IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"Fast and Adaptive Near-Field Scanning Measurement Based on Complete Meshing Algorithm and Field Spatial Fluctuation Criterion","authors":"Quan Huang;Yangyang Zhou;Yusong Xie;Zhentao Huang;Wenxiao Fang;Lei Wang;Guoguang Lu","doi":"10.1109/TIM.2026.3694744","DOIUrl":"https://doi.org/10.1109/TIM.2026.3694744","url":null,"abstract":"This article presents a fast and adaptive near-field scanning measurement based on a complete meshing algorithm (CMA) and field spatial fluctuation criterion. The scanning approach shows a remarkable advantage over traditional methods in reducing the total scanning time and the number of scanned points while maintaining the desired constructed accuracy. Before the scanning, the CMA is proposed to completely mesh the scanning plane into a series of disjoint sets, with the grid points within each set uniformly distributed across the scanning plane to ensure diversity during exploration. In the scanning, a field spatial fluctuation (gradient and variance change) criterion in the variable neighborhood is proposed for scanning selection. The proposed scanning measurement approach can balance exploration and exploitation with high efficiency, with all hyperparameters set adaptively. The proposed scanning measurement approach is validated in both simulation and experiment of near-field scanning, and the efficiency and accuracy outperforms conventional approaches in both efficiency and construction accuracy under the same constrain conditions. Experimental results show that, on a scanning plane with <inline-formula> <tex-math>$181times 181$ </tex-math></inline-formula> grid points, the proposed scanning measurement method reduces scanning time by 98% compared to full scanning and by 45% relative to greedy scanning, with a total computation time of only 8.56 s. Moreover, the constructed field achieves a structural similarity index (SSIM) of 0.90 with respect to the full scanning while using merely 0.37% of the total measurement points.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"8004314-8004314"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512029","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":"Frequency-Aware Discrepancy and Polynomial Loss Weighting for Multisource Domain Adaptation in Rolling Bearing Fault Diagnosis","authors":"Bo Wang;Liangjiang Liu;Aimin Shi;Mengdi Zhu","doi":"10.1109/TIM.2026.3674260","DOIUrl":"https://doi.org/10.1109/TIM.2026.3674260","url":null,"abstract":"Multisource fault diagnosis methods for rolling bearings under variable operating conditions suffer from limited generalization due to severe domain shifts and spectral inconsistencies. To address this issue, this article proposes a novel frequency-aware and weighted multisource domain adaptation (MSDA) network (FWMDA-Net), which effectively leverages knowledge from multiple source domains to enhance diagnostic performance in an unlabeled target domain. A novel frequency-aware multiscale maximum mean square discrepancy (FAMS-MMSD) is introduced to capture both class-wise and spectral discrepancies, enabling fine-grained alignment of class-conditional distributions while dynamically adjusting kernel bandwidths based on domain-specific frequency structures. Additionally, a polynomial loss weighting strategy is adopted to progressively adjust the tradeoff between classification and alignment losses during training, thereby improving training stability and model generalization. Extensive experiments conducted on both public datasets and a laboratory bearing test platform demonstrate that FWMDA-Net achieves superior diagnostic accuracy and robustness compared with state-of-the-art single-source and MSDA approaches.","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":"148512411","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":"Rapid 2-D Complex Capacitance Estimation Enables Real-Time Design of Impedance-Based Sensors","authors":"Ruixuan Qi;Egge Rouwhorst;Doekle R. Yntema;Ming Cao","doi":"10.1109/TIM.2026.3661692","DOIUrl":"https://doi.org/10.1109/TIM.2026.3661692","url":null,"abstract":"Capacitance underpins a wide spectrum of impedance-based sensors, yet the usual method of using iterative finite-element modeling of frequency-dependent, fringing-field geometries is prohibitively slow. We present a rapid, fully 2-D framework that computes the complex capacitance of arbitrary, polygonal cross sections by combining conformal-mapping theory with a generalized boundary criterion based on material admittance. The method: 1) partitions multilayer structures into homogeneous subdomains; 2) assigns mixed Dirichlet–Neumann boundaries that capture both permittivity and conductivity; and 3) assembles the global capacitance through simple series-and-parallel rules applied to the conformal moduli. Benchmarking against both finite-element method (FEM) (COMSOL) and boundary-element methods (BEMs) shows excellent