Global navigation satellite system (GNSS) carrier phase measurement is highly vulnerable to signal attenuation, multipath, and blockage in urban environments, which significantly degrades the availability of precise GNSS positioning. Long coherent integration (LCI) serves as an effective approach to suppress thermal noise and mitigate multipath interferences within phase-locked loops (PLLs); however, its performance is constrained by the dynamic stress resulting from satellite–receiver motions. This study proposes a GNSS/inertial navigation system (INS)/odometer (ODO) deeply coupled (GIO-DC) system with LCI PLLs. An (ODO) distance increment measurement model is integrated with a MEMS IMU to estimate and compensate for the PLLs’ dynamic stress with enhanced accuracy and reliability, thereby enabling extended coherent integration time. In addition, a four-quadrant phase discriminator is adopted to expand the PLL pull-in range and reduce the likelihood of cycle slips. Field tests on a wheeled vehicle in typical urban complex environments were conducted to evaluate the performance of the GIO-DC system from multiple perspectives. The results confirmed the superiority of the proposed approaches. A coherent integration time of 800 ms was achieved, realizing continuous carrier phase measurement and robust centimeter-level positioning. The proposed deeply integrated system, built on the low-cost MEMS IMU and ODO, delivers performance on par with that of a system based on a navigation-grade IMU.
{"title":"MEMS IMU/ODO-Aided GNSS Long Coherent Integration PLL for Urban Vehicle Precise Positioning","authors":"Tisheng Zhang;Huilin Shi;Liqiang Wang;Xin Feng;Yuepei Shi;Xiaoji Niu","doi":"10.1109/TIM.2025.3648069","DOIUrl":"https://doi.org/10.1109/TIM.2025.3648069","url":null,"abstract":"Global navigation satellite system (GNSS) carrier phase measurement is highly vulnerable to signal attenuation, multipath, and blockage in urban environments, which significantly degrades the availability of precise GNSS positioning. Long coherent integration (LCI) serves as an effective approach to suppress thermal noise and mitigate multipath interferences within phase-locked loops (PLLs); however, its performance is constrained by the dynamic stress resulting from satellite–receiver motions. This study proposes a GNSS/inertial navigation system (INS)/odometer (ODO) deeply coupled (GIO-DC) system with LCI PLLs. An (ODO) distance increment measurement model is integrated with a MEMS IMU to estimate and compensate for the PLLs’ dynamic stress with enhanced accuracy and reliability, thereby enabling extended coherent integration time. In addition, a four-quadrant phase discriminator is adopted to expand the PLL pull-in range and reduce the likelihood of cycle slips. Field tests on a wheeled vehicle in typical urban complex environments were conducted to evaluate the performance of the GIO-DC system from multiple perspectives. The results confirmed the superiority of the proposed approaches. A coherent integration time of 800 ms was achieved, realizing continuous carrier phase measurement and robust centimeter-level positioning. The proposed deeply integrated system, built on the low-cost MEMS IMU and ODO, delivers performance on par with that of a system based on a navigation-grade IMU.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-14"},"PeriodicalIF":5.9,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904308","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 : 2025-12-24DOI: 10.1109/TIM.2025.3647989
Lei Cheng;Lihao Guo;Tianya Zhang;Tam Bang;Austin Harris;Mustafa Hajij;Mina Sartipi;Siyang Cao
Accurate multisensor calibration is essential for deploying robust perception systems in applications such as autonomous driving and intelligent transportation. Existing light detection and ranging (LiDAR)–camera calibration methods often rely on manually placed targets, preliminary parameter estimates, or intensive data preprocessing, limiting their scalability and adaptability in real-world settings. In this work, we propose a fully automatic, targetless, and online calibration framework, CalibRefine, which directly processes raw LiDAR point clouds and camera images. Our approach is divided into four stages: 1) a common feature discriminator (CFD) that leverages relative spatial positions, visual appearance embeddings, and semantic class cues to identify and generate reliable LiDAR–camera correspondences; 2) a coarse homography-based calibration that uses the matched feature correspondences to estimate an initial transformation between the LiDAR and camera frames, serving as the foundation for further refinement; 3) an iterative refinement to incrementally improve alignment as