Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541777
Yonghui Hu;Yi Li;Junkai Wang;Yong Yan
Indoor human localization is of great significance in a variety of applications, including navigation, healthcare, security, and many other location-based services. This article presents a passive indoor localization method that exploits the varying electric fields naturally generated by human activities. An array of electrostatic sensors capable of passive, long-range sensing is developed using charge amplifiers. Human localization is formulated as an inverse problem that aims to reconstruct the charge distribution within the target area from sensor measurements. The spatial sensitivity matrix is preprocessed using QR factorization, and then, compressive sensing is used to find the sparse solution. Experiments were conducted in an office environment of $4.2times 4.2$ m. Results obtained show that the localization accuracy is location-dependent and a median error less than 0.26 m has been achieved. Although the sensor signals are vulnerable to a variety of factors, the localization method exhibits strong robustness against environmental and subject changes.
{"title":"Indoor Human Localization Using Electrostatic Sensors and Compressive Sensing Techniques","authors":"Yonghui Hu;Yi Li;Junkai Wang;Yong Yan","doi":"10.1109/TIM.2025.3541777","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541777","url":null,"abstract":"Indoor human localization is of great significance in a variety of applications, including navigation, healthcare, security, and many other location-based services. This article presents a passive indoor localization method that exploits the varying electric fields naturally generated by human activities. An array of electrostatic sensors capable of passive, long-range sensing is developed using charge amplifiers. Human localization is formulated as an inverse problem that aims to reconstruct the charge distribution within the target area from sensor measurements. The spatial sensitivity matrix is preprocessed using QR factorization, and then, compressive sensing is used to find the sparse solution. Experiments were conducted in an office environment of <inline-formula> <tex-math>$4.2times 4.2$ </tex-math></inline-formula> m. Results obtained show that the localization accuracy is location-dependent and a median error less than 0.26 m has been achieved. Although the sensor signals are vulnerable to a variety of factors, the localization method exhibits strong robustness against environmental and subject changes.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3533938
{"title":"2024 List of Reviewers","authors":"","doi":"10.1109/TIM.2025.3533938","DOIUrl":"https://doi.org/10.1109/TIM.2025.3533938","url":null,"abstract":"","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-146"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10884637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541681
En-Zhu Lyu;Yen-Ling Sung;Dong-Yu Hsu;Guan-Lin Su;Ting-Wei Wang
Body sensor network (BSN) is rapidly evolving in mobile health (m-health), enabling the monitoring of multiple physiological sites through interconnected wireless wearable devices for efficient health management. However, the extensive use of devices on the body often requires substantial batteries and IC chips, increasing system complexity, weight, and cost, thus limiting practical applications. We present a battery-free, chipless physiological sensor employing an on-body passive LC repeater to enhance magnetic coupling between a sensor reader and the biomedical target. The passive LC repeater, encapsulated in flexible thermoplastic polyurethane (TPU), integrates seamlessly with skin or clothing to capture contact and noncontact physiological signals, including heart, lung, carotid, radial, and even femoral signals using an external coil connected to a compact $2.1 times 2.3$ cm, lightweight 7.8 g sensor reader, simplifying signal acquisition, and reducing the complexity of readout techniques compared to cumbersome vector network analyzers (VNAs). To demonstrate its robust performance, the skin-attached passive LC repeater was sprayed with water and fully immersed, yet the sensor reader, positioned 2 cm away, continued to successfully capture physiological signals. In conclusion, this study presents a battery-free, chipless BSN solution utilizing on-body passive LC circuitry characterized by a simple structure, lightweight design, and low cost, ideal for disposable skin electronics and smart clothing, offering superior wearable, unobtrusive, and long-term health monitoring solutions.
