IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"Efficient Dynamic Calibration for Accelerometer Sensitivity Using Multifrequency Sinusoidal Excitation","authors":"Kailong Mou;Deguang Wang;Zhenjie Zhou;Ming Yang;Chengbin Liang;Shaohui Li;Hongsheng Han","doi":"10.1109/TIM.2026.3701164","DOIUrl":"https://doi.org/10.1109/TIM.2026.3701164","url":null,"abstract":"Accelerometers are critical instruments for motion measurement in transportation safety, structural health monitoring, and earthquake early warning, where calibration accuracy directly determines data reliability. However, existing dynamic calibration methods mainly rely on single-frequency excitation, which suffers from low efficiency and limited capability for traceable multipoint calibration. To address these limitations, this study proposes an efficient dynamic calibration method based on multifrequency (MF) sinusoidal excitation, enabling simultaneous amplitude and phase sensitivity calibration over a wide frequency range. The proposed method integrates logarithmic-spaced frequency distribution (LSFD) for optimized frequency selection, enhanced hippopotamus optimization (EHO) for crest factor (CF) reduction, and MF sinusoidal approximation method (MFSAM) for parameter extraction. Experimental results demonstrate that the proposed method improves calibration efficiency by more than an order of magnitude compared with traditional single-frequency methods over the frequency range of 39–3163 Hz while maintaining high accuracy. The maximum relative amplitude deviation is <inline-formula> <tex-math>$0.493{,}%$ </tex-math></inline-formula>, the maximum absolute deviation is 0.0523 mV/g, the maximum relative standard deviation is <inline-formula> <tex-math>$0.157{,}%$ </tex-math></inline-formula>, and the maximum phase standard deviation is 0.00114°. These results confirm that the proposed method provides a high-efficiency and high-accuracy solution for MF accelerometer calibration, with strong potential for vibration metrology and advanced sensor testing applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1005414-1005414"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509574","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}

{"title":"Deep Learning-Enhanced Adaptive Statistical Nonholonomic Constraint Navigation Method for GNSS-Denied Environments","authors":"Yanxi Ou;Jinguang Jiang;Peihui Yan;Ziyan Yu;Jiaji Wu;Dongpeng Xie","doi":"10.1109/TIM.2026.3654712","DOIUrl":"https://doi.org/10.1109/TIM.2026.3654712","url":null,"abstract":"Global navigation satellite system (GNSS) signal outages severely degrade vehicle integrated navigation accuracy, with positioning errors reaching tens of meters in environments, such as urban tunnels. Traditional nonholonomic constraint (NHC) methods face difficulties adapting to complex motion dynamics, while odometry (ODO) systems add cost and complexity. This article proposes deep learning-enhanced adaptive statistical NHC (DL-ASNHC), a DL-ASNHC navigation method. The approach designs a lightweight deep learning model, RCTNet, achieving high-precision forward velocity prediction in the vehicle frame (v-frame) with root-mean-square error (RMSE) of 0.057 m/s. Motion state classification determines lateral velocity constraint parameters, which combine with vertical velocity constraints to form a complete pseudo-3-D velocity observation model. An adaptive covariance mechanism based on multilevel anomaly detection [median absolute deviation (MAD), chi-square test, and relative change rate] and Huber robust estimation dynamically optimizes filter parameters to handle complex motion variations. During 120-s GNSS outage tests, DL-ASNHC achieves a minimum horizontal positioning RMSE of 0.12 m, representing 93.8% improvement over traditional schemes and 74.1% average improvement over other deep learning methods; the 90th percentile of positioning errors is 0.32 m. In tested scenarios, this method enables effective virtual ODO as an alternative to physical sensors, providing a cost-effective solution for GNSS-denied navigation.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-15"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509587","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}