additional data frames become available; and 4) an attention-based refinement that addresses nonplanar distortions by leveraging a vision transformer (ViT) and cross-attention mechanisms. Extensive experiments on two urban traffic datasets demonstrate that CalibRefine achieves high-precision calibration with minimal human input, outperforming state-of-the-art targetless methods and matching or surpassing manually tuned baselines. Our results show that robust object-level feature matching, combined with iterative refinement and self-supervised attention-based refinement, enables reliable sensor alignment in complex real-world conditions without ground-truth matrices or elaborate preprocessing. Code is available at https://github.com/radar-lab/Lidar_Camera_Automatic_Calibration
{"title":"CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR–Camera Calibration With Iterative and Attention-Driven Post-Refinement","authors":"Lei Cheng;Lihao Guo;Tianya Zhang;Tam Bang;Austin Harris;Mustafa Hajij;Mina Sartipi;Siyang Cao","doi":"10.1109/TIM.2025.3647989","DOIUrl":"https://doi.org/10.1109/TIM.2025.3647989","url":null,"abstract":"Accurate multisensor calibration is essential for deploying robust perception systems in applications such as autonomous driving and intelligent transportation. Existing light detection and ranging (LiDAR)–camera calibration methods often rely on manually placed targets, preliminary parameter estimates, or intensive data preprocessing, limiting their scalability and adaptability in real-world settings. In this work, we propose a fully automatic, targetless, and online calibration framework, <italic>CalibRefine</i>, which directly processes raw LiDAR point clouds and camera images. Our approach is divided into four stages: 1) a common feature discriminator (CFD) that leverages relative spatial positions, visual appearance embeddings, and semantic class cues to identify and generate reliable LiDAR–camera correspondences; 2) a coarse homography-based calibration that uses the matched feature correspondences to estimate an initial transformation between the LiDAR and camera frames, serving as the foundation for further refinement; 3) an iterative refinement to incrementally improve alignment as additional data frames become available; and 4) an attention-based refinement that addresses nonplanar distortions by leveraging a vision transformer (ViT) and cross-attention mechanisms. Extensive experiments on two urban traffic datasets demonstrate that CalibRefine achieves high-precision calibration with minimal human input, outperforming state-of-the-art targetless methods and matching or surpassing manually tuned baselines. Our results show that robust object-level feature matching, combined with iterative refinement and self-supervised attention-based refinement, enables reliable sensor alignment in complex real-world conditions without ground-truth matrices or elaborate preprocessing. Code is available at <uri>https://github.com/radar-lab/Lidar_Camera_Automatic_Calibration</uri>","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-18"},"PeriodicalIF":5.9,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982170","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 : 2025-12-24DOI: 10.1109/TIM.2025.3648095
Xiaobo Zhang;Jinchan Zhu;Xiaosong Li;Lin Tong;Chun Li;Maoheng Jing;Ning Li;Haijun Wang;Ping Wang
Sparse Bayesian inference (SBI) has emerged as a promising approach for direction-of-arrival (DOA) estimation in acoustic signal processing due to its robust statistical framework. However, conventional SBI methods often lack flexibility due to their dependence on prior models for sparsity constraints, also struggle with high computational complexity caused by covariance matrix inversion, and additionally suffer from precision degradation due to grid mismatch. To address the aforementioned issues, this study proposes an enhanced hierarchical SBI algorithm ($ell _{!p}$ -IFSBI) that integrates $ell _{!p}$ -norm penalty. A nonconvex $ell _{!p}$ -norm regularization model (with $0lt plt 1$ ) is constructed to control the sparsity of the model within a hierarchical Bayesian framework. Additionally, the likelihood function is reformulated through theoretical derivation to eliminate covariance matrix inversion, thereby reducing computational complexity. Furthermore, the coati optimization algorithm (COA) is introduced to perform adaptive searching for the actual source position within the signal subspace, effectively compensating for model errors caused by grid mismatch. Simulation and real-time acoustic source localization experiments show that, at a signal-to-noise ratio (SNR) of 0 dB, the proposed $ell _{!p}$ -IFSBI-COA algorithm achieves a root-mean-square error (RMSE) of less than 0.3° in the DOA estimation. To facilitate further research and reproduction, the source code is available at https://github.com/Xiaob0-Zhang/lp-IFSBI
