IEEE Transactions on Instrumentation and Measurement最新文献_第9页

Tightly Coupled UC-PPP/VIO Integration Through Factor Graph Optimization With Segmented Temporal Parameter Estimation for Urban Vehicle Navigation 基于分段时间参数估计因子图优化的UC-PPP/VIO紧密耦合城市车辆导航

IF 7 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2026-01-01 Epub Date: 2026-02-23 DOI: 10.1109/TIM.2026.3664559

Shujie Zhou;Zihao Wang;Xinye Dai;Jiayu Hu;Weiwei Song;Shengfeng Gu

{"title":"Tightly Coupled UC-PPP/VIO Integration Through Factor Graph Optimization With Segmented Temporal Parameter Estimation for Urban Vehicle Navigation","authors":"Shujie Zhou;Zihao Wang;Xinye Dai;Jiayu Hu;Weiwei Song;Shengfeng Gu","doi":"10.1109/TIM.2026.3664559","DOIUrl":"https://doi.org/10.1109/TIM.2026.3664559","url":null,"abstract":"This article presents a novel, tightly coupled integration framework that combines uncombined precise point positioning (UC-PPP), inertial navigation system (INS), and visual inertial odometry (VIO) for robust vehicle navigation in challenging urban environments. Our approach introduces three key innovations: 1) a unified Earth-centered Earth-fixed (ECEF) reference frame for all sensor observations, eliminating coordinate system inconsistencies and reducing state vector dimensionality; 2) an enhanced UC-PPP algorithm that maximizes observational redundancy through raw multifrequency measurements and employs a hybrid atmospheric compensation approach combining empirical models with dedicated parameter estimation; and 3) a segmented temporal parameter estimation framework that adapts estimation windows to parameter characteristics, significantly improving computational efficiency. Extensive vehicle-mounted experiments demonstrate that our method outperforms state-of-the-art approaches (GICI-LIB, VINS-Fusion, and IC-GVINS) in complex urban environments, achieving 20.4% improvement in 3-D positioning accuracy compared to the IC-GVINS algorithm while reducing computational overhead by 75.9% compared to traditional continuous estimation methods. The proposed solution effectively addresses the challenges of signal degradation, atmospheric delays, and computational efficiency in urban navigation applications, providing continuous, high-precision positioning even in GNSS-challenged 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":"148513004","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}

引用次数: 0

Hybrid Supervised Physics-Informed Neural Network for Robust Power System State Estimation 鲁棒电力系统状态估计的混合监督物理-信息神经网络

IF 7 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2026-01-01 Epub Date: 2026-03-18 DOI: 10.1109/TIM.2026.3693458

Shiwei Xu;Dongjian Huang;Qingsong Fu;Wenxu Yan;Wenyuan Wang

{"title":"Hybrid Supervised Physics-Informed Neural Network for Robust Power System State Estimation","authors":"Shiwei Xu;Dongjian Huang;Qingsong Fu;Wenxu Yan;Wenyuan Wang","doi":"10.1109/TIM.2026.3693458","DOIUrl":"https://doi.org/10.1109/TIM.2026.3693458","url":null,"abstract":"Power system state estimation (PSSE) is fundamental for the secure operation of power grids, relying on measurements to estimate real-time conditions. However, conventional weighted least squares (WLS) is vulnerable to non-Gaussian noise and bad data. Meanwhile, existing data-driven and physics-guided deep learning (PGDL) methods often suffer from poor interpretability, reliance on “soft” constraints, and systematic biases from simplified models. To address these limitations, this article proposes a hybrid supervised physics-informed neural network (HSPINN). The architecture integrates a differentiable power flow model as a “hard” constraint within the decoder to ensure physical consistency. A hybrid supervision mechanism, combining physics-consistency and true-state supervision losses, mitigates model-induced biases, while establishing a theoretical analysis of the framework’s structural robustness against stealthy false data injection attacks (FDIAs), which is further hardened via adversarial training. Furthermore, an uncertainty-based adaptive weighting scheme dynamically balances these competing losses. To evaluate the performance of the proposed framework, a comparison is made between the traditional mean squared error (MSE) loss and robust alternatives, including the Huber and generalized correntropy loss (GCL) functions. Simulation results on the IEEE 14-bus and 118-bus systems, tested under various scenarios with Gaussian and non-Gaussian noise, severe bad data, and stealthy FDIA, show that the proposed HSPINN framework significantly outperforms the traditional WLS method in both accuracy and robustness.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"9004414-9004414"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513024","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}