{"title":"High-Sensitivity Nondestructive Imaging Method for Reinforced Concrete Corrosion Based on Magnetic Resonance Enhancement","authors":"Haitao Chen;Zhengyu Xu;Leng Liao;Zhihong Fu","doi":"10.1109/TIM.2026.3706160","DOIUrl":"https://doi.org/10.1109/TIM.2026.3706160","url":null,"abstract":"Conventional eddy current testing (ECT) is often limited by electromagnetic attenuation when evaluating reinforcement corrosion through concrete cover. To improve quantitative morphological characterization, this study proposes an optimized magnetic resonance-enhanced eddy current imaging (MR-EECI) method. Guided by equivalent-circuit modeling, key sensor parameters, including a 1:600 turn ratio and a 100-mm excitation-to-sensing coil spacing, were systematically optimized to achieve a quality factor (<inline-formula> <tex-math>$Q$ </tex-math></inline-formula>-factor) of 21.57. When evaluating reinforcement corrosion through a 50-mm concrete cover in controlled laboratory settings, the system shows statistical repeatability, with a relative standard deviation below 0.6 %. The minimum normalized voltage (<inline-formula> <tex-math>$V_{mathbf {norm,min}}$ </tex-math></inline-formula>) shows a strong correlation with physical corrosion depth, with an <inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula> of 0.99. Furthermore, a gradient-based method estimates corrosion width with relative errors of 3.71 %–8.15 %, improving the spatial sampling resolution to 5 mm and reducing estimation errors compared with prior methods. Finally, the effects of adjacent rebar interference and diameter variation were experimentally analyzed, providing useful technical guidance for future validation under more complex structural conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"9527717-9527717"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509724","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}

{"title":"Enhancing Material Characterization: Multifidelity Deep Neural Network for Stress–Strain Curve Prediction From Small Punch Test Data","authors":"Zhengni Yang;Rui Yang;Weijian Han;Wenyuan Kang;Xiaohan Chen;Ruihan Dong;Jingyi Zhang;Chao Tong;Jingyu Kong","doi":"10.1109/TIM.2026.3676176","DOIUrl":"https://doi.org/10.1109/TIM.2026.3676176","url":null,"abstract":"The accurate determination of a material’s true stress–strain relationship is crucial for predicting its performance in engineering applications. However, traditional methods for determining material behavior often depend on extensive experiments. Although these methods are essential, they are inherently complex, time-consuming, and often suffer from limited accuracy and reliability when small sample sizes are involved. To address these challenges, this article introduces a multifidelity deep neural network architecture that utilizes both low-fidelity and high-fidelity datasets to predict true stress–strain curves from small punch test (SPT) data. The low-fidelity data are calculated using an empirical function based on the Swift model, providing an initial understanding of material behavior. In contrast, the finite element method (FEM) provides high-fidelity data, enhancing the model’s ability to capture complex material characteristics. This framework merges low-fidelity and high-fidelity networks to estimate mechanical properties with low-fidelity and high-fidelity data. The effectiveness of the proposed approach has been demonstrated through experimental validation, highlighting its exceptional ability to generalize across material thicknesses and properties beyond the training set. The proposed approach outperformed traditional empirical methods and maintained strong performance even when applied to real-world materials.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-14"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510041","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}

{"title":"Hybrid Mapping Polynomial Neural Network Based on PSO Optimization for Robot Calibration","authors":"Peiyang Wei;Xun Deng;Xiangyi Chen;Zhibin Li;Jianhong Gan;Hongping Shu","doi":"10.1109/TIM.2026.3659661","DOIUrl":"https://doi.org/10.1109/TIM.2026.3659661","url":null,"abstract":"The absolute positioning accuracy of industrial robotic arms is crucial for advancing manufacturing applications such as automated assembly. This study presents a hybrid mapping polynomial neural network based on particle swarm optimization (PSO-HMPNN) for industrial robot calibration. The proposed approach integrates independent mapping neural networks with polynomial residual interaction neural modules to enhance error compensation capabilities while employing the particle swarm optimization (PSO) algorithm to optimize neural network weights and avoid local optima. Experimental validation was performed on two distinct datasets (Datasets ABB and HSR) using ABB IRB120 and HSR JR680 robots, with performance compared against eight state-of-the-art calibration algorithms (M2–M9). Results demonstrate that PSO-HMPNN consistently achieved the lowest error metrics across both datasets. On the primary dataset (Dataset ABB), it reached a mean absolute error (MAE) of 0.36 mm, a root-mean-square error (RMSE) of 0.42 mm, and a maximum error (MAX) of 0.92 mm. This represents improvements of 14.3%, 19.2%, and 25.2% in MAE, RMSE, and MAX, respectively, over the best-performing baseline algorithm (M9). Similar high performance was also achieved on Dataset HSR, confirming the method’s robustness. Furthermore, the method shows excellent scalability, with an estimated computation time of 25.0 s for a larger 500-sample dataset, achieving an optimal balance between accuracy and computational efficiency. The optimized Denavit–Hartenberg (D–H) parameters obtained after calibration meet industrial requirements, confirming the method’s superiority and applicability in robot calibration tasks.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-10"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510249","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}