agreement (<3% deviation). Crucially, our method is orders of magnitude faster (5 s) than FEM (233 s) and BEM (418 s) for a representative coplanar sensor. Canonical test cases with angled or nested interfaces further confirm that the approach remains accurate when conventional partial-capacitance formulas fail. By decoupling speed from mesh density, our technique enables real-time optimization, inverse design, and uncertainty analysis for next-generation touch, flow-cytometry, and quantum-device sensors.","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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512542","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":"A Fully Integrated Single-BJT-Based Temperature-to-Digital Converter Achieving ±0.23 °C (3σ) Over 2.0–5.5 V for Duty-Cycled Operation","authors":"Wooyoung Kim;Kwonbeen Du;Hyunjoong Lee;Namsoo Kim;Jooyeol Rhee","doi":"10.1109/TIM.2026.3667318","DOIUrl":"https://doi.org/10.1109/TIM.2026.3667318","url":null,"abstract":"This article presents a fully integrated temperature-to-digital converter (TDC) fabricated in a <inline-formula> <tex-math>$0.18~mu $ </tex-math></inline-formula>m CMOS process, employing a single bipolar junction transistor (BJT). The proposed front-end sequentially biases a single BJT at two current levels to generate a <inline-formula> <tex-math>$Delta V_{text {BE}}$ </tex-math></inline-formula>, thereby eliminating the need for matched BJT pairs and improving reproducibility with a one-point calibration. All critical analog and digital blocks—including the sensing front-end, an incremental delta-sigma analog-to-digital converter, power management units (PMUs), clock generators, e-fuse memory, digital processing logic, and an I²C interface—are fully integrated on-chip to enable compact and standalone operation. After one-point calibration at <inline-formula> <tex-math>$30~^{circ }$ </tex-math></inline-formula>C, the TDC achieves an inaccuracy of <inline-formula> <tex-math>$pm 0.23~^{circ }$ </tex-math></inline-formula>C (<inline-formula> <tex-math>$3sigma $ </tex-math></inline-formula>) over a <inline-formula> <tex-math>$- 40~^{circ }$ </tex-math></inline-formula>C to <inline-formula> <tex-math>$125~^{circ }$ </tex-math></inline-formula>C temperature range. At a conversion time of 7.68 ms, it achieves a 6.71 mK rms resolution, corresponding to a system-level resolution figure of merit (FoM) of 75.3 pJ <inline-formula> <tex-math>$cdot $ </tex-math></inline-formula> K². For the sensing core, the resolution FoM is 11.8 pJ <inline-formula> <tex-math>$cdot $ </tex-math></inline-formula> K². A co-integrated dual-BJT TDC exhibits an inaccuracy of <inline-formula> <tex-math>$pm 0.37~^{circ }$ </tex-math></inline-formula>C (<inline-formula> <tex-math>$3sigma $ </tex-math></inline-formula>), validating the effectiveness of the single-BJT approach for comparison. The TDC operates from a 2.0 to 5.5 V supply with low supply sensitivity of 2.54 m°C/V and supports a low standby current of 420 nA at 2.0 V, enabling compact size, high accuracy, and energy efficiency for duty-cycled applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-13"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512500","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":"Image-Adaptive Point Spread Response Extraction and Guided Reconstruction for RFI Suppression in SAR Imagery","authors":"Lunhao Duan;Xingyu Lu;Ke Tan;Zhichuan Wang;Jianchao Yang;Huizhang Yang;Hong Gu","doi":"10.1109/TIM.2026.3704226","DOIUrl":"https://doi.org/10.1109/TIM.2026.3704226","url":null,"abstract":"Synthetic aperture radar (SAR) is highly vulnerable to radio frequency interference (RFI). Most existing methods exhibit unsatisfactory performance in separating and mitigating time-dense interference with excessive pulses. Due to the wideband characteristics of SAR, interference and targets remain distinguishable in the frequency domain, yet frequency-domain notch filtering (FNF) invariably causes target spectral loss. Conventional reconstruction approaches typically impose sparsity constraints in the spatial domain, implicitly assuming an ideal impulse-like point spread response (PSR). In practice, however, the PSR is spatially variant and nonideal, owing to multiple steps in the imaging chain, including the focusing algorithm itself, windowing, and sampling, which prevents its accurate representation by a unified model. To address these variations, we propose an adaptive PSR extraction method based on the rank-1 property of the PSR covariance matrix. By compensating for windowing effects, correcting mismatches caused by discrete sampling via interpolation, and employing block-wise estimation to handle spatial variability, we construct an accurate and spatially adaptive PSR dictionary. Combined with an enhanced image-domain notch filtering framework, this dictionary enables high-fidelity target reconstruction after interference suppression. Experiments on measured data from Gaofen-3 and Sentinel-1 demonstrate that the proposed method effectively suppresses RFI while preserving target characteristics, outperforming several state-of-the-art approaches.