{"title":"LC Repeater-Inspired Battery-Free, Chipless, Flexible Body Sensor Network With Lightweight Reader","authors":"En-Zhu Lyu;Yen-Ling Sung;Dong-Yu Hsu;Guan-Lin Su;Ting-Wei Wang","doi":"10.1109/TIM.2025.3541681","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541681","url":null,"abstract":"Body sensor network (BSN) is rapidly evolving in mobile health (m-health), enabling the monitoring of multiple physiological sites through interconnected wireless wearable devices for efficient health management. However, the extensive use of devices on the body often requires substantial batteries and IC chips, increasing system complexity, weight, and cost, thus limiting practical applications. We present a battery-free, chipless physiological sensor employing an on-body passive LC repeater to enhance magnetic coupling between a sensor reader and the biomedical target. The passive LC repeater, encapsulated in flexible thermoplastic polyurethane (TPU), integrates seamlessly with skin or clothing to capture contact and noncontact physiological signals, including heart, lung, carotid, radial, and even femoral signals using an external coil connected to a compact <inline-formula> <tex-math>$2.1 times 2.3$ </tex-math></inline-formula> cm, lightweight 7.8 g sensor reader, simplifying signal acquisition, and reducing the complexity of readout techniques compared to cumbersome vector network analyzers (VNAs). To demonstrate its robust performance, the skin-attached passive LC repeater was sprayed with water and fully immersed, yet the sensor reader, positioned 2 cm away, continued to successfully capture physiological signals. In conclusion, this study presents a battery-free, chipless BSN solution utilizing on-body passive LC circuitry characterized by a simple structure, lightweight design, and low cost, ideal for disposable skin electronics and smart clothing, offering superior wearable, unobtrusive, and long-term health monitoring solutions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In bipolar high-voltage direct current (HVdc) transmission power grids with metal return lines (MRLs), the identification of high impedance faults and the sensitivity of fault location and protection are still challenges. In response to these concerns, a fault location and protection strategy is proposed in an HVdc grid with MRL, which extracts the natural frequency of modal derivative current through the variational mode decomposition-Hilbert transform (VMD-HT) algorithm. First, this study establishes equivalent models of dc grids under different fault types considering the line capacitance. On this basis, the expressions of natural frequency are derived, and the fault location can be accurately inferred from the measured natural frequency. Subsequently, a fault protection method based on a joint criterion of natural frequency and modal derivative current is proposed. Theoretically, this method is not affected by the fault transition resistance and exhibits good sensitivity. Besides, a modal phase plane is introduced for faulty pole selection. Finally, this article establishes PSCAD/EMTDC and experimental platforms to validate the precision of the proposed fault location and protection scheme across different situations.
{"title":"Fault Location and Protection for Metallic Return HVdc Grid Based on Natural Frequency Extraction of Modal Derivative Current","authors":"Chunsheng Guo;Jianquan Liao;Yuhong Wang;Wei Zhang;Yangtao Liu;Hongyu Wang;Dachuan Yu","doi":"10.1109/TIM.2025.3538074","DOIUrl":"https://doi.org/10.1109/TIM.2025.3538074","url":null,"abstract":"In bipolar high-voltage direct current (HVdc) transmission power grids with metal return lines (MRLs), the identification of high impedance faults and the sensitivity of fault location and protection are still challenges. In response to these concerns, a fault location and protection strategy is proposed in an HVdc grid with MRL, which extracts the natural frequency of modal derivative current through the variational mode decomposition-Hilbert transform (VMD-HT) algorithm. First, this study establishes equivalent models of dc grids under different fault types considering the line capacitance. On this basis, the expressions of natural frequency are derived, and the fault location can be accurately inferred from the measured natural frequency. Subsequently, a fault protection method based on a joint criterion of natural frequency and modal derivative current is proposed. Theoretically, this method is not affected by the fault transition resistance and exhibits good sensitivity. Besides, a modal phase plane is introduced for faulty pole selection. Finally, this article establishes PSCAD/EMTDC and experimental platforms to validate the precision of the proposed fault location and protection scheme across different situations.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541669
Shaohu Wang;Huijun Li;Tianyuan Miao;Zhenyu Gao;Aiguo Song
Simultaneous localization and mapping (SLAM) is a critical technology in robotics, with LiDAR-based SLAM has shown remarkable success in outdoor environments. However, real-time, robust, and precise state estimation in indoor environments remains a major challenge. This article presents an innovative 3-D LiDAR SLAM framework that incorporates multilayer horizontal plane optimization to address these challenges. Two key innovations distinguish this method. First, the method introduces a plane clustering segmentation (PCS) technique, which segments the raw point cloud based on horizontal and vertical curvatures. This allows for the simultaneous recognition of feature points and various inclined planes. Combined with feedback-based odometry information, this technique enables the extraction of horizontal plane points from tilted LiDAR data, followed by fitting and LiDAR tilt correction. This effectively mitigates issues arising from LiDAR motion and large-angle rotations, which often lead to failure in ground point extraction. Second, the framework integrates edge and surface feature scan-to-scan matching with horizontal plane optimization to refine LiDAR odometry. In the backend, edge, surface, and horizontal plane features are incorporated into both local maps and global horizontal plane constraints, improving pose and map accuracy, particularly in environments with varying ground heights. Compared to existing methods, this approach significantly suppresses pose drift in long-range and multifloor indoor environments. Evaluations on both public and custom datasets demonstrate an average 35% improvement in localization accuracy over state-of-the-art techniques. Overall, this method provides a promising approach for real-time, robust, and accurate state estimation and map building in indoor environments, offering noticeable improvements for indoor LiDAR SLAM applications.
