ieeexplore最新文献

{"title":"A Variational Bayesian-Based Maximum Correntropy Adaptive Kalman Filter for SINS/USBL Integrated Navigation System","authors":"Boyang Wang;Zhenjie Wang;Yuanxi Yang","doi":"10.1109/JSEN.2025.3562864","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3562864","url":null,"abstract":"The strap-down inertial navigation system (SINS) and ultrashort baseline (USBL) (SINS/USBL) integrated system are the highly promising tool for navigating the autonomous underwater vehicles (AUVs). The measurement in the deep sea often contains unknown, time-varying noise and outliers. The traditional Kalman filter (KF) may face challenges in achieving high-precision underwater navigation due to its limited robustness and adaptivity. Although the robust KF has been developed and can effectively handle non-Gaussian noises in most cases, it may still suffer a significant loss in accuracy under nonstationary noise conditions. This study presents an adaptive robust KF that integrates the maximum correntropy criterion (MCC) with the variational Bayesian (VB) method to effectively mitigate the effects of complex noise. The proposed method achieves adaptivity by employing the VB method to estimate measurement noise covariance while enhancing robustness by mitigating outliers using the variable kernel bandwidth MCC strategy. According to simulation and offshore experiments, the proposed method provides superior estimation accuracy compared to related adaptive and robust algorithms.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20147-20157"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstructing the 3-D Methane Plume Image Using Portable Shipborne Sonar","authors":"Lin Guo;Octavian Postolache;Lin Ma;Yang Shi","doi":"10.1109/JSEN.2025.3562924","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3562924","url":null,"abstract":"The methane plume is a sign of subsea natural gas leakage, which generally overflows from the seabed as a bubble plume. The methane plume is a complex 3-D structure that depends on the bubble rising path, which cannot meet the exploration and research needs only through a 2-D image. Based on the developed portable methane plume sonar and backscatter characteristics of the methane bubble overflowing from the methane plume, this article focuses on the 3-D image reconstruction method for the methane plume on the seafloor according to the image projection principle and 2-D measuring image of discrete survey lines in the sea trial. First, the bisliding window correction (BSWC) method is designed to correct the distorted methane plume contour under the beam open angle of the sonar transducer. Second, the geometric-constrained convergence method (GCCM) based on the steepest descent algorithm is proposed to estimate the location of the methane plume and converge the contour under each depth profile. Finally, the 3-D plume image can be formed. The reconstructed 3-D image of the plume has been obtained through the sea trail and data processing. The overlap area between the detection contour and the inversion contour was compared using the inversion algorithm, and the average overlap probability was 79.21%, verifying the effectiveness of the proposed reconstruction method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20445-20454"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-Dimensional Off-Grid Beamforming Acoustic Source Identification for Planar Microphone Arrays via Unfolding Delay-and-Sum","authors":"Shuaiyong Li;Youwei Yu;Pei Shen","doi":"10.1109/JSEN.2025.3562960","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3562960","url":null,"abstract":"The classical sparsity-based source identification method encounters the basis mismatch problem due to discretizing the focus region and assuming that acoustic sources are on-grid. This limitation results in degraded performance when sources do not align precisely with grid points. The off-grid method based on the first-order Taylor expansion offers a solution to this problem. However, when using coarse search grids, the Taylor expansion of the transfer function vectors at the grid points does not approximate the actual transfer function vectors well, leading to the performance deterioration of the off-grid model. To address basis mismatch, this article introduces a 2-D off-grid acoustic source identification method based on unfolding delay-and-sum (OG-UDAS). This approach develops a novel off-grid delay-and-sum (DAS) model by leveraging the orthogonality between the transfer function vector obtained from the first-order Taylor expansion and the noise matrix (NM). OG-UDAS is a quadratic programming problem that is unfolded into a polynomial optimization function with respect to the acoustic source off-grid deviation. Subsequently, by minimizing the objective function, the closed-form solutions for source off-grid deviation are obtained using the difference method (DM). The strength estimate of the source is obtained using the least-squares method (LSM). Using the estimated off-grid deviation and strength estimation can be alternately learned iteratively to the actual source. Both simulations and experiments demonstrate that OG-UDAS effectively alleviates the basis mismatch problem, even with a limited number of microphones. Compared to existing off-grid methods, this approach achieves accurate acoustic source identification even with coarse search grids.