引用次数: 0

High-Accuracy Pipeline Flow Rate Monitoring via Turbulent Noise Frequency-Band Extraction in Distributed Acoustic Sensing Systems 分布式声传感系统中湍流噪声频带提取的管道流量高精度监测

IF 7 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2026-01-01 Epub Date: 2026-03-14 DOI: 10.1109/TIM.2026.3693390

Baoqiang Yan;Zhichao Zeng;Cunzheng Fan;Keqing Zhang;Kan Liu;Lisong Yan;Zhijun Yan;Qizhen Sun

{"title":"High-Accuracy Pipeline Flow Rate Monitoring via Turbulent Noise Frequency-Band Extraction in Distributed Acoustic Sensing Systems","authors":"Baoqiang Yan;Zhichao Zeng;Cunzheng Fan;Keqing Zhang;Kan Liu;Lisong Yan;Zhijun Yan;Qizhen Sun","doi":"10.1109/TIM.2026.3693390","DOIUrl":"https://doi.org/10.1109/TIM.2026.3693390","url":null,"abstract":"Accurate and distributed flow rate measurement is essential for ensuring pipeline safety, enhancing operational efficiency, and extending service life. Conventional point-based flowmeters are intrusive, cause fluid pressure drops, and have limited scalability for real-time, multi-point sensing. This study presents a non-intrusive flow monitoring framework that integrates distributed acoustic sensing (DAS) with a turbulent noise frequency-band extraction (TNFE) algorithm. TNFE is designed to mitigate the dilution of flow sensitivity caused by flow-insensitive and interference components in conventional full-band processing. The acoustic field generated by gas flow is captured by a backscattering-enhanced optical fiber (BEOF) deployed on the pipeline surface, enabling real-time, distributed, and non-intrusive flow measurements. The proposed TNFE algorithm adopts a coarse-to-fine band selection strategy to identify flow-sensitive spectral regions from full-band DAS measurements and constructs band-wise features (with wavelet packet decomposition (WPD) refinement) for flow-rate regression. Field experiments on an operational natural gas pipeline demonstrate a strong quadratic correlation (R<inline-formula> <tex-math>${}^{2} gt 0.999$ </tex-math></inline-formula>) between flow rate and the extracted vibration feature, with measurement accuracies of 95.66% (straight-line), 98.00% (10 cm helical pitch), and 99.14% (5 cm helical pitch) within a ±15% error margin, and consistently reduced root mean square error (RMSE) compared with the full-band baseline. These results validate the feasibility of distributed, real-time flow measurement using DAS with TNFE and indicate its applicability to pipeline integrity management.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"9521909-9521909"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513289","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}

引用次数: 0

An Intelligent System Mitigating Vertical Fixed Pattern Noise in CMOS Image Sensors 抑制CMOS图像传感器垂直固定模式噪声的智能系统

IF 7 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2026-01-01 Epub Date: 2026-07-02 DOI: 10.1109/TIM.2026.3709511

Wilfred Kisku;Sandeep Kodam;Amandeep Kaur

{"title":"An Intelligent System Mitigating Vertical Fixed Pattern Noise in CMOS Image Sensors","authors":"Wilfred Kisku;Sandeep Kodam;Amandeep Kaur","doi":"10.1109/TIM.2026.3709511","DOIUrl":"https://doi.org/10.1109/TIM.2026.3709511","url":null,"abstract":"With the design of column-parallel readout for high-resolution and high-speed applications, there is an added vertical fixed pattern noise (VFPN) that is observed. To mitigate this VFPN, a novel edge deployable convolutional neural network (CNN) called FPNrNet has been successfully proposed. This work aims to integrate both the sensor system and a computational unit together to validate a real-time system with infused intelligence near the CMOS image sensor (CIS). It compensates for the variations in column response, thereby improving the overall image quality. The work introduces an architectural solution for the designed and fabricated CIS in the AMS 350-nm OPTO process that targets VFPN reduction, leveraging a CNN integrated with an attention mechanism. The developed model size is designed to be <inline-formula> <tex-math>$116times $ </tex-math></inline-formula> smaller than DnCNN, which is a standard deep neural networks (DNNs) denoising model. The VFPN of the raw image from CIS has an estimated standard deviation of 0.43, which is reduced approximately by <inline-formula> <tex-math>$5.5times $ </tex-math></inline-formula> with FPNrNet. The hls4ml platform is used to deploy FPNrNet onto the FPGA fabric with an empirical study of the model efficacy in terms of precision. The efficacy of FPNrNet has been further validated across diverse edge platforms, including Cortex-M microcontrollers and Raspberry Pi devices, using INT-8 quantized models. The results demonstrate a clear tradeoff between model size, inference latency, and reconstruction performance.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"2513509-2513509"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513432","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}

