Pub Date : 2025-08-18DOI: 10.1109/JSEN.2025.3597294
Dezhi Zheng;Zonglin Li;Jie Yuan;Chun Hu;Zhen Wang;Peng Peng
Eddy current testing (ECT) is a vital technique for pipeline defect detection, where the sensitivity of detection is heavily influenced by probe design parameters. However, traditional optimization methods for probe parameters often suffer from limitations such as neglecting interactions among parameters, ignoring potential optimal combinations within the step size, and being quite time-consuming. To address these challenges, an advanced optimization framework is proposed, which combines a neural network with Bayesian optimization (BO). A probe configuration consisting of two coaxially arranged coils connected via a bridge circuit is investigated. A multipath residual neural network is developed as a surrogate model to evaluate the design parameters, including coil inner diameter, number of turns, height, and spacing. Bayesian optimization then uses this model as the objective function to identify optimal parameter combinations. Simulation and experimental results validate that the surrogate model demonstrates enhanced prediction accuracy, and the optimization process achieves superior performance with fewer iterations. Compared with the comparison groups, the optimized probes exhibit higher sensitivity for defects in the 1–4-mm depth range. These prove the effectiveness of the proposed method for efficient and high-performance ECT probe design, indicating its significant application potential.
{"title":"High-Sensitivity Eddy Current Probe Design via Multipath ResNet and Bayesian Optimization","authors":"Dezhi Zheng;Zonglin Li;Jie Yuan;Chun Hu;Zhen Wang;Peng Peng","doi":"10.1109/JSEN.2025.3597294","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597294","url":null,"abstract":"Eddy current testing (ECT) is a vital technique for pipeline defect detection, where the sensitivity of detection is heavily influenced by probe design parameters. However, traditional optimization methods for probe parameters often suffer from limitations such as neglecting interactions among parameters, ignoring potential optimal combinations within the step size, and being quite time-consuming. To address these challenges, an advanced optimization framework is proposed, which combines a neural network with Bayesian optimization (BO). A probe configuration consisting of two coaxially arranged coils connected via a bridge circuit is investigated. A multipath residual neural network is developed as a surrogate model to evaluate the design parameters, including coil inner diameter, number of turns, height, and spacing. Bayesian optimization then uses this model as the objective function to identify optimal parameter combinations. Simulation and experimental results validate that the surrogate model demonstrates enhanced prediction accuracy, and the optimization process achieves superior performance with fewer iterations. Compared with the comparison groups, the optimized probes exhibit higher sensitivity for defects in the 1–4-mm depth range. These prove the effectiveness of the proposed method for efficient and high-performance ECT probe design, indicating its significant application potential.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34803-34812"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089998","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}
Pub Date : 2025-08-18DOI: 10.1109/JSEN.2025.3597683
Xinjiu Jin;Lijian Yang
The accurate assessment of the ultimate tensile strength (UTS) of pipeline materials is crucial for determining the maximum allowable operating pressure of pipelines and predicting potential locations of structural failure. To evaluate the UTS of in-service pipelines, this study investigated the relationship between the UTS of steel and its magnetic permeability based on dislocation dynamics and density functional theory. An eddy current-based detection method for assessing the UTS of pipelines was proposed. The effectiveness of the proposed method was verified through experiments, and the impact of temperature variations and surface corrosion on the detection outcomes was also investigated. The experimental results demonstrate that when the detection frequency is set within the range of 5–50 kHz, the eddy current testing results of Q235 and Q345 steels exhibit an approximately linear distribution on the impedance plane, corresponding to the ascending order of their UTS. The optimal detection frequency for both steel types is identified to be between 10 and 50 kHz. Within this frequency range, both the amplitude and the phase angle of the eddy current impedance display an approximately linear correlation with the UTS of the materials. Under linear regression analysis, the Pearson correlation coefficient between impedance amplitude and UTS exceeds 0.75, while that between phase angle and UTS remains above 0.7. This method exhibits less susceptibility to temperature variations and surface corrosion on steel, making it suitable for complex working conditions, including internal inspection of pipelines.