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20169-20184"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Doppler Shift-Compensated FMCW Algorithm for Centimeter-Level Acoustic Single-Anchor Positioning of Moving Targets in Narrow Spaces","authors":"Weimeng Cui;Yuzhang Xi;Guangyao Liu;Naizheng Jia;Peixuan Hu;Shuyu Li;Zhi Wang","doi":"10.1109/JSEN.2025.3563366","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3563366","url":null,"abstract":"This article proposes a novel Doppler shift-compensated frequency-modulated continuous wave (DSC-FMCW) algorithm for achieving high-precision time-of-arrival (TOA) estimation of moving targets based on acoustics. In this algorithm, the stationary speaker transmits a mixed signal with upchirp and downchirp components, received by the moving microphone. Measurements are then compensated using the DSC-FMCW method. We then apply this algorithm to achieve 2-D positioning of moving targets in narrow spaces, using only one external speaker. In this method, a mirror-image speaker concept is introduced, where the primary reflection from the opposite wall is treated as coming from the mirror-image speaker, while reflections from the floor and ceiling are excluded as multipath signals. Additionally, considering the inability to synchronize the microphone and speaker, we leverage the characteristics of narrow spaces and the Doppler effect to achieve precise clock synchronization by estimating the absolute distance from a reference position to the speaker. Experiments show that the DSC-FMCW algorithm outperforms existing methods, achieving high accuracy in both open and multipath-affected scenarios at a target speed of 1.5 m/s, with mean errors of 0.0219 and 0.0418 m, respectively. Additionally, experiments in a <inline-formula> <tex-math>$1.735times 6.5$ </tex-math></inline-formula> m corridor demonstrate an average error of 0.0446 m and a 95th percentile error of 0.0872 m. The proposed algorithm shows strong accuracy and robustness.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20195-20208"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Lactate Monitoring Method Based on Spatiotemporal Inward Bioimpedance Spectroscopy Tomography","authors":"Junwen Peng;Tianyu Jiang;Zihan Zhao;Bo Sun;Yingqi Zhang;Kai Liu;Jiafeng Yao","doi":"10.1109/JSEN.2025.3562965","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3562965","url":null,"abstract":"This study introduces a novel dynamic lactate monitoring approach using spatiotemporal-inward bioimpedance spectroscopy tomography (SI-BIST) to assess lactate distribution changes in human tissue. Numerical simulations were conducted to evaluate the influence of different electrode configurations and measurement patterns on the accuracy of lactate monitoring. Three electrode configurations were proposed: <inline-formula> <tex-math>${R}_{text {16-16}}$ </tex-math></inline-formula> (two rings with 16 electrodes each), <inline-formula> <tex-math>${R}_{text {8-16-8}}$ </tex-math></inline-formula> (three rings with 8, 16, and 8 electrodes, respectively), and <inline-formula> <tex-math>${R}_{text {8-8-8-8}}$ </tex-math></inline-formula> (four rings with eight electrodes each). Additionally, four measurement patterns, namely, adjacent, opposite, “zigzag,” and “snake-shaped” methods, were analyzed to determine the optimal measurement pattern. The imaging performance of the SI-BIST method was further investigated for lactate targets of varying sizes under different interlayer spacings between electrode rings. The numerical simulation results showed that the <inline-formula> <tex-math>${R}_{text {16-16}}$ </tex-math></inline-formula> electrode configuration combined with the “snake-shaped” measurement pattern provided the superior imaging performance achieving an average image correlation coefficient (ICC) of 0.84. Under this optimal setup, the lactate volume (<inline-formula> <tex-math>${V}_{text {stage}}$ </tex-math></inline-formula>) and barycenter offset (<inline-formula> <tex-math>${D}_{text {off}}$ </tex-math></inline-formula>) demonstrated accurate and reliable measurements. Bioimpedance spectroscopy (BIS) simulation results indicated that relaxation impedance decreased with increasing lactate concentration, validating the capability of BIS method to differentiate lactate concentrations. In the experimental studies, the SI-BIST platform successfully monitored lactate diffusion and classified concentrations. Reconstructed images showed that lactate volume (<inline-formula> <tex-math>${V}_{text {stage}}$ </tex-math></inline-formula>) in reconstructed images increased from 1093 in Stage 1 to 4843 in Stage 5, while the spatial-mean conductivity (<inline-formula> <tex-math>$sigma _{text {stage}}$ </tex-math></inline-formula>) rose from 0.022 to 0.061 S/m as lactate diffusion time (T) progressed. Measurements using agar models with varying lactate concentrations achieved a classification accuracy of 74% with a weighted k-nearest neighbor (KNN) algorithm model. This study establishes SI-BIST as an effective technique for monitoring spatiotemporal variations in lactate concentration, offering robust capabilities for imaging lactate diffusion and accurately classifying concentrations.