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"6510815-6510815"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512737","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":"Pure Inertial Navigation in Challenging Environments With Wheeled and Chassis-Mounted Inertial Sensors","authors":"Dusan Nemec;Gal Versano;Itai Savin;Vojtech Simak;Juraj Kekelak;Itzik Klein","doi":"10.1109/TIM.2026.3671861","DOIUrl":"https://doi.org/10.1109/TIM.2026.3671861","url":null,"abstract":"Autonomous vehicles are widely used in many applications in both indoor and outdoor settings. In practical situations with limited global navigation satellite systems (GNSS) signals or degraded lighting conditions, the navigation solution may rely only on inertial sensors and, as a result, drift in time due to errors in the inertial measurement. In this work, we propose WiCHINS, a wheeled and chassis inertial navigation system by combining wheel-mounted inertial sensors with a chassis-mounted inertial sensor for accurate pure inertial navigation. To that end, we derive a three-stage framework, each with a dedicated extended Kalman filter (EKF). This framework utilizes the benefits of each location (wheel/body) during the estimation process. To evaluate our proposed approach, we employed a dataset with five inertial measurement units (IMUs) with a total recording time of 228.6. We compare our approach with four other inertial baselines and demonstrate an average position error of 11.4 m, which is 2.4% of the average traveled distance, using two wheels and one body IMUs. As a consequence, our proposed method enables robust navigation in challenging environments and helps bridge the pure-inertial performance gap.","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":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11430518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512718","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":"Noninvasive Extraction of Maternal and Fetal Electrocardiograms Using Progressive Periodic Source Peel-Off","authors":"Yunfei Liu;Yao Li;Xuanyu Luo;Haowen Zhao;Jiawen Cui;Yangfan She;Dongfang Li;Lai Jiang;Xu Zhang","doi":"10.1109/TIM.2026.3687320","DOIUrl":"https://doi.org/10.1109/TIM.2026.3687320","url":null,"abstract":"Abdominal electrocardiogram (ECG) provides a safe and noninvasive method for monitoring fetal well-being during pregnancy using surface electrodes. However, it is challenging to extract weak fetal ECG (fECG) signals from abdominal ECG recordings with larger maternal ECG (mECG) signals and external noise. In this study, we introduce a novel progressive periodic source peel-off (PPSP) method for the extraction of periodic ECG sources. The PPSP method is designed in a two-step (common and constrained) fast independent component analysis (FastICA) framework for source separation, where the later constrained step is yielded by a novel periodic-constrained FastICA (PCFICA) module to extract precise R-peaks conforming to periodic ECG spike trains. Then, singular value decomposition (SVD) is used for waveform estimation at each R-peak to reconstruct the ECG source signals. In addition, a peel-off strategy is applied to facilitate the extraction of weak fECG signals against the influence of large mECG sources and other noise. The performance of the proposed method was examined on public datasets, synthetic data, and self-collected clinical data, with an <inline-formula> <tex-math>$F1$ </tex-math></inline-formula>-score of 99.55% for fECG extraction on public datasets, 99.50% on synthetic data with the highest noise level, and a normalized root mean square error (NRMSE) of fetal heart rate (FHR) of 10.20%, superior to other methods. The proposed method shows promise in separating mECG and weak fECG from multichannel abdominal recordings, even under high-noise conditions. This capability may contribute to more reliable monitoring of fetal and maternal cardiac activity toward intelligent healthcare.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"4007613-4007613"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512868","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":"Distribution System State Estimation by Correlated Gaussian Processes","authors":"Maximilian Buechel;Marcel Zimmer;Daniele Carta;Andrea Benigni","doi":"10.1109/TIM.2026.3684671","DOIUrl":"https://doi.org/10.1109/TIM.2026.3684671","url":null,"abstract":"Modern power distribution grids face significant monitoring challenges due to a lack of measurements. This results in limited insight into the state of the distribution system, which may cause issues regarding power system stability. In this study, we present a distribution system state estimation (DSSE) method that is data-driven