稀疏贝叶斯推理（SBI）由于其鲁棒的统计框架而成为声信号处理中到达方向（DOA）估计的一种有前途的方法。然而，传统的SBI方法由于依赖于先前模型的稀疏性约束而缺乏灵活性，并且由于协方差矩阵反演而导致的计算复杂度较高，并且由于网格不匹配而导致精度降低。为了解决上述问题，本研究提出了一种增强的分层SBI算法($ well _{！p}$ -IFSBI)集成$ well _{！$ -norm惩罚。非凸$ well _{！构造p}$ -范数正则化模型（$0lt plt 1$）以在层次贝叶斯框架内控制模型的稀疏性。另外，通过理论推导对似然函数进行了重新表述，消除了协方差矩阵的反演，从而降低了计算复杂度。引入coati优化算法（COA）在信号子空间内自适应搜索实际源位置，有效补偿网格失配引起的模型误差。仿真和实时声源定位实验表明，在信噪比（SNR）为0 dB时，所提出的$ well _{！p}$ -IFSBI-COA算法的DOA估计均方根误差（RMSE）小于0.3°。为了便于进一步研究和复制，源代码可在https://github.com/Xiaob0-Zhang/lp-IFSBI上获得
{"title":"A Low-Complexity Sparse Bayesian Acoustic Source Localization Method Based on ℓₚ-Norm Constraint","authors":"Xiaobo Zhang;Jinchan Zhu;Xiaosong Li;Lin Tong;Chun Li;Maoheng Jing;Ning Li;Haijun Wang;Ping Wang","doi":"10.1109/TIM.2025.3648095","DOIUrl":"https://doi.org/10.1109/TIM.2025.3648095","url":null,"abstract":"Sparse Bayesian inference (SBI) has emerged as a promising approach for direction-of-arrival (DOA) estimation in acoustic signal processing due to its robust statistical framework. However, conventional SBI methods often lack flexibility due to their dependence on prior models for sparsity constraints, also struggle with high computational complexity caused by covariance matrix inversion, and additionally suffer from precision degradation due to grid mismatch. To address the aforementioned issues, this study proposes an enhanced hierarchical SBI algorithm (<inline-formula> <tex-math>$ell _{!p}$ </tex-math></inline-formula>-IFSBI) that integrates <inline-formula> <tex-math>$ell _{!p}$ </tex-math></inline-formula>-norm penalty. A nonconvex <inline-formula> <tex-math>$ell _{!p}$ </tex-math></inline-formula>-norm regularization model (with <inline-formula> <tex-math>$0lt plt 1$ </tex-math></inline-formula>) is constructed to control the sparsity of the model within a hierarchical Bayesian framework. Additionally, the likelihood function is reformulated through theoretical derivation to eliminate covariance matrix inversion, thereby reducing computational complexity. Furthermore, the coati optimization algorithm (COA) is introduced to perform adaptive searching for the actual source position within the signal subspace, effectively compensating for model errors caused by grid mismatch. Simulation and real-time acoustic source localization experiments show that, at a signal-to-noise ratio (SNR) of 0 dB, the proposed <inline-formula> <tex-math>$ell _{!p}$ </tex-math></inline-formula>-IFSBI-COA algorithm achieves a root-mean-square error (RMSE) of less than 0.3° in the DOA estimation. To facilitate further research and reproduction, the source code is available at <uri>https://github.com/Xiaob0-Zhang/lp-IFSBI</uri>","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-16"},"PeriodicalIF":5.9,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982178","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 : 2025-12-24DOI: 10.1109/TIM.2025.3648100
Zhenhua Fan;Bing Yan;Che Xu;Shixiang Lu;Kai Zhong
Advanced intelligent fault diagnosis (IFD) methods based on domain generalization (DG) leverage multisource sensor data to overcome the limitations the limitations of domain adaptation (DA)-based models regarding target data demand in the training stage, which resolves domain shift problems under unseen conditions in industrial instrumentation. However, previous studies primarily focused on the closed-set diagnosis with same label space shared by the training and testing data, which struggle to address the problem of new fault identification under intricate industrial dynamics. To overcome this obstacle, a trustworthy evidential open-set DG network (EOSDGN) is proposed for open-set fault diagnosis under unseen conditions. In the EOSDGN method, an evidential deep classifier is constructed to quantify the uncertainty of predicted results, an evidential domain discriminator is employed to integrate the data from diverse source domains, and an evidential uncertainty calibration is established to reconcile the misleading evidence and class-based evidence assignments. The EOSDGN model effectively addresses both domain and label shift challenges by integrating domain-invariant features extraction and uncertainty quantification of the predicted probabilities, which enables efficient classification of known faults while simultaneously facilitating the identification of unknown faults. The effectiveness of the EOSDGN model has been substantiated using public and practical datasets. Experimental findings demonstrate that the EOSDGN model surpasses the performance of the state-of-the-art models.