{"title":"Short-Term Wind Speed Forecasting Based on a Novel Multiresolution Neural Network","authors":"Yilin Zhang;Mumin Zhang;Junjie Tang;Jie Zhang;Ferdinanda Ponci;Antonello Monti","doi":"10.1109/TIM.2026.3664374","DOIUrl":"https://doi.org/10.1109/TIM.2026.3664374","url":null,"abstract":"As the penetration of wind power grows at an unprecedented pace, short-term wind speed forecasting (WSF) has become indispensable. However, in practical power system applications, the temporal resolution of wind speed measurements often exceeds that required for system dispatch. To leverage these high-resolution measurements for generating lower-resolution wind speed predictions compatible with dispatch requirements, a novel multiresolution neural network (MRNN) is first proposed. This method consists of three stages: 1) multiresolution time series are constructed from the original wind speed series; 2) a low-resolution feature extraction module based on an improved scalar long short-term memory (sLSTM) network is employed to capture the trend patterns in the low-resolution data, while a high-resolution feature extraction module (HFEM) based on an improved modern temporal convolutional network (modern TCN) is designed to extract fluctuation features from the high-resolution data; and 3) both high-resolution and low-resolution features are fused through a sparse cross-attention mechanism. In the test case, three datasets are used for effectiveness verification, while the main test results indicate that the proposed model well improves the short-term WSF accuracy.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-11"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510208","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}

{"title":"Automatic Model Extraction of the Match Standard in Symmetric–Reciprocal–Match Calibration","authors":"Ziad Hatab;Michael E. Gadringer;Arash Arsanjani;Wolfgang Bösch","doi":"10.1109/TIM.2026.3664542","DOIUrl":"https://doi.org/10.1109/TIM.2026.3664542","url":null,"abstract":"This article addresses the modeling of parasitics of the match standard in the symmetric–reciprocal–match (SRM) calibration method of vector network analyzers (VNAs). In the original SRM procedure, the match standard is assumed to be defined. Here, we demonstrate that the match can be modeled with an arbitrary frequency-dependent model using a nonlinear global optimization procedure. To highlight the validity of the suggested approach, numerical tests were conducted, demonstrating the ability to recover the match standard parasitic model down to software numerical precision. Additionally, we performed microstrip line measurements on a printed circuit board (PCB) to compare the SRM calibration using the proposed automatic model extraction against an SRM calibration where the match standard is explicitly defined via multiline thru-reflect-line (TRL) data. The results show that the automatic model extraction can achieve accuracy comparable to that of a data-based match definition, validating the approach as an effective substitute when explicit characterization of the match standard is unavailable.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-9"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11396341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GNSS Spoofing Detection and Mitigation Algorithm Based on Robust INCM Reconstruction in Array Receivers","authors":"Zhiyu Hao;Xinzhi Peng;Xiaoyu Zhao;Zukun Lu;Xiaohui Liu","doi":"10.1109/TIM.2026.3704088","DOIUrl":"https://doi.org/10.1109/TIM.2026.3704088","url":null,"abstract":"The reliability of global navigation satellite system (GNSS) is continuously threatened by spoofing attacks that possess characteristics similar to authentic signals. An antenna array, as a prevalent anti-interference technique for GNSS, can significantly enhance the signal-to-interference-plus-noise ratio (SINR). However, in the presence of model mismatch, adaptive beamformers based on antenna arrays suffer from phenomena such as signal self-nulling, incomplete interference suppression, and an increased noise floor, thereby degrading the interference suppression performance. To date, there has been limited public research on the robust detection and mitigation of distributed asynchronous and synchronous spoofing under model mismatch conditions. To address this issue, this article proposes a robust array-receiver method for GNSS spoofing detection and mitigation. First, for both weakly asynchronous and synchronous spoofing signals received by an antenna array, we derive the respective gain mechanisms of coherent integration to facilitate direction of arrival (DOA) estimation, which in turn enables the reconstruction of the interference-plus-noise covariance matrix (INCM). Second, we propose a novel INCM reconstruction algorithm tailored for asynchronous and synchronous spoofing, and thoroughly analyze the impact of the estimated spoofing power on the performance of the adaptive beamformer. Finally, we introduce a spoofing detection algorithm based on the joint monitoring of signal power and DOA, and achieve robust spoofing mitigation by applying the proposed INCM reconstruction algorithm. Experimental results demonstrate that in a complex distributed synchronous spoofing scenario, the proposed algorithm not only achieves accurate spoofing detection but also exhibits exceptional interference suppression capabilities. The output SINR of the algorithm steadily approaches the theoretical optimum and significantly outperforms several state-of-the-art array processing algorithms. Receiver-level evaluation under full-constellation spoofing further validates a 98.5 % reduction in 3-D position drift and per-satellite pseudorange errors within ±10 m, confirming the practical effectiveness of the proposed method.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"8510121-8510121"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510374","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}