and based on correlated Gaussian processes (Corr-GPs). The proposed approach is formulated without the use of pseudo-measurements and requires only 25 samples per node for training. Two variants have been developed: an individual model for estimating voltage magnitude and phase angle, and a coupled model that exploits cross-correlations. These variants have been evaluated on multiple time series and monitoring scenarios. On the IEEE 33-bus system with 11 monitoring units, the individual approach achieves an RMSE of <inline-formula> <tex-math>$0.38 [10^{-2}$ </tex-math></inline-formula> p.u.] for voltage magnitude and <inline-formula> <tex-math>$0.11 [10^{-3}$ </tex-math></inline-formula> rad] for phase angle. A slight degradation in performance is observed with a reduction to six measurement units. Compared to an artificial neural network (ANN), the proposed method achieves comparable accuracy while requiring orders of magnitude less training data. Scalability is demonstrated on the IEEE 69-bus system with 23 monitoring units, where estimation errors remain below <inline-formula> <tex-math>$0.46 [10^{-2}$ </tex-math></inline-formula> p.u.] and <inline-formula> <tex-math>$0.75 [10^{-3}$ </tex-math></inline-formula> rad]. These results demonstrate the potential of Corr-GPs as practical tools for state estimation in modern distribution grids.","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":"148512938","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":"Learning-Based Motion-Aware Dead Reckoning Using IMU and Wheel Encoders","authors":"Xiaolei Li;Wangtian Shen;Pengfei Gu;Junye Zou;Xu Lyu;Fanchen Meng;Ziyang Meng","doi":"10.1109/TIM.2026.3662904","DOIUrl":"https://doi.org/10.1109/TIM.2026.3662904","url":null,"abstract":"Dead-reckoning (DR) algorithms using inertial measurement unit (IMU) and wheel encoders can continuously provide positioning information in scenarios where GNSS signals are lost, providing strong autonomy and playing a critical role in vehicle autonomous navigation. However, a limitation of this method is that errors accumulate over time, resulting in decreasing positioning accuracy and even significant drift during long-term use. This article proposes a learning-based DR algorithm that leverages an encoder–decoder neural network integrating a multimodal attention module (MMAM) and a coarse-to-fine fusion strategy. This approach effectively extracts and fuses motion characteristics from IMU and wheel encoders data, thereby accurately estimating the 3-D vehicle’s instant velocity in the world frame and quantifying its uncertainty. The predicted velocity is then used as the pseudo-measurement in an invariant extended Kalman filter (IEKF), where the uncertainty is directly mapped into a dynamical measurement noise covariance matrix that adjusts the filter update. Real-world experiments demonstrate that our method outperforms existing techniques, reducing average positioning error by approximately 76% compared to the traditional INS + ODO baseline and guaranteeing robust long-term localization in dynamic environments.","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":"148512947","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":"Adaptive Collaborative Multi-Robot Visual-Inertial-Ranging Odometry With Key Trajectory Point Screening","authors":"Wenju Su;Shenghai Yuan;Boyang Lou;Jianmin Zhao;Zhongliang Deng","doi":"10.1109/TIM.2026.3690810","DOIUrl":"https://doi.org/10.1109/TIM.2026.3690810","url":null,"abstract":"Multi-robot distributed collaborative position measurement in a unified global frame has become an important research topic. Traditional multi-robot visual-inertial-ranging odometry (VIRO) utilizes inter-robot ranging to effectively correct odometry drift. However, these methods fail to consider inter-robot relative motion position relationships and weights between heterogeneous residuals, resulting in unstable position measurement accuracy along with system operation. To address this, an error-propagation dilution of precision (EPDOP)-based key trajectory point screening method is proposed, enabling efficient correction of odometry drift and ensuring global consistency without increasing the number of residual factors. In addition, a heterogeneous residual weight allocation method based on Helmert variance component estimation (HVCE) is proposed, which adaptively allocates weights to improve system performance and robustness. Extensive simulations and real-world experiments demonstrate that, compared with previous methods, the proposed method reduces the average ATE by 22.21%, 28.09%, and 27.71% in dataset simulations, single-robot experiments, and multi-robot experiments, respectively, without significantly increasing the backend computation time.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"7504511-7504511"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512981","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}