{"title":"Research on Eddy Current-Based Detection Method for Ultimate Tensile Strength of Pipelines","authors":"Xinjiu Jin;Lijian Yang","doi":"10.1109/JSEN.2025.3597683","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597683","url":null,"abstract":"The accurate assessment of the ultimate tensile strength (UTS) of pipeline materials is crucial for determining the maximum allowable operating pressure of pipelines and predicting potential locations of structural failure. To evaluate the UTS of in-service pipelines, this study investigated the relationship between the UTS of steel and its magnetic permeability based on dislocation dynamics and density functional theory. An eddy current-based detection method for assessing the UTS of pipelines was proposed. The effectiveness of the proposed method was verified through experiments, and the impact of temperature variations and surface corrosion on the detection outcomes was also investigated. The experimental results demonstrate that when the detection frequency is set within the range of 5–50 kHz, the eddy current testing results of Q235 and Q345 steels exhibit an approximately linear distribution on the impedance plane, corresponding to the ascending order of their UTS. The optimal detection frequency for both steel types is identified to be between 10 and 50 kHz. Within this frequency range, both the amplitude and the phase angle of the eddy current impedance display an approximately linear correlation with the UTS of the materials. Under linear regression analysis, the Pearson correlation coefficient between impedance amplitude and UTS exceeds 0.75, while that between phase angle and UTS remains above 0.7. This method exhibits less susceptibility to temperature variations and surface corrosion on steel, making it suitable for complex working conditions, including internal inspection of pipelines.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35201-35211"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078632","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}
Pub Date : 2025-08-18DOI: 10.1109/JSEN.2025.3597861
Sumaiya Afroz Mila;Sandip Ray
The use of Internet of Things (IoT) technology in the healthcare system has significantly improved the efficiency and effectiveness of patient care, marking a paradigm shift in modern healthcare practices. Continuous monitoring of physiological parameters through wearable devices has the potential to contribute to the early detection of various chronic and infectious diseases. In this survey, we dig into a variety of wearable devices, exploring the sensors they employ and the specific physiological parameters they monitor. Additionally, we demonstrate the wireless communication facilitated by these devices, connecting sensors and external servers or cloud platforms. Ultimately, we showcase the diverse array of applications for these wearable devices in the realms of disease diagnosis and prevention, achieved through the continuous monitoring of physiological data.
{"title":"IoT for Continuous Physiological Parameters Monitoring in Healthcare: A Review","authors":"Sumaiya Afroz Mila;Sandip Ray","doi":"10.1109/JSEN.2025.3597861","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597861","url":null,"abstract":"The use of Internet of Things (IoT) technology in the healthcare system has significantly improved the efficiency and effectiveness of patient care, marking a paradigm shift in modern healthcare practices. Continuous monitoring of physiological parameters through wearable devices has the potential to contribute to the early detection of various chronic and infectious diseases. In this survey, we dig into a variety of wearable devices, exploring the sensors they employ and the specific physiological parameters they monitor. Additionally, we demonstrate the wireless communication facilitated by these devices, connecting sensors and external servers or cloud platforms. Ultimately, we showcase the diverse array of applications for these wearable devices in the realms of disease diagnosis and prevention, achieved through the continuous monitoring of physiological data.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34311-34325"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073158","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}
Pub Date : 2025-08-18DOI: 10.1109/JSEN.2025.3598238
Manpreet Kaur;Sheela Ramanna;Yuejian Chen;Qian Liu
Accurately predicting fatigue damage at the grain scale in polycrystalline materials is challenging, primarily due to the complex microstructural topology, anisotropic deformation, and severe class imbalance caused by the rarity of slip-band-marked damage events relative to the vast population of intact grains. Conventional machine learning (ML) methods and single-view graph neural networks (GNNs) often lack the capacity to model such heterogeneity across scales. To bridge this gap, we introduce PolyGraphCL, a novel multiview graph contrastive learning (CL) framework integrating heterogeneous inductive biases from three backbones—graph convolutional network (GCN) for localized neighborhood aggregation, graph attention network (GAT) for globally attentive interactions, and graph sample and aggregate (GraphSAGE) for multiscale sampling. These diverse structural views, derived from applying different GNN architectures to the same input graph, are fused through a learnable attention mechanism, enabling dynamic weighting of view-specific representations per node to capture both fine-grained and holistic structural characteristics. To further address extreme label imbalance, we incorporate cross-view CL that aligns intranode representations across views while repelling internode embeddings, facilitating the formation of class-discriminative manifolds. Evaluated on a ferritic steel microstructure dataset comprising 7633 grains (311 damaged) with 100 descriptors per node, PolyGraphCL achieves an average ${F}1$ score of $0.8816~pm ~0.0505$ and balanced accuracy (BA) of $0.7788~pm ~0.1606$ under stratified fivefold cross-validation-surpassing both conventional ML baselines and single-view GNNs. Furthermore, GNNExplainer-based attribution reveals that PolyGraphCL’s predictions are predominantly governed by local stress concentration, with moderate influence from topological substructures, offering interpretable insights grounded in underlying physical mechanisms. Altogether, PolyGraphCL offers a robust, interpretable, and domain-adaptive framework for advancing data-driven fatigue prediction in computational materials science (MS).
