109/JMASS.2023.3293861","url":null,"abstract":"The miniaturization of memory systems is of great significance to the miniaturization of aerospace electronic systems, and double data rate (DDR) memory is prone to serious signal integrity (SI) problems due to its high-frequency and high-speed characteristics. Eye simulation analysis is often time-consuming and does not provide insightful guidance for link optimization and requires further circuit modeling and mathematical analysis. Based on a multichip DDR microsystem design, this article proposes a circuit model of links under different topologies by taking a representative multilevel bonding interconnection structure as an example and establishes a mathematical model of DDR received signal through theoretical calculation. At the same time, we summarize the quantitative relationship between the bonding wire parameters and the related SI problems by substituting the actual circuit parameters into the mathematical model formula. Finally, the theoretical analysis results and simulation results are compared and verified through circuit simulation, and the error is analyzed. The results show that the circuit model and theoretical analysis method can quantitatively analyze the SI problem from a mathematical perspective within a certain error range, and the method and conclusion can be used to guide the early design and later optimization of the DDR memory microsystem.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"336-344"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72972464","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}
Pub Date : 2023-07-04DOI: 10.1109/JMASS.2023.3292259
Xindi Wang;Hao Liu;Qing Gao
In this article, the optimal real-time decision making and near-optimal path planning problem for multiagent systems subject to bounded state, collision avoidance, external disturbance, and partially unknown nonlinear dynamics of the multiagent system in complex games, is addressed and applied to the unmanned aerial vehicle. A mean-field decision-making model based on the neighbor information is established to transform the decision-making problem into a Bellman equation solving problem. A data-driven dynamic programming algorithm is proposed to solve the Bellman equation and generate an optimal strategy using the data from the historical database and expert knowledge. The near-optimal path planning problem is formulated with an optimal coordination control problem, and an online integral reinforcement learning algorithm is proposed to iteratively interact with the environment to obtain a near-optimal path. Simulation results are provided to verify the effectiveness of the proposed methods.
{"title":"Data-Driven Decision Making and Near-Optimal Path Planning for Multiagent System in Games","authors":"Xindi Wang;Hao Liu;Qing Gao","doi":"10.1109/JMASS.2023.3292259","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3292259","url":null,"abstract":"In this article, the optimal real-time decision making and near-optimal path planning problem for multiagent systems subject to bounded state, collision avoidance, external disturbance, and partially unknown nonlinear dynamics of the multiagent system in complex games, is addressed and applied to the unmanned aerial vehicle. A mean-field decision-making model based on the neighbor information is established to transform the decision-making problem into a Bellman equation solving problem. A data-driven dynamic programming algorithm is proposed to solve the Bellman equation and generate an optimal strategy using the data from the historical database and expert knowledge. The near-optimal path planning problem is formulated with an optimal coordination control problem, and an online integral reinforcement learning algorithm is proposed to iteratively interact with the environment to obtain a near-optimal path. Simulation results are provided to verify the effectiveness of the proposed methods.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 3","pages":"320-328"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49966667","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}
The availability of high-resolution imagery resources for semantic segmentation research has expanded significantly due to the rapid development of remote-sensing technology utilizing unmanned aerial vehicles (UAVs). These images provide researchers with a more accurate view of the region of interest and allow for more detailed analysis and interpretation of the images. However, semantic segmentation based on UAV remote-sensing imagery still faces new challenges in deriving ground objects. In contrast to the commonly used multispectral (MS) imagery, thermal infrared (TIR) imagery can record the emission of ground objects, making the temperature characteristics of TIR imagery and the color characteristics of MS imagery complementary. These two approaches can be used synergistically to provide more comprehensive image information. On this basis, we propose a strategy for semantic segmentation of UAV images by utilizing both TIR and MS image features. The approach combines principal component analysis (PCA) transformation with a deep learning semantic segmentation network, namely, Deeplv3. The effectiveness of the proposed strategy is evaluated by comparing it with both traditional supervised classification algorithms and deep learning algorithms. According to the results, the proposed strategy exhibits greater robustness, achieving a mean pixel accuracy (MPA) of 92.8% and a mean intersection over union (MIOU) of 73.5%. These results outperform several classical deep learning semantic segmentation algorithms that were also evaluated. The proposed strategy would be beneficial to promote the development of semantic segmentation technology for UAV remote-sensing images.