引用次数: 0

Adaptive Nonlinear ESO-Based Global Sliding-Mode Control of EV-PMSM With Unmeasurable Disturbance 具有不可测扰动的EV-PMSM自适应非线性eso全局滑模控制

IF 7 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2026-01-01 Epub Date: 2026-02-09 DOI: 10.1109/TIM.2026.3662868

Zhen Zhang;Yinan Guo;Song Zhu;Peiyao Ji;Feng Jiao;Xianfang Song;Dunwei Gong

{"title":"Adaptive Nonlinear ESO-Based Global Sliding-Mode Control of EV-PMSM With Unmeasurable Disturbance","authors":"Zhen Zhang;Yinan Guo;Song Zhu;Peiyao Ji;Feng Jiao;Xianfang Song;Dunwei Gong","doi":"10.1109/TIM.2026.3662868","DOIUrl":"https://doi.org/10.1109/TIM.2026.3662868","url":null,"abstract":"As core traction components of electric vehicles (EVs), permanent magnet synchronous motors (PMSMs) directly determine EV efficiency, dynamic response, and driving safety. Thus, mitigating the adverse effects of unmeasurable lumped disturbances in PMSMs on control performance is crucial for enhancing overall EV performance. Previous methods suffer from insufficient disturbance rejection capability and low tracking accuracy, hindering EV driving smoothness. To address these issues, an improved global sliding-mode control (SMC) method based on an adaptive nonlinear extended state observer (ANESO) is proposed. First, an ANESO is designed to estimate unmeasurable lumped disturbance, where its gain adaptively adjusted based on both state estimation error and tracking error. Then, an improved global SMC law (IGSMCL) is presented by employing a fused error that comprehensively integrates the tracking error and disturbance estimation error, aiming to ensure the global sliding-mode robustness and further improve tracking performance. Finally, the unmeasurable lumped disturbance estimated by the ANESO is adopted for compensation in the IGSMCL. Experimental results on a PMSM test suit with three kinds of disturbance validate the superiority of the proposed controller. More especially, the ANESO provides better disturbance rejection ability than the traditional extended state observers (ESOs) because its average disturbance estimation error is less than 0.16% of the maximum disturbance amplitude and the accuracy improvement rate is larger than 69.4%. Taking tracking accuracy into account, the proposed method achieves the less steady-state speed tracking error (<0.00032 rad/s). Its average tracking error outperforms four competitors with the accuracy improvement rate more than 95%. This confirms its suitability for high-performance EV-PMSM control.","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":"148513431","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}

引用次数: 0

PARNet: A High-Quality Reconstruction Algorithm for Single-Harmonic Projection Magnetic Particle Imaging PARNet：一种高质量的单谐波投影磁粒子成像重建算法

IF 7 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2026-01-01 Epub Date: 2026-02-09 DOI: 10.1109/TIM.2026.3662872

Keyi Tang;Wei Zhao;Jianghao Zhao;Shufeng Zhou;Hongping Gan;Yang Gao;Shouhua Luo;Guohui Ruan;Shanshan Shan;Chunyi Liu;Ning Gu

{"title":"PARNet: A High-Quality Reconstruction Algorithm for Single-Harmonic Projection Magnetic Particle Imaging","authors":"Keyi Tang;Wei Zhao;Jianghao Zhao;Shufeng Zhou;Hongping Gan;Yang Gao;Shouhua Luo;Guohui Ruan;Shanshan Shan;Chunyi Liu;Ning Gu","doi":"10.1109/TIM.2026.3662872","DOIUrl":"https://doi.org/10.1109/TIM.2026.3662872","url":null,"abstract":"Magnetic particle imaging (MPI) is an emerging quantitative imaging modality that exploits the nonlinear magnetization response of superparamagnetic nanoparticles, offering high sensitivity and real-time visualization for biomedical applications. Field-free line (FFL) scanning improves signal-to-noise ratio (SNR) and acquisition efficiency but introduces severe challenges in image reconstruction. Conventional system matrix (SM)-based reconstruction methods achieve high accuracy but suffer from extensive calibration burdens, while X-space methods enable rapid reconstruction yet remain vulnerable to FFL nonuniformity and information loss, leading to blurring and artifacts. To address these limitations, this work proposes a progressive artifact-removal network (PARNet) for FFL-based MPI reconstruction. The proposed algorithm integrates stage-wise artifact suppression, multidimensional attention modeling, and a noise-aware mechanism to progressively eliminate complex artifacts while preserving fine structural details. In support of this framework, a simulation platform with parameters consistent with our in-house MPI prototype is developed to generate large-scale, diverse datasets for efficient network training, eliminating the need for time-consuming SM calibration and mitigating reconstruction errors caused by the nonuniformity of magnetic fields along the FFL direction and filtered backprojection limitations. Comprehensive evaluations on both simulated and experimental data demonstrate that the proposed method outperforms existing approaches such as Deconv and SwinIR, achieving superior reconstruction quality with reduced blur and artifacts, accurate structural preservation, and efficient recovery from voltage signals to quantitative concentration images.","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":"148513537","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}