{"title":"Trustworthy Open Set Domain Generalization Network for Unknown Fault Diagnosis Under Unseen Conditions","authors":"Zhenhua Fan;Bing Yan;Che Xu;Shixiang Lu;Kai Zhong","doi":"10.1109/TIM.2025.3648100","DOIUrl":"https://doi.org/10.1109/TIM.2025.3648100","url":null,"abstract":"Advanced intelligent fault diagnosis (IFD) methods based on domain generalization (DG) leverage multisource sensor data to overcome the limitations the limitations of domain adaptation (DA)-based models regarding target data demand in the training stage, which resolves domain shift problems under unseen conditions in industrial instrumentation. However, previous studies primarily focused on the closed-set diagnosis with same label space shared by the training and testing data, which struggle to address the problem of new fault identification under intricate industrial dynamics. To overcome this obstacle, a trustworthy evidential open-set DG network (EOSDGN) is proposed for open-set fault diagnosis under unseen conditions. In the EOSDGN method, an evidential deep classifier is constructed to quantify the uncertainty of predicted results, an evidential domain discriminator is employed to integrate the data from diverse source domains, and an evidential uncertainty calibration is established to reconcile the misleading evidence and class-based evidence assignments. The EOSDGN model effectively addresses both domain and label shift challenges by integrating domain-invariant features extraction and uncertainty quantification of the predicted probabilities, which enables efficient classification of known faults while simultaneously facilitating the identification of unknown faults. The effectiveness of the EOSDGN model has been substantiated using public and practical datasets. Experimental findings demonstrate that the EOSDGN model surpasses the performance of the state-of-the-art models.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904273","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 : 2025-12-24DOI: 10.1109/TIM.2025.3648104
Taorui Chen;Yuki Gao;Yi Wang;Hai-Han Sun
Potatoes are an economically important crop, and their quality is closely related to the starch content, which is typically inferred from specific gravity (SG). Although microwave sensing technologies have been increasingly developed for underground potato detection and quality assessment in recent years, no accurate model has yet been established to link the dielectric properties of potatoes with their key agronomic traits. To address this gap, we developed a model for estimating potato tubers’ SG based on their dielectric constant. To construct and validate the model, we conducted SG measurements and dielectric spectroscopy measurements in the frequency range of 0.3–3.0 GHz on 250 potatoes of five different types (red, russet, yellow, purple, and chipping potatoes, with 50 samples per type). Out of the 250 datasets, 200 datasets were used for model development, and 50 datasets were used for model validation. A linear regression model was used to summarize the relationship between SG and dielectric constant, where the regression coefficients are expressed as fourth-order polynomial functions of frequency. Experimental results on 50 validation datasets show that the model achieves high estimation accuracy with mean absolute errors (MAEs) of less than $4.80 times 10^{-3}$ and mean absolute percentage errors (MAPEs) of less than 0.45%. The model was further validated on 50 yellow potatoes at different growing stages, achieving consistent estimation accuracy with MAE of $3.71 times 10^{-3}$ and MAPE of 0.35%. The study of the dielectric properties of potatoes, along with the derived SG estimation model, provides a foundation for the future development of microwave sensing technologies for agronomic trait assessment in potato production and processing industries.
{"title":"Estimation of Specific Gravity of Potato Tubers Using Dielectric Properties","authors":"Taorui Chen;Yuki Gao;Yi Wang;Hai-Han Sun","doi":"10.1109/TIM.2025.3648104","DOIUrl":"https://doi.org/10.1109/TIM.2025.3648104","url":null,"abstract":"Potatoes are an economically important crop, and their quality is closely related to the starch content, which is typically inferred from specific gravity (SG). Although microwave sensing technologies have been increasingly developed for underground potato detection and quality assessment in recent years, no accurate model has yet been established to link the dielectric properties of potatoes with their key agronomic traits. To address this gap, we developed a model for estimating potato tubers’ SG based on their dielectric constant. To construct and validate the model, we conducted SG measurements and dielectric spectroscopy measurements in the frequency range of 0.3–3.0 GHz on 250 potatoes of five different types (red, russet, yellow, purple, and chipping potatoes, with 50 samples per type). Out of the 250 datasets, 200 datasets were used for model development, and 50 datasets were used for model validation. A linear regression model was used to summarize the relationship between SG and dielectric constant, where the regression coefficients are expressed as fourth-order polynomial functions of frequency. Experimental results on 50 validation datasets show that the model achieves high estimation accuracy with mean absolute errors (MAEs) of less than <inline-formula> <tex-math>$4.80 times 10^{-3}$ </tex-math></inline-formula> and mean absolute percentage errors (MAPEs) of less than 0.45%. The model was further validated on 50 yellow potatoes at different growing stages, achieving consistent estimation accuracy with MAE of <inline-formula> <tex-math>$3.71 times 10^{-3}$ </tex-math></inline-formula> and MAPE of 0.35%. The study of the dielectric properties of potatoes, along with the derived SG estimation model, provides a foundation for the future development of microwave sensing technologies for agronomic trait assessment in potato production and processing industries.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904300","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}
Endpoint localization of faint streak-like objects is an important component of space situational awareness. In this study, a correlation-based endpoint localization is proposed. It is composed of coarse localization and fine localization. The mathematical model of correlation is derived and verified, and it is used to select the optimal reference image of endpoints in different localization stages and conditions. Meanwhile, a correlation-coefficient-weighted centroid mapping (CCWCM) is proposed to achieve the fine location. Experiments demonstrate that the proposed method achieves superior localization accuracy for faint streak-like objects in single-frame star images while maintaining practical computational efficiency, and maintains robust performance for a peak signal-to-noise ratio (SNR) $geq 2$ . Furthermore, validation on real star images confirms the method’s validity and expected performance in practical operation.