{"title":"UAV Remote-Sensing Image Semantic Segmentation Strategy Based on Thermal Infrared and Multispectral Image Features","authors":"Pakezhamu Nuradili;Ji Zhou;Xiangbing Zhou;Jin Ma;Ziwei Wang;Lingxuan Meng;Wenbin Tang;Yizhen Meng","doi":"10.1109/JMASS.2023.3286418","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3286418","url":null,"abstract":"The availability of high-resolution imagery resources for semantic segmentation research has expanded significantly due to the rapid development of remote-sensing technology utilizing unmanned aerial vehicles (UAVs). These images provide researchers with a more accurate view of the region of interest and allow for more detailed analysis and interpretation of the images. However, semantic segmentation based on UAV remote-sensing imagery still faces new challenges in deriving ground objects. In contrast to the commonly used multispectral (MS) imagery, thermal infrared (TIR) imagery can record the emission of ground objects, making the temperature characteristics of TIR imagery and the color characteristics of MS imagery complementary. These two approaches can be used synergistically to provide more comprehensive image information. On this basis, we propose a strategy for semantic segmentation of UAV images by utilizing both TIR and MS image features. The approach combines principal component analysis (PCA) transformation with a deep learning semantic segmentation network, namely, Deeplv3. The effectiveness of the proposed strategy is evaluated by comparing it with both traditional supervised classification algorithms and deep learning algorithms. According to the results, the proposed strategy exhibits greater robustness, achieving a mean pixel accuracy (MPA) of 92.8% and a mean intersection over union (MIOU) of 73.5%. These results outperform several classical deep learning semantic segmentation algorithms that were also evaluated. The proposed strategy would be beneficial to promote the development of semantic segmentation technology for UAV remote-sensing images.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 3","pages":"311-319"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49966668","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}
Pub Date : 2023-06-14DOI: 10.1109/JMASS.2023.3286271
Xinsheng He;Ming Deng;Bingjie Chai;Wenlong Dong;Zhaohui Zhang;Chunmin Wu;Yuqi Wang
The synthetic aperture passive localization system generally compensates for the second-order phase term of the received signal with the Taylor series of the range history and then uses the focusing result of the compensated signal to obtain the position of the emitter. However, the existence of a higher-order residual phase causes the mismatch of reference function, leading to the bias of localization results. To solve the problem, this article proposes a slant range expansion method based on an orthogonal basis. The optimal expansion of the range history is obtained by constructing a set of orthogonal bases in the space composed of quadratic polynomials so that the residual phase after integration is minimized. The proposed method can effectively mitigate the localization bias caused by the model approximation of a synthetic aperture localization system. Simulations and Monte Carlo tests show that the proposed method outperforms the traditional synthetic aperture localization method.
{"title":"Synthetic Aperture Passive Localization Method Based on Slant Range Orthogonal Expansion","authors":"Xinsheng He;Ming Deng;Bingjie Chai;Wenlong Dong;Zhaohui Zhang;Chunmin Wu;Yuqi Wang","doi":"10.1109/JMASS.2023.3286271","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3286271","url":null,"abstract":"The synthetic aperture passive localization system generally compensates for the second-order phase term of the received signal with the Taylor series of the range history and then uses the focusing result of the compensated signal to obtain the position of the emitter. However, the existence of a higher-order residual phase causes the mismatch of reference function, leading to the bias of localization results. To solve the problem, this article proposes a slant range expansion method based on an orthogonal basis. The optimal expansion of the range history is obtained by constructing a set of orthogonal bases in the space composed of quadratic polynomials so that the residual phase after integration is minimized. The proposed method can effectively mitigate the localization bias caused by the model approximation of a synthetic aperture localization system. Simulations and Monte Carlo tests show that the proposed method outperforms the traditional synthetic aperture localization method.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 3","pages":"305-310"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49966669","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}
Pub Date : 2023-03-29DOI: 10.1109/JMASS.2023.3279411
Ravi Teja Nallapu;Yinan Xu;Tristan Schuler;Jekan Thangavelautham
The next frontier in space exploration involves visiting some of the 2 million small bodies scattered throughout the solar system. However, these missions are expected to be challenging due to the surface irregularities of these bodies and the very low gravity, which makes steps like getting into orbit very complex. For these reasons, reconnaissance is crucial for small-body exploration before taking on ambitious orbital, surface, and sample-return missions. Our previous work developed IDEAS, an automated design software for small-body reconnaissance mission development using spacecraft swarms. A critical challenge to furthering such designs is the lack of hardware demonstration platforms for interplanetary spacecraft operations. In this article, we present multiagent photogrammetry of small bodies (MAPS), a hardware platform to demonstrate critical reconnaissance operations of multispacecraft missions identified by the IDEAS framework. MAPS uses unmanned air vehicles (UAVs) as the autonomous agents that perform reconnaissance operations. The UAVs use their visual feed to generate a 3-D surface map of a small-body mockup, which is encountered along their flight path. In this article, we examine the various design elements of a small-body surface reconstruction mission inside the MAPS testbed. These elements are used for designing reference trajectories of the participating UAVs, which is enforced using a tracking feedback control law. We then formulate the small-body mapping problem as a mixed-integer nonlinear programming problem, which is handled by the Automated Swarm Designer module of the IDEAS framework. The solutions are implemented inside the MAPS, and shape models generated from the UAV feeds are compared.