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"18971-18981"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neighborhood Structure Consistency for Point Cloud Registration With Severe Outliers","authors":"Junfeng Ding;Pei An;Yunbiao Xu;Jie Ma","doi":"10.1109/JSEN.2025.3563380","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3563380","url":null,"abstract":"Point cloud registration (PCR) is a fundamental task in 3-D sensor technology and computer vision, aiming to estimate the rigid pose between two point clouds and align them. Correspondence-based methods generate and verify hypothetical poses by sampling consistent correspondences. However, the presence of severe outliers in feature correspondences poses substantial challenges during the sampling process. It may result in misjudgment of inlier consistency relationships, leading to incorrect sampling and causing PCR failure. In this article, we introduce the neighborhood structure consistency (NSC) metric to guide consensus excavation. It incorporates tighter global consistency constraints rather than local consistency, yielding a more distinct inlier cluster and significantly mitigating the impact of outliers. After that, we propose an NSC-based graph-guided sampling consensus (NSC-GSAC) algorithm, which performs the sampling process on a consistency graph based on the NSC metric. This graph provides a dense representation of connections among inlier correspondences, narrowing the search space and facilitating cleaner consensus subsets sampling, thus improving the registration robustness and efficiency. Additionally, to further improve efficiency, we introduce a pruning redundancy hypothesis selection (PRHS) strategy to prioritize highly promising representative hypothetical poses, thus accelerating the registration process. Extensive experiments on real-world indoor and outdoor datasets demonstrate that our method achieves superior registration accuracy and efficiency compared to state-of-the-art methods, including both traditional and learning-based methods. Furthermore, experimental results also indicate that our method exhibits more robust performance in the presence of severe outliers, outperforming all other methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20209-20223"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Linearity and High-Stability AgNPs Thin-Film Temperature Sensor and Microcrack Thin-Film Strain Gauge Based on Laser-Assisted Fabrication","authors":"Luoxin Li;Wei Xiao;Lianjie Lu;Luntao Xia;Chenhe Shao;Yong Huang;Xin Liu;Zhenjin Xu;Qibin Zhuang;Zhengmao Ding;Dezhi Wu","doi":"10.1109/JSEN.2025.3563269","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3563269","url":null,"abstract":"Real-time monitoring of bearing operational status is critical for mechanical equipment safety. However, integrated temperature-strain sensors often encounter crosstalk between signals, which affects the accuracy and stability of measurements. Achieving precise in situ monitoring of bearing temperature and strain conditions remains a significant technical challenge. Here, highly linear and stable AgNPs thin-film temperature sensors (AgNPs TFTSs) and AgNPs microcrack thin-film strain gauges (AgNPs MTFSGs) are proposed using laser-assisted printing technology, which is capable of accurately acquiring condition parameters of bearing. The printed AgNPs TFTS exhibited a low hysteresis error of 2.6% and a high linearity of 0.999. The microcracks in AgNPs MTFSG were induced by ultraviolet (UV) laser, yielding a significant enhancement of the gauge factor (GF) from 1.03 (baseline) to 3.19. This twofold sensitivity improvement was achieved while maintaining excellent linearity (<inline-formula> <tex-math>${R}^{{2}}=0.997$ </tex-math></inline-formula>). In particular, these thin-film sensors exhibited good stability even when applied to bearings. Typically, AgNPs TFTS and AgNPs MTFSG maintained a low-resistance drift rate of 0.012%/h and a repeatability error of 2.1%, respectively, demonstrating broad adaptability. In addition, both AgNPs TFTS and AgNPs MTFSG exhibited excellent dynamic and static response characteristics. This provides a method for the direct in situ fabrication of thin-film sensors with high linearity and high stability on mechanical components, such as gears and bolts.