{"title":"Endpoint Localization of Faint Streak-Like Objects in Single-Frame Star Images","authors":"Yong Han;Desheng Wen;Jie Li;Zhangchi Qiao;Xin Wei;Tuochi Jiang","doi":"10.1109/TIM.2025.3647994","DOIUrl":"https://doi.org/10.1109/TIM.2025.3647994","url":null,"abstract":"Endpoint localization of faint streak-like objects is an important component of space situational awareness. In this study, a correlation-based endpoint localization is proposed. It is composed of coarse localization and fine localization. The mathematical model of correlation is derived and verified, and it is used to select the optimal reference image of endpoints in different localization stages and conditions. Meanwhile, a correlation-coefficient-weighted centroid mapping (CCWCM) is proposed to achieve the fine location. Experiments demonstrate that the proposed method achieves superior localization accuracy for faint streak-like objects in single-frame star images while maintaining practical computational efficiency, and maintains robust performance for a peak signal-to-noise ratio (SNR) <inline-formula> <tex-math>$geq 2$ </tex-math></inline-formula>. Furthermore, validation on real star images confirms the method’s validity and expected performance in practical operation.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-18"},"PeriodicalIF":5.9,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904318","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 : 2025-12-24DOI: 10.1109/TIM.2025.3647995
Long Cheng;Ke Liu;Jie Pan;Zhentao Fu
Indoor localization remains challenging due to multipath propagation, dynamic obstacles, and environmental noise. Traditional methods based on geometric or probabilistic models often fail under such complex conditions. The core challenge lies in effectively modeling spatial, temporal, and multichannel characteristics of noisy wireless signals. Channel state information (CSI) has the potential to address these issues by providing more detailed spatial and frequency domain features, making it a promising candidate for robust indoor localization. To address these limitations, this article proposes a unified indoor localization framework—graph attention convolution and bidirectional long short- term memory (GACB) Loc—which integrates graph convolution-based multichannel attention, convolutional neural networks (CNNs), and bidirectional long short-term memory (BLSTM) to jointly model spatial, temporal, and channelwise dependencies in CSI data. Aiming at the multichannel characteristics of CSI data, a Transformer-inspired graph convolution attention mechanism framework suitable for CSI data is proposed. First, the CSI phase data are preprocessed, and CNN is employed to extract advanced features and capture complex spatial and frequency domain patterns from the CSI phase data. Then, by utilizing the graph structure of CSI data and adaptively focusing on the most important channels, the model’s ability to prioritize relevant information is improved. Finally, BLSTM is proposed to capture temporal dependencies in the data. We conducted experiments on the proposed method using both publicly available datasets and real-world deployment environments. The results on two public datasets showed mean localization errors of 0.4945 and 0.6546 m, while real-world tests achieved average errors of 0.1691 and 0.8259 m, demonstrating our approach’s effectiveness and robustness. Compared to ten other representative methods—including incremental learning for intelligence localization (ILCL), broad learning system (BLS), multi-layer perceptron (MLP), neural network (NN), Horus, multi-output regression (MOR), RF-based user location and tracking system (RADAR), speed-aware WiFi-based passive indoor localization for mobile ship environment (SWIM), Bayes, and decision tree estimator (DTE)—our approach achieved average improvements of approximately 74.95% and 86.1%, respectively.
