Pub Date : 2023-03-14DOI: 10.1109/JMASS.2023.3257177
Xiangwei Bu
Variable prescribed performance control (PPC) is investigated for a type of nonlinear dynamic systems subject to actuator saturation, with an application to the manipulator of unmanned aerial vehicles (UAVs). Different from the current state-of-the-art, new performance functions are proposed to construct a variable prescribed funnel which is able to be readjusted according to the saturation situation. Furthermore, a new auxiliary system is developed to provide timely and bounded compensations on ideal control inputs. Thereby, the control singular problem associated with the existing PPC, caused by a saturated actuator, is effectively handled, and moreover, the addressed control protocol exhibits nonfragility to actuator saturation. In addition, the robustness of control is guaranteed via neural approximation. Finally, compared simulations on the manipulator of UAVs are presented to validate the design.
{"title":"Saturated Control With Variable Prescribed Performance Applied to the Manipulator of UAV","authors":"Xiangwei Bu","doi":"10.1109/JMASS.2023.3257177","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3257177","url":null,"abstract":"Variable prescribed performance control (PPC) is investigated for a type of nonlinear dynamic systems subject to actuator saturation, with an application to the manipulator of unmanned aerial vehicles (UAVs). Different from the current state-of-the-art, new performance functions are proposed to construct a variable prescribed funnel which is able to be readjusted according to the saturation situation. Furthermore, a new auxiliary system is developed to provide timely and bounded compensations on ideal control inputs. Thereby, the control singular problem associated with the existing PPC, caused by a saturated actuator, is effectively handled, and moreover, the addressed control protocol exhibits nonfragility to actuator saturation. In addition, the robustness of control is guaranteed via neural approximation. Finally, compared simulations on the manipulator of UAVs are presented to validate the design.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"212-220"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964256","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 : 2023-03-13DOI: 10.1109/JMASS.2022.3226183
Jianlai Chen;Mengliang Li;Mengdao Xing;Gang Xu;Yucan Zhu;Ruoming Li;Wangzhe Li
Due to system instability and other reasons, the actual range sampling frequency (RSF) of the system may deviate from the ideal value for the microwave photonic synthetic aperture radar (SAR). This deviation may lead to severe residual range cell migration (RCM) and even range defocus after imaging, which can seriously affect the image quality. To resolve this problem, this article proposes an airborne microwave photonic SAR imaging algorithm based on inaccurate system parameter estimation. First, the algorithm estimates and compensates for the range spatial-variant motion error to eliminate the effect of this motion error on the remaining RCM and range defocus. Second, based on the minimum entropy criterion of the image, we use the optimization model to estimate the actual RSF. Finally, the existing wide-beam autofocus method is used to correct the azimuth spatial-variant motion error. The simulation data and the measured data processing results verify the effectiveness of the proposed method.
{"title":"Processing of Airborne Microwave Photonic SAR Raw Data With Inaccurate RSF","authors":"Jianlai Chen;Mengliang Li;Mengdao Xing;Gang Xu;Yucan Zhu;Ruoming Li;Wangzhe Li","doi":"10.1109/JMASS.2022.3226183","DOIUrl":"https://doi.org/10.1109/JMASS.2022.3226183","url":null,"abstract":"Due to system instability and other reasons, the actual range sampling frequency (RSF) of the system may deviate from the ideal value for the microwave photonic synthetic aperture radar (SAR). This deviation may lead to severe residual range cell migration (RCM) and even range defocus after imaging, which can seriously affect the image quality. To resolve this problem, this article proposes an airborne microwave photonic SAR imaging algorithm based on inaccurate system parameter estimation. First, the algorithm estimates and compensates for the range spatial-variant motion error to eliminate the effect of this motion error on the remaining RCM and range defocus. Second, based on the minimum entropy criterion of the image, we use the optimization model to estimate the actual RSF. Finally, the existing wide-beam autofocus method is used to correct the azimuth spatial-variant motion error. The simulation data and the measured data processing results verify the effectiveness of the proposed method.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"86-92"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964254","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 : 2023-03-10DOI: 10.1109/JMASS.2023.3274929
Ivan V. Saetchnikov;Victor V. Skakun;Elina A. Tcherniavskaia
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.
{"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}
Pub Date : 2023-03-01DOI: 10.1109/JMASS.2023.3269434
Jianlai Chen;Xiaoqing Xu;Junchao Zhang;Gang Xu;Yucan Zhu;Buge Liang;Degui Yang
Aiming at the problem of target detection for multiple source information fusion, in this article, a decision-level fusion algorithm for visible and SAR images is proposed. First, using the Faster-RCNN network detects visible and SAR images to retain the detection results, respectively. Second, the semantic segmentation of visible images based on U-Net is realized. Finally, based on the detection results of single source and semantic segmentation results of land and sea, a fusion strategy based on decision level is proposed to achieve accurate target detection under multisource information. Through experimental verification, the detection performance of the proposed algorithm is an advantage over that of single-source image detection. The detection accuracy is 2.87% and 4.73% higher, and the recall rate is 3.02% and 0.19% higher than that of visible and SAR images separately. Compared with other target detection algorithms based on traditional image fusion, the proposed method has fewer false detections and missed detections.