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"18989-19000"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Analytical Model for the Linear Variable Differential Transformer","authors":"Yuan Gao;William Spirnock;Heng Ban","doi":"10.1109/JSEN.2025.3563422","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3563422","url":null,"abstract":"The linear variable differential transformer (LVDT) is widely used for displacement measurement in both industry and research due to its high accuracy and working ability in harsh environments. However, the lack of a precise and detailed analytical model limits the application and development of the LVDT. The traditional LVDT model, the magnetic circuit model, and finite-element method (FEM) models all have limitations when it comes to customizing LVDT designs for specific applications. This article builds an analytical model for the LVDT by solving Maxwell’s equations, which can investigate a broader range of parameters to design LVDTs. The truncated region eigenfunction expansion (TREE) method, based on the magnetic vector potential, is used to solve the finite-length magnetic core problem. The model is validated by experiments, showing that the maximum discrepancy is smaller than 1.5%. FEM is used to validate the model in a broader range of parameters, and the maximum discrepancy between the model results and FEM is below 0.9% at different working frequencies and magnetic core permeabilities. This article presents an example of using the model to investigate LVDT parameters’ influences on the output’s sensitivity and linearity error, quantifying the impact of the magnetic core’s geometric parameters. This model can optimize LVDT design and facilitate the advancement of LVDT-related sensors.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"19522-19531"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abnormal EEG Microstates in Poststroke Patients: A Pilot Study","authors":"Xiaoling Chen;Jiashun Zhai;Yuhao Cai;Tengyu Zhang;Juan Wang;Shengcui Cheng;Yuanyuan Zhang;Ping Xie","doi":"10.1109/JSEN.2025.3559489","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3559489","url":null,"abstract":"Exploring brain function is crucial for unraveling the pathological mechanism underlying stroke. While most studies focus on brain function and emphasize dynamic connections and interactions within or between brain regions, they often ignore the global properties of large-scale network topology. In this study, we analyzed resting-state electroencephalography (EEG) microstates in stroke patients, calculating key parameters such as mean duration (MD), occurrence (OC), time coverage (TC), and transition probability (TP) across different microstate classes. As a result, we identified four microstate classes (A-D) in both healthy subjects and stroke patients. Notably, stroke patients showed significant changes in Classes B-D, with increased MD, OC, and TC in Classes B and C and decreased MD, OC, and TC in Class D. In addition, stroke patients displayed higher TP between Classes B and C than healthy controls. Moreover, we observed a positive correlation between the OC of Class D and clinical Fugl-Meyer scores, suggesting a link between microstate dynamic and motor recovery. This study highlights the connection between specific microstates and brain regions, providing evidence that stroke patients demonstrate increased activity in visual and salience networks (SNs), but decreased activity in the dorsal attention network (DAN). We speculate these results from changes in the occipital lobe, frontal lobe, parietal lobe, and dorsal anterior cingulate cortex. These findings deepen our understanding of stroke pathophysiology and support further research.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"19863-19873"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LSReg-Net: An End-to-End Registration Network for Large-Scale LiDAR Point Cloud in Autonomous Driving","authors":"Yucheng Tao;Xiuqing Yang;Hanqi Wang;Jian Wang;Zhiyuan Li;Huawei Liang","doi":"10.1109/JSEN.2025.3562916","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3562916","url":null,"abstract":"Large-scale point cloud registration is a fundamental problem for autonomous driving. To achieve alignment, most existing methods focus on local point cloud features for matching. However, these approaches fail to account for the sparse distribution of features in large-scale driving scenarios, limiting their effectiveness. This article introduces an end-to-end registration network, named LSReg-Net, specifically designed to address this challenge, offering a more reliable solution for large-scale LiDAR point cloud. The LSReg-Net performs registration by following sequential modules. First, this study develops a sampling strategy, distance-weighted farthest point sampling (DW-FPS), which effectively enhances sparse feature robustness. Then, a scale attention fusion (SAF) network is proposed to capture both local and geometric features. By considering both spatial sparsity and geometric context of the keypoints, the LSReg-Net obtains accurate correspondences after matching. Finally, a coarse-to-fine registration module is conducted with multilevel correspondences to acquire a precise rigid transformation. Extensive experiments on two large-scale LiDAR datasets demonstrate that the proposed approach outperforms existing registration methods. In addition, real-world experiments based on an experimental platform further validate the high efficiency.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20675-20686"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}