由于多径传播、动态障碍物和环境噪声，室内定位仍然具有挑战性。基于几何或概率模型的传统方法在这种复杂条件下往往失效。其核心挑战在于如何有效地模拟有噪声无线信号的空间、时间和多通道特性。信道状态信息（CSI）有可能通过提供更详细的空间和频域特征来解决这些问题，使其成为强大的室内定位的有希望的候选者。为了解决这些限制，本文提出了一个统一的室内定位框架-图注意卷积和双向长短期记忆（GACB） loc -它集成了基于图卷积的多通道注意、卷积神经网络（cnn）和双向长短期记忆（BLSTM），共同建模CSI数据中的空间、时间和通道依赖关系。针对CSI数据的多通道特性，提出了一种适用于CSI数据的Transformer-inspired图卷积注意机制框架。首先，对CSI相位数据进行预处理，利用CNN提取CSI相位数据的高级特征，捕获复杂的空间和频域模式；然后，利用CSI数据的图形结构，自适应地聚焦最重要的渠道，提高了模型对相关信息的优先级排序能力。最后，提出了BLSTM来捕获数据中的时间依赖性。我们使用公开可用的数据集和实际部署环境对提出的方法进行了实验。在两个公开数据集上的平均定位误差为0.4945和0.6546 m，而实际测试的平均定位误差为0.1691和0.8259 m，证明了我们的方法的有效性和鲁棒性。与其他十种代表性方法（包括智能定位的增量学习（ILCL）、广泛学习系统（BLS）、多层感知器（MLP）、神经网络（NN）、Horus、多输出回归（MOR）、基于射频的用户定位和跟踪系统（RADAR）、基于速度感知wifi的移动船舶环境被动室内定位（SWIM）、贝叶斯和决策树估计器（DTE））相比，我们的方法实现了大约74.95%和86.1%的平均改进。分别。
{"title":"GACB-Loc: A CSI Indoor Localization Method Based on Graph Convolutional Multichannel Attention Using CNN and BLSTM","authors":"Long Cheng;Ke Liu;Jie Pan;Zhentao Fu","doi":"10.1109/TIM.2025.3647995","DOIUrl":"https://doi.org/10.1109/TIM.2025.3647995","url":null,"abstract":"Indoor localization remains challenging due to multipath propagation, dynamic obstacles, and environmental noise. Traditional methods based on geometric or probabilistic models often fail under such complex conditions. The core challenge lies in effectively modeling spatial, temporal, and multichannel characteristics of noisy wireless signals. Channel state information (CSI) has the potential to address these issues by providing more detailed spatial and frequency domain features, making it a promising candidate for robust indoor localization. To address these limitations, this article proposes a unified indoor localization framework—graph attention convolution and bidirectional long short- term memory (GACB) Loc—which integrates graph convolution-based multichannel attention, convolutional neural networks (CNNs), and bidirectional long short-term memory (BLSTM) to jointly model spatial, temporal, and channelwise dependencies in CSI data. Aiming at the multichannel characteristics of CSI data, a Transformer-inspired graph convolution attention mechanism framework suitable for CSI data is proposed. First, the CSI phase data are preprocessed, and CNN is employed to extract advanced features and capture complex spatial and frequency domain patterns from the CSI phase data. Then, by utilizing the graph structure of CSI data and adaptively focusing on the most important channels, the model’s ability to prioritize relevant information is improved. Finally, BLSTM is proposed to capture temporal dependencies in the data. We conducted experiments on the proposed method using both publicly available datasets and real-world deployment environments. The results on two public datasets showed mean localization errors of 0.4945 and 0.6546 m, while real-world tests achieved average errors of 0.1691 and 0.8259 m, demonstrating our approach’s effectiveness and robustness. Compared to ten other representative methods—including incremental learning for intelligence localization (ILCL), broad learning system (BLS), multi-layer perceptron (MLP), neural network (NN), Horus, multi-output regression (MOR), RF-based user location and tracking system (RADAR), speed-aware WiFi-based passive indoor localization for mobile ship environment (SWIM), Bayes, and decision tree estimator (DTE)—our approach achieved average improvements of approximately 74.95% and 86.1%, respectively.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-16"},"PeriodicalIF":5.9,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904342","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}
The drift performance of a relative gravimeter is a critical factor in its ability to detect long-term gravity variation signals, which typically change at an extremely slow rate. Recently, microelectromechanical system (MEMS) gravimeters have demonstrated remarkable performances and advantages such as mass production, compact size, and cost-effectiveness. However, their long-term drift behavior remains unexplored. In this study, a highly sensitive MEMS gravimeter with a self-noise of $0.8~mu $ Gal/$surd $ Hz and an Allan variance of $1.1~mu $ Gal@50 s is employed to investigate the long-term drift characteristics and correction strategies based on data obtained over 900 days. The start-up drift is analyzed first, revealing that circuit drift dominates during the initial four days. A detailed analysis of 300-day data of two MEMS gravimeters reveals that the long-term drift characteristics follow a natural logarithmic model, challenging the widely adopted linear model. The fit natural logarithmic drift models are then applied to compensate for the drifts of the two MEMS gravimeters in the following 262 days of observations, reducing the drift rate from $292.6~pm ~34.6~mu $ Gal/day and $252.2~pm ~28.2~mu $ Gal/day to $3.1~pm ~37.7~mu $ Gal/day and $7.1~pm ~25.5~mu $ Gal/day, respectively. Furthermore, the drift performance of the MEMS gravimeter after relocation is also found to agree with the same natural logarithmic model. This breakthrough not only minimizes the impact of the drift when observing time-varying gravitational fields but also extends the calibration interval for mobile gravity measurements, showcasing a significant step toward transitioning MEMS gravimeters from laboratory research to real-world engineering applications.