{"title":"Feature Extraction of Horizontal Plane and Optimization of 3-D LiDAR SLAM in Indoor Environments","authors":"Shaohu Wang;Huijun Li;Tianyuan Miao;Zhenyu Gao;Aiguo Song","doi":"10.1109/TIM.2025.3541669","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541669","url":null,"abstract":"Simultaneous localization and mapping (SLAM) is a critical technology in robotics, with LiDAR-based SLAM has shown remarkable success in outdoor environments. However, real-time, robust, and precise state estimation in indoor environments remains a major challenge. This article presents an innovative 3-D LiDAR SLAM framework that incorporates multilayer horizontal plane optimization to address these challenges. Two key innovations distinguish this method. First, the method introduces a plane clustering segmentation (PCS) technique, which segments the raw point cloud based on horizontal and vertical curvatures. This allows for the simultaneous recognition of feature points and various inclined planes. Combined with feedback-based odometry information, this technique enables the extraction of horizontal plane points from tilted LiDAR data, followed by fitting and LiDAR tilt correction. This effectively mitigates issues arising from LiDAR motion and large-angle rotations, which often lead to failure in ground point extraction. Second, the framework integrates edge and surface feature scan-to-scan matching with horizontal plane optimization to refine LiDAR odometry. In the backend, edge, surface, and horizontal plane features are incorporated into both local maps and global horizontal plane constraints, improving pose and map accuracy, particularly in environments with varying ground heights. Compared to existing methods, this approach significantly suppresses pose drift in long-range and multifloor indoor environments. Evaluations on both public and custom datasets demonstrate an average 35% improvement in localization accuracy over state-of-the-art techniques. Overall, this method provides a promising approach for real-time, robust, and accurate state estimation and map building in indoor environments, offering noticeable improvements for indoor LiDAR SLAM applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-18"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3538084
Zhihong Chen;Jun Zhu
Combat simulation has become crucial in military assessment in recent years due to the rapid development of information technology and artificial intelligence. However, the increasingly large volume of data and the credibility of information have heightened the difficulty of processing and analysis, subsequently affecting the quality of military decision-making. Extracting key features can clarify the core factors influencing the indicators, simplify model complexity, improve prediction accuracy, and save computation time. Quantifying uncertainty helps enhance decision quality and increases the system’s adaptability in uncertain environments. Accordingly, we propose a novel method for key feature selection and interval prediction to address specific regression tasks in combat simulation systems. First, our approach comprehensively considers the importance of features to the target variable, the interaction between features, and redundancy by integrating various feature selection methods, thereby precisely extracting key features. Second, we modify the output structure of traditional neural networks and design a new hybrid loss function to train the model. Furthermore, deep ensemble methods are utilized to enhance diversity and robustness, thus enabling uncertainty evaluation and interval prediction. The experimental results indicate that, after feature selection, the estimation achieved a mean squared error (mse) of only 0.151 and a prediction interval coverage probability (PICP) of 86.99%, providing crucial support for military decision-making.