引用次数: 0

Development of a Dual-Transmitter–Dual-Receiver Rayleigh Wave EMAT for Stress Measurement 用于应力测量的双发射双接收瑞利波EMAT的研制

IF 7 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2026-01-01 Epub Date: 2026-02-23 DOI: 10.1109/TIM.2026.3664543

Tianran Liu;Tao Su;Yao Zhang;Xingjian Liu;Haibo Liu;Yongqing Wang

{"title":"Development of a Dual-Transmitter–Dual-Receiver Rayleigh Wave EMAT for Stress Measurement","authors":"Tianran Liu;Tao Su;Yao Zhang;Xingjian Liu;Haibo Liu;Yongqing Wang","doi":"10.1109/TIM.2026.3664543","DOIUrl":"https://doi.org/10.1109/TIM.2026.3664543","url":null,"abstract":"A Rayleigh wave electromagnetic acoustic transducer (EMAT) is an ideal sensing method for detecting part stress in the field environment. To solve the problem of difficulty in synergistically improving the spatial measurement resolution and conversion efficiency of Rayleigh wave EMAT, a dual-transmitter–dual-receiver Rayleigh wave EMAT for stress measurement is developed. A permanent magnet array structure with enhanced magnetic flux density (MFD) is proposed. The influence mechanisms of coil structures on the EMAT performance are analyzed. Finally, the excitation and stress characterization ability of the Rayleigh wave EMAT is tested. The results show that compared with a single permanent magnet, the theoretical MFD of the array of permanent magnets is increased by more than two times. Compared with the classical Rayleigh wave EMAT, the signal voltage amplitude is increased by more than ten times, and the signal-to-noise ratio (SNR) is increased from 35.7 to 74.3 dB. The spatial measurement resolution is 7 mm. In addition, Rayleigh wave propagation velocity has a good linear relationship with loading stresses. The effectiveness of the developed Rayleigh wave EMAT is comprehensively validated.","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":"148513509","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}

引用次数: 0

Femtosecond Laser-Fabricated Wavelength Division Multiplexing Sensors for Three-Parameter Robotic Arm Monitoring 三参数机械臂监测用飞秒激光波分复用传感器

IF 7 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2026-01-01 Epub Date: 2026-02-23 DOI: 10.1109/TIM.2026.3665959

Mao-Qing Chen;Zheng Zhou;Ning Li;Qiao-Yun Wang;Yong Zhao

{"title":"Femtosecond Laser-Fabricated Wavelength Division Multiplexing Sensors for Three-Parameter Robotic Arm Monitoring","authors":"Mao-Qing Chen;Zheng Zhou;Ning Li;Qiao-Yun Wang;Yong Zhao","doi":"10.1109/TIM.2026.3665959","DOIUrl":"https://doi.org/10.1109/TIM.2026.3665959","url":null,"abstract":"To address the growing demands for high-precision pose monitoring and health diagnostics in industrial and medical robotic arms, this article proposes an integrated wavelength division multiplexing (WDM) multicore optical fiber detection system. A triple-core fiber serves as the sensing element for simultaneous strain, bending, and torsion monitoring. Efficient optical waveguides (OWGs) and fiber Bragg grating (FBG) arrays are fabricated using femtosecond laser micromachining, creating wavelength-decoupled multiparameter sensing units. Experimental results demonstrate that excellent strain sensitivity is 1.78–1.91 pm/<inline-formula> <tex-math>$mu varepsilon $ </tex-math></inline-formula> and discrimination in strain tests; significant multiparameter decoupling and directional identification capabilities in bending/torsion tests; and bending sensitivity enhanced to ±199.2 pm/m−1 through differential calculation. The measurements are supported by systematic uncertainty analysis, indicating combined standard uncertainties of approximately <inline-formula> <tex-math>$pm 2.5~mu varepsilon $ </tex-math></inline-formula>, ±0.15 m−1, and ±0.8° for strain, bending, and torsion, respectively. This system exhibits extremely high repeatability and stability. Its wavelength drift is less than 0.04 nm, and the intensity fluctuation is less than 0.04 dB. At the same time, it can also achieve distributed multipoint sensing along the robotic arm. Overall, the proposed sensor provides a high-precision and reliable multidimensional sensing solution, showing broad application potential in intelligent manufacturing and minimally invasive medicine.","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":"148513518","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}