{"title":"Logarithmic Long-Term Drift Characteristics of MEMS Gravimeters: Insights From Over 900-Day Data","authors":"Lujia Yang;Wenjie Wu;Shasha Liu;Xiaochao Xu;Fangzheng Li;Le Gao;Bingyang Cai;Runhan Xie;Fangjing Hu;Liangcheng Tu","doi":"10.1109/TIM.2025.3644569","DOIUrl":"https://doi.org/10.1109/TIM.2025.3644569","url":null,"abstract":"The drift performance of a relative gravimeter is a critical factor in its ability to detect long-term gravity variation signals, which typically change at an extremely slow rate. Recently, microelectromechanical system (MEMS) gravimeters have demonstrated remarkable performances and advantages such as mass production, compact size, and cost-effectiveness. However, their long-term drift behavior remains unexplored. In this study, a highly sensitive MEMS gravimeter with a self-noise of <inline-formula> <tex-math>$0.8~mu $ </tex-math></inline-formula>Gal/<inline-formula> <tex-math>$surd $ </tex-math></inline-formula>Hz and an Allan variance of <inline-formula> <tex-math>$1.1~mu $ </tex-math></inline-formula>Gal@50 s is employed to investigate the long-term drift characteristics and correction strategies based on data obtained over 900 days. The start-up drift is analyzed first, revealing that circuit drift dominates during the initial four days. A detailed analysis of 300-day data of two MEMS gravimeters reveals that the long-term drift characteristics follow a natural logarithmic model, challenging the widely adopted linear model. The fit natural logarithmic drift models are then applied to compensate for the drifts of the two MEMS gravimeters in the following 262 days of observations, reducing the drift rate from <inline-formula> <tex-math>$292.6~pm ~34.6~mu $ </tex-math></inline-formula>Gal/day and <inline-formula> <tex-math>$252.2~pm ~28.2~mu $ </tex-math></inline-formula>Gal/day to <inline-formula> <tex-math>$3.1~pm ~37.7~mu $ </tex-math></inline-formula>Gal/day and <inline-formula> <tex-math>$7.1~pm ~25.5~mu $ </tex-math></inline-formula>Gal/day, respectively. Furthermore, the drift performance of the MEMS gravimeter after relocation is also found to agree with the same natural logarithmic model. This breakthrough not only minimizes the impact of the drift when observing time-varying gravitational fields but also extends the calibration interval for mobile gravity measurements, showcasing a significant step toward transitioning MEMS gravimeters from laboratory research to real-world engineering applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982168","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 : 2025-12-18DOI: 10.1109/TIM.2025.3645945
Xiaodong Hong;Wei Liang;Dichang Huang;Zhenting Xu;Qiukun Zhang;Jiewen Lin;Shuncong Zhong;Tao Li
As industrialization advances and the global push for carbon neutrality intensifies, enhancing the efficiency of mechanical equipment has become essential. Accurate measurement of rotational torque plays a crucial role in monitoring efficiency and ensuring optimal performance. This article presents a novel, noncontact measurement technique based on optical coherent displacement, integrating dual-optical-detector probes for simultaneous rotational torque and speed measurement. A sensing model that links optical coherent signals to changes in rotational torque and rotational speed is established. The experiment demonstrates high accuracy of the system, with rotational speed error ranging from 0.25% to 0.67%, and rotational torque indication error between 0.03% and 2.25%. Furthermore, the experiments proved that the repeatability error is less than 1%, the hysteresis error is less than 1.6%, and the linearity error is in the range of 0.24% $sim ~0.57$ % for rotational torque measurement. The research further evaluates the influence of rotational speed on rotational torque measurement accuracy, revealing minimal impact at higher speeds. The findings suggest that the proposed method offers significant potential for precision measurement in rotating machinery, enabling the simultaneous measurement of both torque and rotational speed. This capability has important implications for improving system efficiency and supporting sustainable industrial practices.