{"title":"Intelligent Inference in Combat Simulation Systems Based on Key Feature Extraction and Uncertainty Interval Estimation","authors":"Zhihong Chen;Jun Zhu","doi":"10.1109/TIM.2025.3538084","DOIUrl":"https://doi.org/10.1109/TIM.2025.3538084","url":null,"abstract":"Combat simulation has become crucial in military assessment in recent years due to the rapid development of information technology and artificial intelligence. However, the increasingly large volume of data and the credibility of information have heightened the difficulty of processing and analysis, subsequently affecting the quality of military decision-making. Extracting key features can clarify the core factors influencing the indicators, simplify model complexity, improve prediction accuracy, and save computation time. Quantifying uncertainty helps enhance decision quality and increases the system’s adaptability in uncertain environments. Accordingly, we propose a novel method for key feature selection and interval prediction to address specific regression tasks in combat simulation systems. First, our approach comprehensively considers the importance of features to the target variable, the interaction between features, and redundancy by integrating various feature selection methods, thereby precisely extracting key features. Second, we modify the output structure of traditional neural networks and design a new hybrid loss function to train the model. Furthermore, deep ensemble methods are utilized to enhance diversity and robustness, thus enabling uncertainty evaluation and interval prediction. The experimental results indicate that, after feature selection, the estimation achieved a mean squared error (mse) of only 0.151 and a prediction interval coverage probability (PICP) of 86.99%, providing crucial support for military decision-making.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541800
Jiancheng Yin;Wentao Sui;Xuye Zhuang;Yunlong Sheng;Jianjun Wang;Rujun Song;Yongbo Li
Lempel-Ziv (LZ) complexity has been widely applied in multiple fields, and there are numerous improvements in multiscale computation and encoding to enhance its ability to characterize signal changes. Based on the hierarchical analysis, this article proposes an improved LZ indicator based on multiscale decomposition and multiscale encoding, which is applied to the recognition of bearing failure severity. The signal is first decomposed into multiple scales through hierarchical analysis. Next, the decomposed node signal is further decomposed by coarse-grained methods. Then, the multiscale decomposed signal is further decomposed into low- and high-frequency components using hierarchical analysis and the multiscale encoding is performed based on the decomposed low- and high-frequency components. Finally, the LZ complexity is calculated based on multiscale encoding. The effectiveness of the proposed method is validated by three single-point bearing fault datasets with different failure severity. The proposed method can achieve a classification accuracy of over 97%. The proposed method can be effectively applied to classify the bearing failure severity.
{"title":"An Improved Lempel–Ziv Complexity Indicator Based on Multiscale Decomposition and Multiscale Encoding for Bearing Failure Severity Recognition","authors":"Jiancheng Yin;Wentao Sui;Xuye Zhuang;Yunlong Sheng;Jianjun Wang;Rujun Song;Yongbo Li","doi":"10.1109/TIM.2025.3541800","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541800","url":null,"abstract":"Lempel-Ziv (LZ) complexity has been widely applied in multiple fields, and there are numerous improvements in multiscale computation and encoding to enhance its ability to characterize signal changes. Based on the hierarchical analysis, this article proposes an improved LZ indicator based on multiscale decomposition and multiscale encoding, which is applied to the recognition of bearing failure severity. The signal is first decomposed into multiple scales through hierarchical analysis. Next, the decomposed node signal is further decomposed by coarse-grained methods. Then, the multiscale decomposed signal is further decomposed into low- and high-frequency components using hierarchical analysis and the multiscale encoding is performed based on the decomposed low- and high-frequency components. Finally, the LZ complexity is calculated based on multiscale encoding. The effectiveness of the proposed method is validated by three single-point bearing fault datasets with different failure severity. The proposed method can achieve a classification accuracy of over 97%. The proposed method can be effectively applied to classify the bearing failure severity.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541696
Feihe Xiang;Zhongtan Zhang;Yuxin Han;Deqing Mei;Yancheng Wang
Robotic gripping and manipulation are common tasks in underwater exploration and archeological applications. The robotic contact force sensing in aquatic environments faces several technical challenges, such as high hydraulic pressure will greatly affect the sensor’s sensitivity and sensing accuracy. Herein, we developed a novel aquatic iontronic-based tri-axis force sensor array (ITFSA) for contact force sensing during underwater robotic gripping applications. Utilizing water as a natural ion carrier, the proposed ITFSA has a packaging-free structure design to create a balanced hydraulic environment for underwater force detection. A large-area-ratio electrode pair is designed to improve the force detection sensitivity and linearity. Also, a relative detection method (RDM) was proposed to achieve unified performance in different aquatic environments. Characterization tests showed that the ITFSA exhibits a high normal force detection sensitivity of 0.058 N−1 with a detection range of 0–12 N, shear force detection sensitivities are measured as 0.079 and 0.085 N−1 with a detection range of 0–2 N in two orthogonal directions, respectively. Moreover, our developed ITFSA has generally consistent performances in various aquatic environments. After that, underwater robotic gripping experiments were performed and showed that the ITFSA can accurately detect three-axis forces and reflect characteristics of various underwater objects, showing its promising applications in underwater robotic manipulation.