{"title":"Determining of Geopotential Difference With Multi-GNSS Undifferenced Time–Frequency Signals","authors":"Wei Xu;Pengfei Zhang","doi":"10.1109/TIM.2026.3664549","DOIUrl":"https://doi.org/10.1109/TIM.2026.3664549","url":null,"abstract":"Within the framework of general relativity, the geopotential difference between two ground stations can be derived from a precise comparison of their atomic clocks’ vibrational frequencies. In this study, two active hydrogen masers (H-Masers) were employed to measure the geopotential difference across a baseline of about 129 km. Experiments were performed on an ultrashort baseline at a single site and on a medium-long baseline between two locations, using undifferenced carrier phase time–frequency signals from multiglobal navigation satellite system (GNSS) constellations. The results show that, when referenced against the EIGEN-6C4 model, the geopotential difference derived from multi-GNSS undifferenced carrier phase time–frequency comparison exhibits a bias of approximately 21.4 m2/s2 and a standard deviation (STD) of about 18.0 m2/s2 after convergence. The findings confirm the practicality and consistency of geopotential estimation based on multi-GNSS undifferenced time–frequency signals. As atomic clock frequency stability and multifrequency GNSS products continue to advance, achieving a centimeter-level unified global vertical height system through GNSS time–frequency signals appears increasingly attainable.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-10"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510322","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}

{"title":"TIF-DE: A GNSS/INS Positioning Framework With Track Irregularity Fingerprint Constraints for Heavy-Haul Railways","authors":"Yuxuan Wang;Dalin Zhang;Xihan Li;Yunjuan Peng;Rui Zhao;Pengfei Gao;Yang Li;Baigen Cai;Hongwei Wang","doi":"10.1109/TIM.2026.3699638","DOIUrl":"https://doi.org/10.1109/TIM.2026.3699638","url":null,"abstract":"Heavy-haul railway operations face significant challenges in high-precision positioning due to environmental, equipment, and communication constraints. In global navigation satellite system (GNSS)-denied environments such as tunnels and canyons, traditional GNSS/INS fusion suffers from a lack of longitudinal observability. This article addresses this bottleneck by proposing a tightly coupled positioning framework: track irregularity fingerprint-aided differential evolution (TIF-DE). The framework comprises three elements. A hierarchical TIF representation converts microscopic track irregularities sensed by onboard inertial measurement units (IMUs) into high-dimensional, spatially unique positioning constraints while balancing feature uniqueness and speed invariance. The TIF-DE optimizer operates in an augmented search space that includes the fingerprint database index, incorporating the TIF matching residual as a term in its multiobjective function to achieve tight sensor–fingerprint integration. An adaptive dual-criterion weighting scheme then adjusts fusion weights according to GNSS quality and real-time TIF matching confidence, maintaining reliability under varying signal conditions. Field tests on approximately 297 km of the Shuohuang heavy-haul railway show that, under the tested operational conditions, TIF-DE maintains an overall along-track (AT) RMSE of 1.242 m. During 65-s GNSS outages, it restricts the maximum error to 5.12 m, reducing the drift rate by 74.5 % and decreasing the error by 65.4 % compared to state-of-the-art factor graph optimization (FGO).","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"8509113-8509113"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510425","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}