引用次数: 0

Robust Online Parameter Estimation of Linear ARX Models Based on Variational Bayesian Inference 基于变分贝叶斯推理的线性ARX模型鲁棒在线参数估计

IF 7 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2026-01-01 Epub Date: 2026-03-04 DOI: 10.1109/TIM.2026.3670563

Xin Chen;Jianxue Fan;Zhichao Pan;Shunyi Zhao;Fei Liu

{"title":"Robust Online Parameter Estimation of Linear ARX Models Based on Variational Bayesian Inference","authors":"Xin Chen;Jianxue Fan;Zhichao Pan;Shunyi Zhao;Fei Liu","doi":"10.1109/TIM.2026.3670563","DOIUrl":"https://doi.org/10.1109/TIM.2026.3670563","url":null,"abstract":"In this article, the online and robust parameter estimation problem of linear systems is investigated within the framework of variational Bayesian (VB) inference. Generally, the input-output dynamic characteristic of a practical linear process can be represented with an autoregressive exogenous (ARX) model, of which the model parameters exhibit random-walk dynamics. To deal with outliers that arise in practice, the contaminated measurements are described with a Student’s t-distribution. Meanwhile, the statistical characteristics of the parameters’ wandering behavior are also assumed to follow a Student’s t-distribution, for robust estimation. Based on the above assumptions, a robust estimation algorithm is formulated, in which the posterior distribution of the parameter vector is estimated in an online manner. In addition, the statistical characteristics of measurement noise and the degree of freedom of the Student’s t-distribution (t-dof) are also addressed. Finally, a numerical example and a simulated rotary servo plant system with a permanent magnet direct current (DC) motor are employed for performance verification of the suggested algorithm.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"1-18"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513558","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}

引用次数: 0

Code Estimation in Noncooperative GNSS Signal Quality Monitoring: Uncertainty Measurement and Correction Solution 非合作GNSS信号质量监测中的码估计：不确定性测量与修正方案

IF 7 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2026-01-01 Epub Date: 2026-06-18 DOI: 10.1109/TIM.2026.3705047

Jie Song;Zukun Lu;Zhu Xiao;Wei Xiao;Guangfu Sun

{"title":"Code Estimation in Noncooperative GNSS Signal Quality Monitoring: Uncertainty Measurement and Correction Solution","authors":"Jie Song;Zukun Lu;Zhu Xiao;Wei Xiao;Guangfu Sun","doi":"10.1109/TIM.2026.3705047","DOIUrl":"https://doi.org/10.1109/TIM.2026.3705047","url":null,"abstract":"GNSS pseudorandom noise (PRN) code estimation is a key technology for enabling integrity measurement and service verification of noncooperative GNSS signal quality monitoring (SQM). The uncertainty of code estimation refers to a statistical measurement metric characterizing bit error rate (BER). The employment of nondisclosed signal structure in noncooperative GNSS terminals constrains prior knowledge, limiting the prior knowledge of code estimation uncertainty to relative signal-to-noise ratio (SNR) alone. Current solutions primarily rely on instrumentation of high-gain antennas to improve SNR and reduce BER, while the estimation accuracy remains fundamentally limited by instrumentation costs. This article proposes a conditional entropy-based technique for code uncertainty measurement and estimation, which further reduces the BER. By quantifying the impact of intrachip SNR on polarity decision posterior probabilities, a chip-level conditional entropy model is constructed to transform code uncertainty into measurable prior knowledge for correction guidance. Following a descending conditional entropy strategy, chips within an optimal correction ratio range are reconstructed into local PRN codes. Ultimately, the optimal estimation result is identified through a comparative correlation analysis based on the local signals with both positive and negative polarities. Simulations with global positioning system (GPS) L1 P(Y) signal and practical experiments from real instruments demonstrate that the proposed method achieves maximum reductions in BER of 10.2 % and 8.8 %, respectively.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"75 ","pages":"8510715-8510715"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11570906","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513569","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}

引用次数: 0