{"title":"Highly Sensitive Aquatic Iontronic-Based Tri-Axis Force Sensor Array for Underwater Robotic Gripping","authors":"Feihe Xiang;Zhongtan Zhang;Yuxin Han;Deqing Mei;Yancheng Wang","doi":"10.1109/TIM.2025.3541696","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541696","url":null,"abstract":"Robotic gripping and manipulation are common tasks in underwater exploration and archeological applications. The robotic contact force sensing in aquatic environments faces several technical challenges, such as high hydraulic pressure will greatly affect the sensor’s sensitivity and sensing accuracy. Herein, we developed a novel aquatic iontronic-based tri-axis force sensor array (ITFSA) for contact force sensing during underwater robotic gripping applications. Utilizing water as a natural ion carrier, the proposed ITFSA has a packaging-free structure design to create a balanced hydraulic environment for underwater force detection. A large-area-ratio electrode pair is designed to improve the force detection sensitivity and linearity. Also, a relative detection method (RDM) was proposed to achieve unified performance in different aquatic environments. Characterization tests showed that the ITFSA exhibits a high normal force detection sensitivity of 0.058 N−1 with a detection range of 0–12 N, shear force detection sensitivities are measured as 0.079 and 0.085 N−1 with a detection range of 0–2 N in two orthogonal directions, respectively. Moreover, our developed ITFSA has generally consistent performances in various aquatic environments. After that, underwater robotic gripping experiments were performed and showed that the ITFSA can accurately detect three-axis forces and reflect characteristics of various underwater objects, showing its promising applications in underwater robotic manipulation.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541703
Yan Ma;Chuanshuo Gu;Jie Jiang;Xinguo Wei;Dongyu Xie;Gangyi Wang;Jian Li
Traditional radio-based navigation methods rely on communication with ground tracking networks to achieve deep space navigation. However, as deep space exploration continues to advance, the increasing communication distance leads to increased communication delays, which subsequently diminish the real-time capability and accuracy of the navigation. Furthermore, factors such as obstructions by celestial bodies exacerbate the inadequacy of radio navigation for the navigational demands of deep space exploration. The autonomous optical navigation technology, which primarily employs optical navigation sensors as the core navigation equipment, can obtain navigation information of the current carrier independently of ground tracking networks. It has demonstrated significant advantages in terms of autonomy, real-time capability, reliability, accuracy, and cost-effectiveness, making it an indispensable key navigation technology for deep space exploration. This article initially reviews the fundamental navigation principles applicable to different observation targets and the primary methods for determining navigation states during deep space exploration. It then systematically analyzes the characteristics of optical navigation for the four phases of deep space exploration, namely, transfer, capture, orbital, and landing phases. Finally, using typical missions as examples, this article focuses on the study of optical navigation sensors and algorithms for different phases. The review reveals that optical navigation sensors exhibit distinct characteristics across mission phases. Sensors in the transfer phase typically feature the narrowest field of view with the longest focal lengths and the largest apertures, while those used during the landing phase employ the opposite attributes. For the capture and orbital phases, sensors strike a balance between these extremes. Moreover, the spectrum range predominantly falls within the visible light band. In terms of optical navigation algorithms, various approaches are employed during different phases. Line-of-sight (LOS) navigation is most commonly used in the transfer phase. Compared with the transfer phase, the capture and orbital phases incorporate celestial surface feature navigation, and the landing phase primarily adopts terrain relative navigation (TRN). This article would serve as a valuable reference for promoting the development of autonomous optical navigation technology for deep space exploration.