{"title":"Ship Target Detection Algorithm Based on Decision-Level Fusion of Visible and SAR Images","authors":"Jianlai Chen;Xiaoqing Xu;Junchao Zhang;Gang Xu;Yucan Zhu;Buge Liang;Degui Yang","doi":"10.1109/JMASS.2023.3269434","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3269434","url":null,"abstract":"Aiming at the problem of target detection for multiple source information fusion, in this article, a decision-level fusion algorithm for visible and SAR images is proposed. First, using the Faster-RCNN network detects visible and SAR images to retain the detection results, respectively. Second, the semantic segmentation of visible images based on U-Net is realized. Finally, based on the detection results of single source and semantic segmentation results of land and sea, a fusion strategy based on decision level is proposed to achieve accurate target detection under multisource information. Through experimental verification, the detection performance of the proposed algorithm is an advantage over that of single-source image detection. The detection accuracy is 2.87% and 4.73% higher, and the recall rate is 3.02% and 0.19% higher than that of visible and SAR images separately. Compared with other target detection algorithms based on traditional image fusion, the proposed method has fewer false detections and missed detections.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 3","pages":"242-249"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49966950","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 : 2023-02-28DOI: 10.1109/JMASS.2023.3250581
Koffi V. C. K. de Souza;Yassine Bouslimani;Mohsen Ghribi;Tobie Boutot
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.
{"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}
Pub Date : 2023-02-24DOI: 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
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.
{"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}
Pub Date : 2023-02-22DOI: 10.1109/JMASS.2023.3235675
{"title":"The Journal of Miniaturized Air and Space Systems","authors":"","doi":"10.1109/JMASS.2023.3235675","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3235675","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 1","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8253411/10050211/10050213.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49953253","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 : 2023-02-22DOI: 10.1109/JMASS.2023.3247586
Cheng Fang;Yumeng Song;Fangheng Guan;Feifei Liang;Lei Yang
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.
{"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}
Pub Date : 2023-02-14DOI: 10.1109/JMASS.2023.3244848
Bai Zhu;Liang Zhou;Simiao Pu;Jianwei Fan;Yuanxin Ye
Over the past few decades, with the rapid development of global aerospace and aerial remote sensing technology, the types of sensors have evolved from the traditional monomodal sensors (e.g., optical sensors) to the new generation of multimodal sensors (e.g., multispectral, hyperspectral, light detection and ranging (LiDAR), and synthetic aperture radar (SAR) sensors). These advanced devices can dynamically provide various and abundant multimodal remote sensing images (MRSIs) with different spatial, temporal, and spectral resolutions according to different application requirements. Since then, it is of great scientific significance to carry out the research of MRSI registration, which is a crucial step for integrating the complementary information among multimodal data and making comprehensive observations and analysis of the Earth’s surface. In this work, we will present our own contributions to the field of multimodal image registration, summarize the advantages and limitations of existing multimodal image registration methods, and then discuss the remaining challenges and make a forward-looking prospect for the future development of the field.
{"title":"Advances and Challenges in Multimodal Remote Sensing Image Registration","authors":"Bai Zhu;Liang Zhou;Simiao Pu;Jianwei Fan;Yuanxin Ye","doi":"10.1109/JMASS.2023.3244848","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3244848","url":null,"abstract":"Over the past few decades, with the rapid development of global aerospace and aerial remote sensing technology, the types of sensors have evolved from the traditional monomodal sensors (e.g., optical sensors) to the new generation of multimodal sensors (e.g., multispectral, hyperspectral, light detection and ranging (LiDAR), and synthetic aperture radar (SAR) sensors). These advanced devices can dynamically provide various and abundant multimodal remote sensing images (MRSIs) with different spatial, temporal, and spectral resolutions according to different application requirements. Since then, it is of great scientific significance to carry out the research of MRSI registration, which is a crucial step for integrating the complementary information among multimodal data and making comprehensive observations and analysis of the Earth’s surface. In this work, we will present our own contributions to the field of multimodal image registration, summarize the advantages and limitations of existing multimodal image registration methods, and then discuss the remaining challenges and make a forward-looking prospect for the future development of the field.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"165-174"},"PeriodicalIF":0.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964260","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 : 2023-02-07DOI: 10.1109/JMASS.2023.3243110
Xiaomao Chen;Ying Huang;Chao He;Xianming Xie
In this article, we proposed a phase unwrapping (PU) method which combines with unscented Kalman filter, pixel classification, and an efficient path-following strategy. The characteristics of the proposed method are summarized as: 1) the path-following strategy speeds up the process of PU without decreasing the accuracy; 2) the reliability of each pixel will be graded according to the position of residue and pixel classification strategy; and 3) different from the traditional methods, the proposed method can perform filtering and PU at the same time to prevent global propagation of error. In addition, we also introduce a signal model which can obtain a similar correlation map by only using a wrapped phase image when without the primary-secondary image. The results on synthetic data and real data show that the proposed method can obtain better results.
{"title":"An Efficient Phase Unwrapping Method Based on Unscented Kalman Filter","authors":"Xiaomao Chen;Ying Huang;Chao He;Xianming Xie","doi":"10.1109/JMASS.2023.3243110","DOIUrl":"https://doi.org/10.1109/JMASS.2023.3243110","url":null,"abstract":"In this article, we proposed a phase unwrapping (PU) method which combines with unscented Kalman filter, pixel classification, and an efficient path-following strategy. The characteristics of the proposed method are summarized as: 1) the path-following strategy speeds up the process of PU without decreasing the accuracy; 2) the reliability of each pixel will be graded according to the position of residue and pixel classification strategy; and 3) different from the traditional methods, the proposed method can perform filtering and PU at the same time to prevent global propagation of error. In addition, we also introduce a signal model which can obtain a similar correlation map by only using a wrapped phase image when without the primary-secondary image. The results on synthetic data and real data show that the proposed method can obtain better results.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 2","pages":"157-164"},"PeriodicalIF":0.0,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49964261","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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