{"title":"A Novel Rotational Torque Measuring Method Based on Double-Micro-Indentation Shaft Sensed by Optical Coherent System","authors":"Xiaodong Hong;Wei Liang;Dichang Huang;Zhenting Xu;Qiukun Zhang;Jiewen Lin;Shuncong Zhong;Tao Li","doi":"10.1109/TIM.2025.3645945","DOIUrl":"https://doi.org/10.1109/TIM.2025.3645945","url":null,"abstract":"As industrialization advances and the global push for carbon neutrality intensifies, enhancing the efficiency of mechanical equipment has become essential. Accurate measurement of rotational torque plays a crucial role in monitoring efficiency and ensuring optimal performance. This article presents a novel, noncontact measurement technique based on optical coherent displacement, integrating dual-optical-detector probes for simultaneous rotational torque and speed measurement. A sensing model that links optical coherent signals to changes in rotational torque and rotational speed is established. The experiment demonstrates high accuracy of the system, with rotational speed error ranging from 0.25% to 0.67%, and rotational torque indication error between 0.03% and 2.25%. Furthermore, the experiments proved that the repeatability error is less than 1%, the hysteresis error is less than 1.6%, and the linearity error is in the range of 0.24% <inline-formula> <tex-math>$sim ~0.57$ </tex-math></inline-formula>% for rotational torque measurement. The research further evaluates the influence of rotational speed on rotational torque measurement accuracy, revealing minimal impact at higher speeds. The findings suggest that the proposed method offers significant potential for precision measurement in rotating machinery, enabling the simultaneous measurement of both torque and rotational speed. This capability has important implications for improving system efficiency and supporting sustainable industrial practices.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982344","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 : 2025-12-18DOI: 10.1109/TIM.2025.3645927
Saša Radosavljevic;Alain Rivero;Abdelhafid El Ouardi;Sergio Rodríguez Flórez
The expansion and increasing complexity of railway infrastructure, combined with a growing demand for higher safety and maintenance standards, has driven important innovation in rail defect detection. This review examines recent methods for railway track defect identification, with a particular focus on their deployment on embedded computing architectures. Detection methods are categorized across multiple sensing modalities—vision, acoustics, vibration, and electromagnetic—while highlighting recent advances in deep learning (DL). This study addresses the critical gap between the performance of algorithms and their potential to be deployed on hardware architectures to design reliable, real-time systems. This review evaluates these approaches in terms of suitability for real-time onboard deployment, identifies their limitations, and proposes a new multisensory, embedded system that will balance performance, energy efficiency, and scalability. A study of detection methods and their in-depth evaluation aims to bridge the gap between complex and high-accuracy detection algorithms and their integration into lighter railway monitoring systems.
{"title":"Railway Track Defect Detection: From a Comprehensive Review of Methods to New Embedded System Modeling Perspectives","authors":"Saša Radosavljevic;Alain Rivero;Abdelhafid El Ouardi;Sergio Rodríguez Flórez","doi":"10.1109/TIM.2025.3645927","DOIUrl":"https://doi.org/10.1109/TIM.2025.3645927","url":null,"abstract":"The expansion and increasing complexity of railway infrastructure, combined with a growing demand for higher safety and maintenance standards, has driven important innovation in rail defect detection. This review examines recent methods for railway track defect identification, with a particular focus on their deployment on embedded computing architectures. Detection methods are categorized across multiple sensing modalities—vision, acoustics, vibration, and electromagnetic—while highlighting recent advances in deep learning (DL). This study addresses the critical gap between the performance of algorithms and their potential to be deployed on hardware architectures to design reliable, real-time systems. This review evaluates these approaches in terms of suitability for real-time onboard deployment, identifies their limitations, and proposes a new multisensory, embedded system that will balance performance, energy efficiency, and scalability. A study of detection methods and their in-depth evaluation aims to bridge the gap between complex and high-accuracy detection algorithms and their integration into lighter railway monitoring systems.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-25"},"PeriodicalIF":5.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904311","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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