{"title":"Development of a Hardware Demonstration Platform for Multispacecraft Reconnaissance of Small Bodies","authors":"Ravi Teja Nallapu;Yinan Xu;Tristan Schuler;Jekan Thangavelautham","doi":"10.1109/JMASS.2023.3279411","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3279411","url":null,"abstract":"The next frontier in space exploration involves visiting some of the 2 million small bodies scattered throughout the solar system. However, these missions are expected to be challenging due to the surface irregularities of these bodies and the very low gravity, which makes steps like getting into orbit very complex. For these reasons, reconnaissance is crucial for small-body exploration before taking on ambitious orbital, surface, and sample-return missions. Our previous work developed IDEAS, an automated design software for small-body reconnaissance mission development using spacecraft swarms. A critical challenge to furthering such designs is the lack of hardware demonstration platforms for interplanetary spacecraft operations. In this article, we present multiagent photogrammetry of small bodies (MAPS), a hardware platform to demonstrate critical reconnaissance operations of multispacecraft missions identified by the IDEAS framework. MAPS uses unmanned air vehicles (UAVs) as the autonomous agents that perform reconnaissance operations. The UAVs use their visual feed to generate a 3-D surface map of a small-body mockup, which is encountered along their flight path. In this article, we examine the various design elements of a small-body surface reconstruction mission inside the MAPS testbed. These elements are used for designing reference trajectories of the participating UAVs, which is enforced using a tracking feedback control law. We then formulate the small-body mapping problem as a mixed-integer nonlinear programming problem, which is handled by the Automated Swarm Designer module of the IDEAS framework. The solutions are implemented inside the MAPS, and shape models generated from the UAV feeds are compared.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 3","pages":"283-304"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49966670","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}
Pub Date : 2023-03-23DOI: 10.1109/JMASS.2023.3273095
{"title":"The Journal of Miniaturized Air and Space Systems","authors":"","doi":"10.1109/JMASS.2023.3273095","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3273095","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8253411/10131919/10131923.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964196","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}
In the traditional processing methods of azimuth multichannel spaceborne synthetic aperture radar (SAR), the azimuth spectrum reconstruction and subsequent azimuth focusing are always via full-aperture processing. However, if the multichannel full-aperture echo data are stored on the satellite, and then the full-aperture algorithms are used for the on-board imaging processing, the huge amount of echo data will require more on-board storage resources and computing resources, and the imaging processing time will become longer. To solve the above problems, a novel on-board imaging processing algorithm via the idea that the data acquisition and the on-board imaging processing of the subaperture data are carried out simultaneously is proposed in this article. In the algorithm, the azimuth spectrum ambiguity is eliminated by the subaperture azimuth spectrum reconstruction. Then, the range cell migration correction (RCMC) and the range compression for the unambiguous subaperture signals are accomplished by the chirp scaling algorithm (CSA). After that, the low-resolution subaperture images are got via the subaperture focusing. By coherently combining all subaperture images, the final result with high resolution of all echo data can be obtained. Finally, the simulation for the point targets is given to verify the effectiveness of the proposed algorithm.
{"title":"An On-Board Imaging Processing Algorithm for Stripmap Mode of Azimuth Multichannel Spaceborne SAR","authors":"Yanbin Liu;Dongxu Chen;Wenjie Xing;Xuan Zhou;Guang-Cai Sun;Jiarong Xiao;Yue Cao;Shuai Jiang;Shuchen Guo;Zhongjun Yu;Mengdao Xing","doi":"10.1109/JMASS.2023.3278572","DOIUrl":"10.1109/JMASS.2023.3278572","url":null,"abstract":"In the traditional processing methods of azimuth multichannel spaceborne synthetic aperture radar (SAR), the azimuth spectrum reconstruction and subsequent azimuth focusing are always via full-aperture processing. However, if the multichannel full-aperture echo data are stored on the satellite, and then the full-aperture algorithms are used for the on-board imaging processing, the huge amount of echo data will require more on-board storage resources and computing resources, and the imaging processing time will become longer. To solve the above problems, a novel on-board imaging processing algorithm via the idea that the data acquisition and the on-board imaging processing of the subaperture data are carried out simultaneously is proposed in this article. In the algorithm, the azimuth spectrum ambiguity is eliminated by the subaperture azimuth spectrum reconstruction. Then, the range cell migration correction (RCMC) and the range compression for the unambiguous subaperture signals are accomplished by the chirp scaling algorithm (CSA). After that, the low-resolution subaperture images are got via the subaperture focusing. By coherently combining all subaperture images, the final result with high resolution of all echo data can be obtained. Finally, the simulation for the point targets is given to verify the effectiveness of the proposed algorithm.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 4","pages":"330-335"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82540240","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}
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