{"title":"PolyGraphCL: A Multiview Graph Contrastive Learning Framework for Grain-Level Fatigue Damage Prediction in Polycrystalline Materials","authors":"Manpreet Kaur;Sheela Ramanna;Yuejian Chen;Qian Liu","doi":"10.1109/JSEN.2025.3598238","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3598238","url":null,"abstract":"Accurately predicting fatigue damage at the grain scale in polycrystalline materials is challenging, primarily due to the complex microstructural topology, anisotropic deformation, and severe class imbalance caused by the rarity of slip-band-marked damage events relative to the vast population of intact grains. Conventional machine learning (ML) methods and single-view graph neural networks (GNNs) often lack the capacity to model such heterogeneity across scales. To bridge this gap, we introduce PolyGraphCL, a novel multiview graph contrastive learning (CL) framework integrating heterogeneous inductive biases from three backbones—graph convolutional network (GCN) for localized neighborhood aggregation, graph attention network (GAT) for globally attentive interactions, and graph sample and aggregate (GraphSAGE) for multiscale sampling. These diverse structural views, derived from applying different GNN architectures to the same input graph, are fused through a learnable attention mechanism, enabling dynamic weighting of view-specific representations per node to capture both fine-grained and holistic structural characteristics. To further address extreme label imbalance, we incorporate cross-view CL that aligns intranode representations across views while repelling internode embeddings, facilitating the formation of class-discriminative manifolds. Evaluated on a ferritic steel microstructure dataset comprising 7633 grains (311 damaged) with 100 descriptors per node, PolyGraphCL achieves an average <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula> score of <inline-formula> <tex-math>$0.8816~pm ~0.0505$ </tex-math></inline-formula> and balanced accuracy (BA) of <inline-formula> <tex-math>$0.7788~pm ~0.1606$ </tex-math></inline-formula> under stratified fivefold cross-validation-surpassing both conventional ML baselines and single-view GNNs. Furthermore, GNNExplainer-based attribution reveals that PolyGraphCL’s predictions are predominantly governed by local stress concentration, with moderate influence from topological substructures, offering interpretable insights grounded in underlying physical mechanisms. Altogether, PolyGraphCL offers a robust, interpretable, and domain-adaptive framework for advancing data-driven fatigue prediction in computational materials science (MS).","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35212-35222"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078692","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}
Pub Date : 2025-08-15DOI: 10.1109/JSEN.2025.3597329
Christian Tamantini;Maria Laura Cristofanelli;Francesca Fracasso;Alessandro Umbrico;Gabriella Cortellessa;Andrea Orlandini;Francesca Cordella
Workload estimation is essential for artificial systems designed to assist users across various domains. These systems can provide personalized support by continuously assessing the user’s state and optimizing intervention strategies. Physiological data acquisition through advanced sensors enables objective and real-time workload estimation, offering a more reliable alternative to self-reported measures. Despite the growing interest in workload estimation, existing literature reviews are often domain-specific or focus on cognitive workload only, without providing a comprehensive analysis of methodologies for estimating both physical and cognitive workload across different applications. To address this gap, this systematic review analyzes 35 studies on multimodal physiological monitoring, examining feature extraction methodologies and supervised learning models used for workload estimation. The review identifies key challenges, including the need for standardized protocols, improved generalization across real-world scenarios, and the integration of adaptive artificial intelligence models. It underscores the role of sensor-based workload estimation in healthcare, rehabilitation, and assistive technologies, positioning it as a fundamental component for developing intelligent, user-centered, and adaptive human–machine interaction systems.