{"title":"Review of Autonomous Optical Navigation for Deep Space Exploration","authors":"Yan Ma;Chuanshuo Gu;Jie Jiang;Xinguo Wei;Dongyu Xie;Gangyi Wang;Jian Li","doi":"10.1109/TIM.2025.3541703","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541703","url":null,"abstract":"Traditional radio-based navigation methods rely on communication with ground tracking networks to achieve deep space navigation. However, as deep space exploration continues to advance, the increasing communication distance leads to increased communication delays, which subsequently diminish the real-time capability and accuracy of the navigation. Furthermore, factors such as obstructions by celestial bodies exacerbate the inadequacy of radio navigation for the navigational demands of deep space exploration. The autonomous optical navigation technology, which primarily employs optical navigation sensors as the core navigation equipment, can obtain navigation information of the current carrier independently of ground tracking networks. It has demonstrated significant advantages in terms of autonomy, real-time capability, reliability, accuracy, and cost-effectiveness, making it an indispensable key navigation technology for deep space exploration. This article initially reviews the fundamental navigation principles applicable to different observation targets and the primary methods for determining navigation states during deep space exploration. It then systematically analyzes the characteristics of optical navigation for the four phases of deep space exploration, namely, transfer, capture, orbital, and landing phases. Finally, using typical missions as examples, this article focuses on the study of optical navigation sensors and algorithms for different phases. The review reveals that optical navigation sensors exhibit distinct characteristics across mission phases. Sensors in the transfer phase typically feature the narrowest field of view with the longest focal lengths and the largest apertures, while those used during the landing phase employ the opposite attributes. For the capture and orbital phases, sensors strike a balance between these extremes. Moreover, the spectrum range predominantly falls within the visible light band. In terms of optical navigation algorithms, various approaches are employed during different phases. Line-of-sight (LOS) navigation is most commonly used in the transfer phase. Compared with the transfer phase, the capture and orbital phases incorporate celestial surface feature navigation, and the landing phase primarily adopts terrain relative navigation (TRN). This article would serve as a valuable reference for promoting the development of autonomous optical navigation technology for deep space exploration.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-30"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1109/TIM.2025.3541651
Fenglin Xian;Fenping Cui;Shixin Pei;Bing Tu;Zhaolou Cao
Accurate measurement of the 3-D complex point spread function (PSF) greatly benefits the aberration analysis of optical systems and beam engineering. In this work, a multi-image phase retrieval algorithm is proposed to retrieve the wavefront of either a scalar or vector beam in the frequency domain. The gradients with respect to the angular spectrum are analytically derived by reformulating the beam propagation into a neural network framework, grounded in rigorous electromagnetic theory. The angular spectrum is optimized to minimize the deviation between measured and numerically reconstructed intensity by the stochastic gradient descent algorithm. Numerical simulation is first performed to examine the applicability of the proposed method in a scalar Gaussian beam and radially polarized Bessel-Gaussian vector beam. Experimental measurements of a beam with astigmatism are then demonstrated. Given the method’s simplicity, ease of implementation, and robustness against noise, we anticipate its broad application in the quantitative characterization of optical systems and structured beams.
{"title":"Three-Dimensional Complex Point Spread Function Reconstruction via Multi-Image Phase Retrieval in the Frequency Domain","authors":"Fenglin Xian;Fenping Cui;Shixin Pei;Bing Tu;Zhaolou Cao","doi":"10.1109/TIM.2025.3541651","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541651","url":null,"abstract":"Accurate measurement of the 3-D complex point spread function (PSF) greatly benefits the aberration analysis of optical systems and beam engineering. In this work, a multi-image phase retrieval algorithm is proposed to retrieve the wavefront of either a scalar or vector beam in the frequency domain. The gradients with respect to the angular spectrum are analytically derived by reformulating the beam propagation into a neural network framework, grounded in rigorous electromagnetic theory. The angular spectrum is optimized to minimize the deviation between measured and numerically reconstructed intensity by the stochastic gradient descent algorithm. Numerical simulation is first performed to examine the applicability of the proposed method in a scalar Gaussian beam and radially polarized Bessel-Gaussian vector beam. Experimental measurements of a beam with astigmatism are then demonstrated. Given the method’s simplicity, ease of implementation, and robustness against noise, we anticipate its broad application in the quantitative characterization of optical systems and structured beams.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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