{"title":"Physiological Sensor Technologies in Workload Estimation: A Review","authors":"Christian Tamantini;Maria Laura Cristofanelli;Francesca Fracasso;Alessandro Umbrico;Gabriella Cortellessa;Andrea Orlandini;Francesca Cordella","doi":"10.1109/JSEN.2025.3597329","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597329","url":null,"abstract":"Workload estimation is essential for artificial systems designed to assist users across various domains. These systems can provide personalized support by continuously assessing the user’s state and optimizing intervention strategies. Physiological data acquisition through advanced sensors enables objective and real-time workload estimation, offering a more reliable alternative to self-reported measures. Despite the growing interest in workload estimation, existing literature reviews are often domain-specific or focus on cognitive workload only, without providing a comprehensive analysis of methodologies for estimating both physical and cognitive workload across different applications. To address this gap, this systematic review analyzes 35 studies on multimodal physiological monitoring, examining feature extraction methodologies and supervised learning models used for workload estimation. The review identifies key challenges, including the need for standardized protocols, improved generalization across real-world scenarios, and the integration of adaptive artificial intelligence models. It underscores the role of sensor-based workload estimation in healthcare, rehabilitation, and assistive technologies, positioning it as a fundamental component for developing intelligent, user-centered, and adaptive human–machine interaction systems.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34298-34310"},"PeriodicalIF":4.3,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11126940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The surface-bonded fiber Bragg grating (FBG) sensors are extensively utilized in structural health monitoring. During the strain transfer process from the substrate being measured to the FBG sensor, shear deformation occurs within the adhesive layer. Consequently, the strain detected by the FBG sensor differs from that of the substrate, resulting in strain transfer loss. To solve this problem, a relatively simple strain transfer model for the FBG sensor with surface-bonded in the nongrating region was developed. The impact of various parameters on strain transfer efficiency was examined, and the influence laws of parameters, such as the adhesive layer’s elastic modulus, thickness, and length on transfer efficiency, were elucidated. The theoretical model was validated through finite element simulation. This model offers a theoretical foundation for the design optimization and precise calibration of FBG sensors, as well as for strain monitoring in applications, such as bridges and aerospace.
{"title":"Analysis and Simulation Verification of the Strain Transfer Model for the FBG Sensor With Surface-Bonded in the Nongrating Region","authors":"Xianhuan Luo;Baowu Zhang;Jianjun Cui;Kai Chen;Yihao Zhang;Lu Peng;Liang Pang;Bo Tang;Pinhong Yang;Depei Zeng","doi":"10.1109/JSEN.2025.3597422","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3597422","url":null,"abstract":"The surface-bonded fiber Bragg grating (FBG) sensors are extensively utilized in structural health monitoring. During the strain transfer process from the substrate being measured to the FBG sensor, shear deformation occurs within the adhesive layer. Consequently, the strain detected by the FBG sensor differs from that of the substrate, resulting in strain transfer loss. To solve this problem, a relatively simple strain transfer model for the FBG sensor with surface-bonded in the nongrating region was developed. The impact of various parameters on strain transfer efficiency was examined, and the influence laws of parameters, such as the adhesive layer’s elastic modulus, thickness, and length on transfer efficiency, were elucidated. The theoretical model was validated through finite element simulation. This model offers a theoretical foundation for the design optimization and precise calibration of FBG sensors, as well as for strain monitoring in applications, such as bridges and aerospace.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34813-34818"},"PeriodicalIF":4.3,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089949","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}
Pub Date : 2025-08-14DOI: 10.1109/JSEN.2025.3596715
Peng Song;Yuening Wang;Yan Gao;Bo Gong;Xin Ni;Zhaoying Zuo;Tao Wu;Xixi Zhu;Qingyun Liu
This study demonstrates the synthesis of a Cu@Sch-HNT nanocomposite via an oil-bath-assisted approach, exhibiting enhanced peroxidase-mimetic activity. Comprehensive characterization employing electron paramagnetic resonance (EPR) spectroscopy and radical scavenging assays established ${}^{bullet }$ ${mathrm {O}}_{{2}}^{-}$ radicals as the predominant reactive species governing the catalytic mechanism. Optimal enzymatic activity was observed at physiological temperature, indicative of favorable biocompatibility. Capitalizing on these catalytic properties, a rapid colorimetric sensing platform was engineered for kanamycin detection. Quantitative analysis revealed a significant linear correlation between kanamycin concentration and absorbance at 652 nm, with detection limit determination conducted according to standard signal-to-noise ratio criteria. This methodology affords three principal advantages as follows: 1) visual analyte recognition through distinct chromogenic transitions; 2) high sensitivity confirmed by systematic detection limit assessment; and 3) practical utility validated through recovery analyses in complex matrices. The platform demonstrates significant potential for environmental surveillance and biosensing applications, particularly in resource-constrained environments.
{"title":"Colorimetric Sensor for Kanamycin Based on Peroxidase-Like Activity of Cu@Sch-HNT","authors":"Peng Song;Yuening Wang;Yan Gao;Bo Gong;Xin Ni;Zhaoying Zuo;Tao Wu;Xixi Zhu;Qingyun Liu","doi":"10.1109/JSEN.2025.3596715","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596715","url":null,"abstract":"This study demonstrates the synthesis of a Cu@Sch-HNT nanocomposite via an oil-bath-assisted approach, exhibiting enhanced peroxidase-mimetic activity. Comprehensive characterization employing electron paramagnetic resonance (EPR) spectroscopy and radical scavenging assays established <inline-formula> <tex-math>${}^{bullet }$ </tex-math></inline-formula><inline-formula> <tex-math>${mathrm {O}}_{{2}}^{-}$ </tex-math></inline-formula> radicals as the predominant reactive species governing the catalytic mechanism. Optimal enzymatic activity was observed at physiological temperature, indicative of favorable biocompatibility. Capitalizing on these catalytic properties, a rapid colorimetric sensing platform was engineered for kanamycin detection. Quantitative analysis revealed a significant linear correlation between kanamycin concentration and absorbance at 652 nm, with detection limit determination conducted according to standard signal-to-noise ratio criteria. This methodology affords three principal advantages as follows: 1) visual analyte recognition through distinct chromogenic transitions; 2) high sensitivity confirmed by systematic detection limit assessment; and 3) practical utility validated through recovery analyses in complex matrices. The platform demonstrates significant potential for environmental surveillance and biosensing applications, particularly in resource-constrained environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34363-34369"},"PeriodicalIF":4.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073216","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}
Accurate relative positioning is essential for the deployment of an intelligent transportation system. However, in complex environments such as urban canyons and tunnels, the global positioning system (GPS) signals are often blocked or interrupted, resulting in decreased or invalid positioning accuracy. To meet the demand for accurate vehicle positioning in complex environments of urban roads, this article proposes a deep learning model for GPS pseudo-range and Doppler shift prediction based on the fusion of the animated oat optimization (AOO), a convolutional neural network (CNN), a bidirectional gated recurrent unit (BiGRU), and an attention mechanism. CNN is applied to capture spatiotemporal features from the input sequence, while BiGRU explores the long-term dependencies in the data. The attention assigns varying weights according to the importance of input data, enabling the model to focus more effectively on critical parts. To improve predictive accuracy, the AOO algorithm is employed for hyperparameter optimization. Then, the predicted GPS pseudo-range and Doppler shift are used for GPS/ultrawide band (UWB) tightly coupled cooperative positioning by utilizing the characteristics of UWB technology that can provide high-precision ranging information. The results of the experiment show that the proposed fusion model improves the relative positioning accuracy by 13%, 29%, 33%, and 50% over CNN-BiGRU-Attention, CNN-BiGRU, BiGRU, and GRU models, respectively, during a GPS signal loss-of-lock environment, which significantly enhances the stability of vehicle positioning in complex environments.
{"title":"GPS/UWB Tightly Coupled Vehicle Cooperative Positioning Based on AOO-CNN- BiGRU-Attention Model","authors":"Wei Sun;Xinyu Qin;Wei Ding;Jingang Zhao;Chen Liang","doi":"10.1109/JSEN.2025.3596781","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596781","url":null,"abstract":"Accurate relative positioning is essential for the deployment of an intelligent transportation system. However, in complex environments such as urban canyons and tunnels, the global positioning system (GPS) signals are often blocked or interrupted, resulting in decreased or invalid positioning accuracy. To meet the demand for accurate vehicle positioning in complex environments of urban roads, this article proposes a deep learning model for GPS pseudo-range and Doppler shift prediction based on the fusion of the animated oat optimization (AOO), a convolutional neural network (CNN), a bidirectional gated recurrent unit (BiGRU), and an attention mechanism. CNN is applied to capture spatiotemporal features from the input sequence, while BiGRU explores the long-term dependencies in the data. The attention assigns varying weights according to the importance of input data, enabling the model to focus more effectively on critical parts. To improve predictive accuracy, the AOO algorithm is employed for hyperparameter optimization. Then, the predicted GPS pseudo-range and Doppler shift are used for GPS/ultrawide band (UWB) tightly coupled cooperative positioning by utilizing the characteristics of UWB technology that can provide high-precision ranging information. The results of the experiment show that the proposed fusion model improves the relative positioning accuracy by 13%, 29%, 33%, and 50% over CNN-BiGRU-Attention, CNN-BiGRU, BiGRU, and GRU models, respectively, during a GPS signal loss-of-lock environment, which significantly enhances the stability of vehicle positioning in complex environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35312-35322"},"PeriodicalIF":4.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073166","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}
Pub Date : 2025-08-14DOI: 10.1109/JSEN.2025.3596895
Jianwei Zhao;Zhaofa Zeng;Shuai Zhou
With the increasing speed of magnetic data acquisition by uncrewed platforms, unexploded ordnance (UXO) surveys now face challenges such as susceptibility to environmental noise interference and low data acquisition. This study proposes a multilevel orthogonal basis function (MOBF) detection method to address the challenges of weak magnetic anomaly detection (MAD) in complex noise environments, particularly for UXO surveys. The MOBF method integrates discrete stationary wavelet transform (DSWT) and 2-D orthogonal basis function (2D-OBF) processing through a cascaded decomposition-fusion architecture. By leveraging DSWT’s shift-invariant multiscale decomposition, the method effectively separates colored noise (with a power spectral density (PSD) of 1/${f}^{,alpha }$ ) from target signals, while OBF enhances localized spatial correlations of anomalies. A variance-weighted energy fusion strategy is introduced to aggregate multiresolution features, significantly improving signal-to-noise ratio (SNR). Numerical simulations demonstrate MOBF’s robustness across diverse noise scenarios: at −20 dB SNR under Gaussian noise, the MOBF method has a higher detection probability and lower false alarm rate than traditional methods. In colored noise environments, MOBF maintains reliable detection at −15 dB SNR, whereas 2D-OBF fails. Field tests conducted in coastal areas with uncrewed aerial vehicle (UAV)-borne magnetic surveys validate MOBF’s practicality, successfully identifying ferromagnetic targets (anchors, iron tools) under challenging conditions (strip noise). Despite limitations in distinguishing UXOs from nonhazardous ferromagnetic objects, MOBF exhibits superior noise immunity and spatial resolution compared to existing methods. The proposed method provides a viable solution for real-time UXO detection on mobile platforms, particularly in low SNR scenarios with colored noise interference.
{"title":"Target Detection for Low Signal-to-Noise Ratio Scalar Magnetic Unexploded Ordnance Surveys: A Multilevel Orthogonal Basis Function Approach","authors":"Jianwei Zhao;Zhaofa Zeng;Shuai Zhou","doi":"10.1109/JSEN.2025.3596895","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596895","url":null,"abstract":"With the increasing speed of magnetic data acquisition by uncrewed platforms, unexploded ordnance (UXO) surveys now face challenges such as susceptibility to environmental noise interference and low data acquisition. This study proposes a multilevel orthogonal basis function (MOBF) detection method to address the challenges of weak magnetic anomaly detection (MAD) in complex noise environments, particularly for UXO surveys. The MOBF method integrates discrete stationary wavelet transform (DSWT) and 2-D orthogonal basis function (2D-OBF) processing through a cascaded decomposition-fusion architecture. By leveraging DSWT’s shift-invariant multiscale decomposition, the method effectively separates colored noise (with a power spectral density (PSD) of 1/<inline-formula> <tex-math>${f}^{,alpha }$ </tex-math></inline-formula>) from target signals, while OBF enhances localized spatial correlations of anomalies. A variance-weighted energy fusion strategy is introduced to aggregate multiresolution features, significantly improving signal-to-noise ratio (SNR). Numerical simulations demonstrate MOBF’s robustness across diverse noise scenarios: at −20 dB SNR under Gaussian noise, the MOBF method has a higher detection probability and lower false alarm rate than traditional methods. In colored noise environments, MOBF maintains reliable detection at −15 dB SNR, whereas 2D-OBF fails. Field tests conducted in coastal areas with uncrewed aerial vehicle (UAV)-borne magnetic surveys validate MOBF’s practicality, successfully identifying ferromagnetic targets (anchors, iron tools) under challenging conditions (strip noise). Despite limitations in distinguishing UXOs from nonhazardous ferromagnetic objects, MOBF exhibits superior noise immunity and spatial resolution compared to existing methods. The proposed method provides a viable solution for real-time UXO detection on mobile platforms, particularly in low SNR scenarios with colored noise interference.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35157-35169"},"PeriodicalIF":4.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078565","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}
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